Real-Time Detection of Rupture Development: Earthquake Early Warning Using P Waves From Growing Ruptures

NASA Astrophysics Data System (ADS)

Kodera, Yuki

2018-01-01

Large earthquakes with long rupture durations emit P wave energy throughout the rupture period. Incorporating late-onset P waves into earthquake early warning (EEW) algorithms could contribute to robust predictions of strong ground motion. Here I describe a technique to detect in real time P waves from growing ruptures to improve the timeliness of an EEW algorithm based on seismic wavefield estimation. The proposed P wave detector, which employs a simple polarization analysis, successfully detected P waves from strong motion generation areas of the 2011 Mw 9.0 Tohoku-oki earthquake rupture. An analysis using 23 large (M ≥ 7) events from Japan confirmed that seismic intensity predictions based on the P wave detector significantly increased lead times without appreciably decreasing the prediction accuracy. P waves from growing ruptures, being one of the fastest carriers of information on ongoing rupture development, have the potential to improve the performance of EEW systems.

A new real-time tsunami detection algorithm

NASA Astrophysics Data System (ADS)

Chierici, F.; Embriaco, D.; Pignagnoli, L.

2016-12-01

Real-time tsunami detection algorithms play a key role in any Tsunami Early Warning System. We have developed a new algorithm for tsunami detection based on the real-time tide removal and real-time band-pass filtering of sea-bed pressure recordings. The algorithm greatly increases the tsunami detection probability, shortens the detection delay and enhances detection reliability, at low computational cost. The algorithm is designed to be used also in autonomous early warning systems with a set of input parameters and procedures which can be reconfigured in real time. We have also developed a methodology based on Monte Carlo simulations to test the tsunami detection algorithms. The algorithm performance is estimated by defining and evaluating statistical parameters, namely the detection probability, the detection delay, which are functions of the tsunami amplitude and wavelength, and the occurring rate of false alarms. Pressure data sets acquired by Bottom Pressure Recorders in different locations and environmental conditions have been used in order to consider real working scenarios in the test. We also present an application of the algorithm to the tsunami event which occurred at Haida Gwaii on October 28th, 2012 using data recorded by the Bullseye underwater node of Ocean Networks Canada. The algorithm successfully ran for test purpose in year-long missions onboard the GEOSTAR stand-alone multidisciplinary abyssal observatory, deployed in the Gulf of Cadiz during the EC project NEAREST and on NEMO-SN1 cabled observatory deployed in the Western Ionian Sea, operational node of the European research infrastructure EMSO.

Main components and characteristics of landslide early warning systems operational worldwide

NASA Astrophysics Data System (ADS)

Piciullo, Luca; Cepeda, José

2017-04-01

During the last decades the number of victims and economic losses due to natural hazards are dramatically increased worldwide. The reason can be mainly ascribed to climate changes and urbanization in areas exposed at high level of risk. Among the many mitigation measures available for reducing the risk to life related to natural hazards, early warning systems certainly constitute a significant cost-effective option available to the authorities in charge of risk management and governance. The aim is to help and protect populations exposed to natural hazards, reducing fatalities when major events occur. Landslide is one of the natural hazards addressed by early warning systems. Landslide early warning systems (LEWSs) are mainly composed by the following four components: set-up, correlation laws, decisional algorithm and warning management. Within this framework, the set-up includes all the preliminary actions and choices necessary for designing a LEWS, such as: the area covered by the system, the types of landslides and the monitoring instruments. The monitoring phase provides a series of important information on different variables, considered as triggering factors for landslides, in order to define correlation laws and thresholds. Then, a decisional algorithm is necessary for defining the: number of warning levels to be employed in the system, decision making procedures, and everything else system managers may need for issuing warnings in different warning zones. Finally the warning management is composed by: monitoring and warning strategy; communication strategy; emergency plan and, everything connected to the social sphere. Among LEWSs operational worldwide, two categories can be defined as a function of the scale of analysis: "local" and "territorial" systems. The scale of analysis influences several actions and aspects connected to the design and employment of the system, such as: the actors involved, the monitoring systems, type of landslide phenomena addressed and variables to be considered for correlations. The characteristics of LEWSs at local scale are strongly affected by numerous constraints and factors, from time to time different, related to the characteristics of the problem they address. Monitoring measures, variables and correlation laws considered for the design and employment of local LEWSs, strongly depends on the type of landslide to be addressed. On the other hand, territorial LEWSs mainly deals with rainfall-induced landslides characterized by fast slope movement. These systems have become a risk management approach, employed worldwide over areas of relevant extension. Before 2005 only few experiences of LEWSs at a regional scale were carried out, such as in: Hong Kong, China; Zhejiang Province, China; San Francisco Bay, California, USA; Appalachians, USA; Oregon, USA; Rio de Janeiro, Brazil. Since the beginning of the XXI century, increased knowledge on rainfall-landslide correlations and upgraded technologies in weather forecast have promoted the development and improvement of territorial LEWSs around the world.

Performances of the New Real Time Tsunami Detection Algorithm applied to tide gauges data

NASA Astrophysics Data System (ADS)

Chierici, F.; Embriaco, D.; Morucci, S.

2017-12-01

Real-time tsunami detection algorithms play a key role in any Tsunami Early Warning System. We have developed a new algorithm for tsunami detection (TDA) based on the real-time tide removal and real-time band-pass filtering of seabed pressure time series acquired by Bottom Pressure Recorders. The TDA algorithm greatly increases the tsunami detection probability, shortens the detection delay and enhances detection reliability with respect to the most widely used tsunami detection algorithm, while containing the computational cost. The algorithm is designed to be used also in autonomous early warning systems with a set of input parameters and procedures which can be reconfigured in real time. We have also developed a methodology based on Monte Carlo simulations to test the tsunami detection algorithms. The algorithm performance is estimated by defining and evaluating statistical parameters, namely the detection probability, the detection delay, which are functions of the tsunami amplitude and wavelength, and the occurring rate of false alarms. In this work we present the performance of the TDA algorithm applied to tide gauge data. We have adapted the new tsunami detection algorithm and the Monte Carlo test methodology to tide gauges. Sea level data acquired by coastal tide gauges in different locations and environmental conditions have been used in order to consider real working scenarios in the test. We also present an application of the algorithm to the tsunami event generated by Tohoku earthquake on March 11th 2011, using data recorded by several tide gauges scattered all over the Pacific area.

Early warning system for financially distressed hospitals via data mining application.

PubMed

Koyuncugil, Ali Serhan; Ozgulbas, Nermin

2012-08-01

The aim of this study is to develop a Financial Early Warning System (FEWS) for hospitals by using data mining. A data mining method, Chi-Square Automatic Interaction Detector (CHAID) decision tree algorithm, was used in the study for financial profiling and developing FEWS. The study was conducted in Turkish Ministry of Health's public hospitals which were in financial distress and in need of urgent solutions for financial issues. 839 hospitals were covered and financial data of the year 2008 was obtained from Ministry of Health. As a result of the study, it was determined that 28 hospitals (3.34%) had good financial performance, and 811 hospitals (96.66%) had poor financial performance. According to FEWS, the covered hospitals were categorized into 11 different financial risk profiles, and it was found that 6 variables affected financial risk of hospitals. According to the profiles of hospitals in financial distress, one early warning signal was detected and financial road map was developed for risk mitigation.

A triangular climate-based decision model to forecast crop anomalies in Kenya

NASA Astrophysics Data System (ADS)

Guimarães Nobre, G.; Davenport, F.; Veldkamp, T.; Jongman, B.; Funk, C. C.; Husak, G. J.; Ward, P.; Aerts, J.

2017-12-01

By the end of 2017, the world is expected to experience unprecedented demands for food assistance where, across 45 countries, some 81 million people will face a food security crisis. Prolonged droughts in Eastern Africa are playing a major role in these crises. To mitigate famine risk and save lives, government bodies and international donor organisations are increasingly building up efforts to resolve conflicts and secure humanitarian relief. Disaster-relief and financing organizations traditionally focus on emergency response, providing aid after an extreme drought event, instead of taking actions in advance based on early warning. One of the reasons for this approach is that the seasonal risk information provided by early warning systems is often considered highly uncertain. Overcoming the reluctance to act based on early warnings greatly relies on understanding the risk of acting in vain, and assessing the cost-effectiveness of early actions. This research develops a triangular climate-based decision model for multiple seasonal time-scales to forecast strong anomalies in crop yield shortages in Kenya using Casual Discovery Algorithms and Fast and Frugal Decision Trees. This Triangular decision model (1) estimates the causality and strength of the relationship between crop yields and hydro climatological predictors (extracted from the Famine Early Warning Systems Network's data archive) during the crop growing season; (2) provides probabilistic forecasts of crop yield shortages in multiple time scales before the harvesting season; and (3) evaluates the cost-effectiveness of different financial mechanisms to respond to early warning indicators of crop yield shortages obtained from the model. Furthermore, we reflect on how such a model complements and advances the current state-of-art FEWS Net system, and examine its potential application to improve the management of agricultural risks in Kenya.

A fundamental conflict of care: Nurses' accounts of balancing patients' sleep with taking vital sign observations at night.

PubMed

Hope, Joanna; Recio-Saucedo, Alejandra; Fogg, Carole; Griffiths, Peter; Smith, Gary B; Westwood, Greta; Schmidt, Paul E

2017-12-21

To explore why adherence to vital sign observations scheduled by an early warning score protocol reduces at night. Regular vital sign observations can reduce avoidable deterioration in hospital. early warning score protocols set the frequency of these observations by the severity of a patient's condition. Vital sign observations are taken less frequently at night, even with an early warning score in place, but no literature has explored why. A qualitative interpretative design informed this study. Seventeen semi-structured interviews with nursing staff working on wards with varying levels of adherence to scheduled vital sign observations. A thematic analysis approach was used. At night, nursing teams found it difficult to balance the competing care goals of supporting sleep with taking vital sign observations. The night-time frequency of these observations was determined by clinical judgement, ward-level expectations of observation timing and the risk of disturbing other patients. Patients with COPD or dementia could be under-monitored, while patients nearing the end of life could be over-monitored. In this study, we found an early warning score algorithm focused on deterioration prevention did not account for long-term management or palliative care trajectories. Nurses were therefore less inclined to wake such patients to take vital sign observations at night. However, the perception of widespread exceptions and lack of evidence regarding optimum frequency risks delegitimising the early warning score approach. This may pose a risk to patient safety, particularly patients with dementia or chronic conditions. Nurses should document exceptions and discuss these with the wider team. Hospitals should monitor why vital sign observations are missed at night, identify which groups are under-monitored and provide guidance on prioritising competing expectations. early warning score protocols should take account of different care trajectories. © 2017 The Authors. Journal of Clinical Nursing Published by John Wiley & Sons Ltd.

Multiple-Threshold Event Detection and Other Enhancements to the Virtual Seismologist (VS) Earthquake Early Warning Algorithm

NASA Astrophysics Data System (ADS)

Fischer, M.; Caprio, M.; Cua, G. B.; Heaton, T. H.; Clinton, J. F.; Wiemer, S.

2009-12-01

The Virtual Seismologist (VS) algorithm is a Bayesian approach to earthquake early warning (EEW) being implemented by the Swiss Seismological Service at ETH Zurich. The application of Bayes’ theorem in earthquake early warning states that the most probable source estimate at any given time is a combination of contributions from a likelihood function that evolves in response to incoming data from the on-going earthquake, and selected prior information, which can include factors such as network topology, the Gutenberg-Richter relationship or previously observed seismicity. The VS algorithm was one of three EEW algorithms involved in the California Integrated Seismic Network (CISN) real-time EEW testing and performance evaluation effort. Its compelling real-time performance in California over the last three years has led to its inclusion in the new USGS-funded effort to develop key components of CISN ShakeAlert, a prototype EEW system that could potentially be implemented in California. A significant portion of VS code development was supported by the SAFER EEW project in Europe. We discuss recent enhancements to the VS EEW algorithm. We developed and continue to test a multiple-threshold event detection scheme, which uses different association / location approaches depending on the peak amplitudes associated with an incoming P pick. With this scheme, an event with sufficiently high initial amplitudes can be declared on the basis of a single station, maximizing warning times for damaging events for which EEW is most relevant. Smaller, non-damaging events, which will have lower initial amplitudes, will require more picks to be declared an event to reduce false alarms. This transforms the VS codes from a regional EEW approach reliant on traditional location estimation (and it requirement of at least 4 picks as implemented by the Binder Earthworm phase associator) to a hybrid on-site/regional approach capable of providing a continuously evolving stream of EEW information starting from the first P-detection. Offline analysis on Swiss and California waveform datasets indicate that the multiple-threshold approach is faster and more reliable for larger events than the earlier version of the VS codes. This multiple-threshold approach is well-suited for implementation on a wide range of devices, from embedded processor systems installed at a seismic stations, to small autonomous networks for local warnings, to large-scale regional networks such as the CISN. In addition, we quantify the influence of systematic use of prior information and Vs30-based corrections for site amplification on VS magnitude estimation performance, and describe how components of the VS algorithm will be integrated into non-EEW standard network processing procedures at CHNet, the national broadband / strong motion network in Switzerland. These enhancements to the VS codes will be transitioned from off-line to real-time testing at CHNet in Europe in the coming months, and will be incorporated into the development of key components of CISN ShakeAlert prototype system in California.

Signature-forecasting and early outbreak detection system

PubMed Central

Naumova, Elena N.; MacNeill, Ian B.

2008-01-01

SUMMARY Daily disease monitoring via a public health surveillance system provides valuable information on population risks. Efficient statistical tools for early detection of rapid changes in the disease incidence are a must for modern surveillance. The need for statistical tools for early detection of outbreaks that are not based on historical information is apparent. A system is discussed for monitoring cases of infections with a view to early detection of outbreaks and to forecasting the extent of detected outbreaks. We propose a set of adaptive algorithms for early outbreak detection that does not rely on extensive historical recording. We also include knowledge of infection disease epidemiology into forecasts. To demonstrate this system we use data from the largest water-borne outbreak of cryptosporidiosis, which occurred in Milwaukee in 1993. Historical data are smoothed using a loess-type smoother. Upon receipt of a new datum, the smoothing is updated and estimates are made of the first two derivatives of the smooth curve, and these are used for near-term forecasting. Recent data and the near-term forecasts are used to compute a color-coded warning index, which quantify the level of concern. The algorithms for computing the warning index have been designed to balance Type I errors (false prediction of an epidemic) and Type II errors (failure to correctly predict an epidemic). If the warning index signals a sufficiently high probability of an epidemic, then a forecast of the possible size of the outbreak is made. This longer term forecast is made by fitting a ‘signature’ curve to the available data. The effectiveness of the forecast depends upon the extent to which the signature curve captures the shape of outbreaks of the infection under consideration. PMID:18716671

Istanbul Earthquake Early Warning and Rapid Response System

NASA Astrophysics Data System (ADS)

Erdik, M. O.; Fahjan, Y.; Ozel, O.; Alcik, H.; Aydin, M.; Gul, M.

2003-12-01

As part of the preparations for the future earthquake in Istanbul a Rapid Response and Early Warning system in the metropolitan area is in operation. For the Early Warning system ten strong motion stations were installed as close as possible to the fault zone. Continuous on-line data from these stations via digital radio modem provide early warning for potentially disastrous earthquakes. Considering the complexity of fault rupture and the short fault distances involved, a simple and robust Early Warning algorithm, based on the exceedance of specified threshold time domain amplitude levels is implemented. The band-pass filtered accelerations and the cumulative absolute velocity (CAV) are compared with specified threshold levels. When any acceleration or CAV (on any channel) in a given station exceeds specific threshold values it is considered a vote. Whenever we have 2 station votes within selectable time interval, after the first vote, the first alarm is declared. In order to specify the appropriate threshold levels a data set of near field strong ground motions records form Turkey and the world has been analyzed. Correlations among these thresholds in terms of the epicenter distance the magnitude of the earthquake have been studied. The encrypted early warning signals will be communicated to the respective end users by UHF systems through a "service provider" company. The users of the early warning signal will be power and gas companies, nuclear research facilities, critical chemical factories, subway system and several high-rise buildings. Depending on the location of the earthquake (initiation of fault rupture) and the recipient facility the alarm time can be as high as about 8s. For the rapid response system one hundred 18 bit-resolution strong motion accelerometers were placed in quasi-free field locations (basement of small buildings) in the populated areas of the city, within an area of approximately 50x30km, to constitute a network that will enable early damage assessment and rapid response information after a damaging earthquake. Early response information is achieved through fast acquisition and analysis of processed data obtained from the network. The stations are routinely interrogated on regular basis by the main data center. After triggered by an earthquake, each station processes the streaming strong motion data to yield the spectral accelerations at specific periods, 12Hz filtered PGA and PGV and will send these parameters in the form of SMS messages at every 20s directly to the main data center through a designated GSM network and through a microwave system. A shake map and damage distribution map (using aggregate building inventories and fragility curves) will be automatically generated using the algorithm developed for this purpose. Loss assessment studies are complemented by a large citywide digital database on the topography, geology, soil conditions, building, infrastructure and lifeline inventory. The shake and damage maps will be conveyed to the governor's and mayor's offices, fire, police and army headquarters within 3 minutes using radio modem and GPRS communication. An additional forty strong motion recorders were placed on important structures in several interconnected clusters to monitor the health of these structures after a damaging earthquake.

The Accelerometric Networks in Istanbul

NASA Astrophysics Data System (ADS)

Zulfikar, Can; Alcik, Hakan; Mert, Aydin; Tahtasizoglu, Bahar; Kafadar, Nafiz; Korkmaz, Ahmet; Ozel, Oguz; Erdik, Mustafa

2010-05-01

In recent years several strong motion networks have been established in Istanbul with a preparation purpose for future probable earthquake. This study addresses the introduction of current seismic networks and presentation of some recent results recorded in these networks. Istanbul Earthquake Early Warning System Istanbul Earthquake Early Warning System has ten strong motion stations which were installed as close as possible to Marmara Sea main fault zone. Continuous on-line data from these stations via digital radio modem provide early warning for potentially disastrous earthquakes. Considering the complexity of fault rupture and the short fault distances involved, a simple and robust Early Warning algorithm, based on the exceedance of specified threshold time domain amplitude levels is implemented. The current algorithm compares the band-pass filtered accelerations and the cumulative absolute velocity (CAV) with specified threshold levels. The bracketed CAV window values that will be put into practice are accepted as to be 0.20, 0.40 and 0.70 m/s for three alarm levels, respectively. Istanbul Earthquake Rapid Response System Istanbul Earthquake Rapid Response System has one hundred 18 bit-resolution strong motion accelerometers which were placed in quasi-free field locations (basement of small buildings) in the populated areas of the city, within an area of approximately 50x30km, to constitute a network that will enable early damage assessment and rapid response information after a damaging earthquake. Early response information is achieved through fast acquisition and analysis of processed data obtained from the network. The stations are routinely interrogated on regular basis by the main data center. After triggered by an earthquake, each station processes the streaming strong motion data to yield the spectral accelerations at specific periods and sends these parameters in the form of SMS messages at every 20s directly to the main data center through a designated GSM network and through a microwave system. A shake map and damage distribution map (using aggregate building inventories and fragility curves) will then be automatically generated using the algorithm developed for this purpose. Loss assessment studies are complemented by a large citywide digital database on the topography, geology, soil conditions, building, infrastructure and lifeline inventory. The shake and damage maps will be conveyed to the governor's and mayor's offices and army headquarters within 3 minutes using radio modem and GPRS communication. Self Organizing Seismic Early Warning Information Network (SOSEWIN) in Atakoy District SOSEWIN sensors were developed by GFZ and Humbold University as part of SAFER project and EDIM project, and with cooperation of KOERI, the sensors were installed in Atakoy district of Istanbul city with Early Warning purpose. The main features of the SOSEWIN system are each sensing unit is comprised of low-cost components, undertakes its own seismological data processing, analysis and archiving, and its self-organizing capability with wireless mesh network communication. Seismic Network in Important Structures Some of the critical structures located in Istanbul city such as Fatih Sultan Mehmet Suspension Bridge which is connecting Asian and European sides of the city, Hagia Sophia Museum and Suleymaniye Mosque which are historical structures with an age of over 1000 years and 450 years respectively, . Kanyon Tower&Mall, Trakya Elektrik (formerly ENRON) and Isbank Tower (ISKULE) are monitorized to observe their seismic behaviors.

Integrated Land- and Underwater-Based Sensors for a Subduction Zone Earthquake Early Warning System

NASA Astrophysics Data System (ADS)

Pirenne, B.; Rosenberger, A.; Rogers, G. C.; Henton, J.; Lu, Y.; Moore, T.

2016-12-01

Ocean Networks Canada (ONC — oceannetworks.ca/ ) operates cabled ocean observatories off the coast of British Columbia (BC) to support research and operational oceanography. Recently, ONC has been funded by the Province of BC to deliver an earthquake early warning (EEW) system that integrates offshore and land-based sensors to deliver alerts of incoming ground shaking from the Cascadia Subduction Zone. ONC's cabled seismic network has the unique advantage of being located offshore on either side of the surface expression of the subduction zone. The proximity of ONC's sensors to the fault can result in faster, more effective warnings, which translates into more lives saved, injuries avoided and more ability for mitigative actions to take place.ONC delivers near real-time data from various instrument types simultaneously, providing distinct advantages to seismic monitoring and earthquake early warning. The EEW system consists of a network of sensors, located on the ocean floor and on land, that detect and analyze the initial p-wave of an earthquake as well as the crustal deformation on land during the earthquake sequence. Once the p-wave is detected and characterized, software systems correlate the data streams of the various sensors and deliver alerts to clients through a Common Alerting Protocol-compliant data package. This presentation will focus on the development of the earthquake early warning capacity at ONC. It will describe the seismic sensors and their distribution, the p-wave detection algorithms selected and the overall architecture of the system. It will further overview the plan to achieve operational readiness at project completion.

Implementing a C++ Version of the Joint Seismic-Geodetic Algorithm for Finite-Fault Detection and Slip Inversion for Earthquake Early Warning

NASA Astrophysics Data System (ADS)

Smith, D. E.; Felizardo, C.; Minson, S. E.; Boese, M.; Langbein, J. O.; Guillemot, C.; Murray, J. R.

2015-12-01

The earthquake early warning (EEW) systems in California and elsewhere can greatly benefit from algorithms that generate estimates of finite-fault parameters. These estimates could significantly improve real-time shaking calculations and yield important information for immediate disaster response. Minson et al. (2015) determined that combining FinDer's seismic-based algorithm (Böse et al., 2012) with BEFORES' geodetic-based algorithm (Minson et al., 2014) yields a more robust and informative joint solution than using either algorithm alone. FinDer examines the distribution of peak ground accelerations from seismic stations and determines the best finite-fault extent and strike from template matching. BEFORES employs a Bayesian framework to search for the best slip inversion over all possible fault geometries in terms of strike and dip. Using FinDer and BEFORES together generates estimates of finite-fault extent, strike, dip, preferred slip, and magnitude. To yield the quickest, most flexible, and open-source version of the joint algorithm, we translated BEFORES and FinDer from Matlab into C++. We are now developing a C++ Application Protocol Interface for these two algorithms to be connected to the seismic and geodetic data flowing from the EEW system. The interface that is being developed will also enable communication between the two algorithms to generate the joint solution of finite-fault parameters. Once this interface is developed and implemented, the next step will be to run test seismic and geodetic data through the system via the Earthworm module, Tank Player. This will allow us to examine algorithm performance on simulated data and past real events.

Assessment of early warning system performance and improvements since it is in operational phase in Romania

NASA Astrophysics Data System (ADS)

Ionescu, Constantin; Marmureanu, Alexandru; Marmureanu, Gheorghe; Ortansa Cioflan, Carmen

2017-04-01

Earthquake represents a major natural disaster for Romanian territory. The main goal following the occurrence of a strong earthquake is to minimize the total number of fatalities. A rapid early warning system (REWS) was developed in Romania in order to provide 25-35 seconds warning time to Bucharest facilities for the earthquakes with M>5.0. The system consists of four components: a network of strong motion sensors installed in the epicentral area, a redundant communication network, an automatic analyzing system located in the Romanian Data Centre and an alert distribution system. The detection algorithm is based on the magnitude computation using strong motion data and rapid evaluation and scaling relation between the maximum P-wave acceleration measured in the epicentral area and the higher ground motion amplitude recorded in Bucharest. In order to reduce the damages caused by earthquakes, the exploitation of the up to date technology is very important. The information is the key point in the disaster management, and the internet is one of the most used instrument, implying also low costs. The Rapid Early Warning System was expanded to cover all countries affected by major earthquakes originating in the Vrancea seismic area and reduce their impact on existing installations of national interest in neighbouring Romania and elsewhere. REWS provides an efficient instrument for prevention and reaction based on the integrated system for seismic detection in South-Eastern Europe. REWS has been operational since 2013 and sends alert the authorities, hazardous facilities in Romania and Bulgaria (NPP, emergency response agencies etc.) and to public via twitter and some smartphone applications developed in the house. Also, NIEP is part of the UNESCO initiative case on developing a platform on earthquake early warning systems (IP-MEP) that aims to promote and strengthen the development of earthquake early warning systems in earthquake-prone regions of the world by sharing scientific knowledge, capacity building and international cooperation.

Reality Check Algorithm for Complex Sources in Early Warning

NASA Astrophysics Data System (ADS)

Karakus, G.; Heaton, T. H.

2013-12-01

In almost all currently operating earthquake early warning (EEW) systems, presently available seismic data are used to predict future shaking. In most cases, location and magnitude are estimated. We are developing an algorithm to test the goodness of that prediction in real time. We monitor envelopes of acceleration, velocity, and displacement; if they deviate significantly from the envelope predicted by Cua's envelope gmpe's then we declare an overfit (perhaps false alarm) or an underfit (possibly a larger event has just occurred). This algorithm is designed to provide a robust measure and to work as quickly as possible in real-time. We monitor the logarithm of the ratio between the envelopes of the ongoing observed event and the envelopes derived from the predicted envelopes of channels of ground motion of the Virtual Seismologist (VS) (Cua, G. and Heaton, T.). Then, we recursively filter this result with a simple running median (de-spiking operator) to minimize the effect of one single high value. Depending on the result of the filtered value we make a decision such as if this value is large enough (e.g., >1), then we would declare, 'that a larger event is in progress', or similarly if this value is small enough (e.g., <-1), then we would declare a false alarm. We design the algorithm to work at a wide range of amplitude scales; that is, it should work for both small and large events.

Geostationary Lightning Mapper for GOES-R

NASA Technical Reports Server (NTRS)

Goodman, Steven; Blakeslee, Richard; Koshak, William

2007-01-01

The Geostationary Lightning Mapper (GLM) is a single channel, near-IR optical detector, used to detect, locate and measure total lightning activity over the full-disk as part of a 3-axis stabilized, geostationary weather satellite system. The next generation NOAA Geostationary Operational Environmental Satellite (GOES-R) series with a planned launch in 2014 will carry a GLM that will provide continuous day and night observations of lightning from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fully operational. The mission objectives for the GLM are to 1) provide continuous, full-disk lightning measurements for storm warning and Nowcasting, 2) provide early warning of tornadic activity, and 3) accumulate a long-term database to track decadal changes of lightning. The GLM owes its heritage to the NASA Lightning Imaging Sensor (1997-Present) and the Optical Transient Detector (1995-2000), which were developed for the Earth Observing System and have produced a combined 11 year data record of global lightning activity. Instrument formulation studies begun in January 2006 will be completed in March 2007, with implementation expected to begin in September 2007. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite, airborne science missions (e.g., African Monsoon Multi-disciplinary Analysis, AMMA), and regional test beds (e.g, Lightning Mapping Arrays) are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. Real time lightning mapping data now being provided to selected forecast offices will lead to improved understanding of the application of these data in the severe storm warning process and accelerate the development of the pre-launch algorithms and Nowcasting applications. Proxy data combined with MODIS and Meteosat Second Generation SEVERI observations will also lead to new applications (e.g., multi-sensor precipitation algorithms blending the GLM with the Advanced Baseline Imager, convective cloud initiation and identification, early warnings of lightning threat, storm tracking, and data assimilation).

Evaluating the Real-time and Offline Performance of the Virtual Seismologist Earthquake Early Warning Algorithm

NASA Astrophysics Data System (ADS)

Cua, G.; Fischer, M.; Heaton, T.; Wiemer, S.

2009-04-01

The Virtual Seismologist (VS) algorithm is a Bayesian approach to regional, network-based earthquake early warning (EEW). Bayes' theorem as applied in the VS algorithm states that the most probable source estimates at any given time is a combination of contributions from relatively static prior information that does not change over the timescale of earthquake rupture and a likelihood function that evolves with time to take into account incoming pick and amplitude observations from the on-going earthquake. Potentially useful types of prior information include network topology or station health status, regional hazard maps, earthquake forecasts, and the Gutenberg-Richter magnitude-frequency relationship. The VS codes provide magnitude and location estimates once picks are available at 4 stations; these source estimates are subsequently updated each second. The algorithm predicts the geographical distribution of peak ground acceleration and velocity using the estimated magnitude and location and appropriate ground motion prediction equations; the peak ground motion estimates are also updated each second. Implementation of the VS algorithm in California and Switzerland is funded by the Seismic Early Warning for Europe (SAFER) project. The VS method is one of three EEW algorithms whose real-time performance is being evaluated and tested by the California Integrated Seismic Network (CISN) EEW project. A crucial component of operational EEW algorithms is the ability to distinguish between noise and earthquake-related signals in real-time. We discuss various empirical approaches that allow the VS algorithm to operate in the presence of noise. Real-time operation of the VS codes at the Southern California Seismic Network (SCSN) began in July 2008. On average, the VS algorithm provides initial magnitude, location, origin time, and ground motion distribution estimates within 17 seconds of the earthquake origin time. These initial estimate times are dominated by the time for 4 acceptable picks to be available, and thus are heavily influenced by the station density in a given region; these initial estimate times also include the effects of telemetry delay, which ranges between 6 and 15 seconds at the SCSN, and processing time (~1 second). Other relevant performance statistics include: 95% of initial real-time location estimates are within 20 km of the actual epicenter, 97% of initial real-time magnitude estimates are within one magnitude unit of the network magnitude. Extension of real-time VS operations to networks in Northern California is an on-going effort. In Switzerland, the VS codes have been run on offline waveform data from over 125 earthquakes recorded by the Swiss Digital Seismic Network (SDSN) and the Swiss Strong Motion Network (SSMS). We discuss the performance of the VS algorithm on these datasets in terms of magnitude, location, and ground motion estimation.

Validation of the Intensive Care Unit Early Warning Dashboard: Quality Improvement Utilizing a Retrospective Case-Control Evaluation.

PubMed

Kavanaugh, Michael J; So, Joanne D; Park, Peter J; Davis, Konrad L

2017-02-01

Risk stratification with the Modified Early Warning System (MEWS) or electronic cardiac arrest trigger (eCART) has been utilized with ward patients to preemptively identify high-risk patients who might benefit from enhanced monitoring, including early intensive care unit (ICU) transfer. In-hospital mortality from cardiac arrest is ∼80%, making preventative interventions an important focus area. ICUs have lower patient to nurse ratios than wards, resulting in less emphasis on the development of ICU early warning systems. Our institution developed an early warning dashboard (EWD) identifying patients who may benefit from earlier interventions. Using the adverse outcomes of cardiac arrest, ICU mortality, and ICU readmissions, a retrospective case-control study was performed using three demographic items (age, diabetes, and morbid obesity) and 24 EWD measured items, including vital signs, laboratory values, ventilator information, and other clinical information, to validate the EWD. Ten statistically significant areas were identified for cardiac arrest and 13 for ICU death. Identified items included heart rate, dialysis, leukocytosis, and lactate. The ICU readmission outcome was compared to controls from both ICU patients and ward patients, and statistical significance was identified for respiratory rate >30. With several statistically significant data elements, the EWD parameters have been incorporated into advanced clinical decision algorithms to identify at-risk ICU patients. Earlier identification and treatment of organ failure in the ICU improve outcomes and the EWD can serve as a safety measure for both at-risk in-house patients and also extend critical care expertise through telemedicine to smaller hospitals.

Cascade reservoir flood control operation based on risk grading and warning in the Upper Yellow River

NASA Astrophysics Data System (ADS)

Xuejiao, M.; Chang, J.; Wang, Y.

2017-12-01

Flood risk reduction with non-engineering measures has become the main idea for flood management. It is more effective for flood risk management to take various non-engineering measures. In this paper, a flood control operation model for cascade reservoirs in the Upper Yellow River was proposed to lower the flood risk of the water system with multi-reservoir by combining the reservoir flood control operation (RFCO) and flood early warning together. Specifically, a discharge control chart was employed to build the joint RFCO simulation model for cascade reservoirs in the Upper Yellow River. And entropy-weighted fuzzy comprehensive evaluation method was adopted to establish a multi-factorial risk assessment model for flood warning grade. Furthermore, after determining the implementing mode of countermeasures with future inflow, an intelligent optimization algorithm was used to solve the optimization model for applicable water release scheme. In addition, another model without any countermeasure was set to be a comparative experiment. The results show that the model developed in this paper can further decrease the flood risk of water system with cascade reservoirs. It provides a new approach to flood risk management by coupling flood control operation and flood early warning of cascade reservoirs.

An Emergency Packet Forwarding Scheme for V2V Communication Networks

PubMed Central

2014-01-01

This paper proposes an effective warning message forwarding scheme for cooperative collision avoidance. In an emergency situation, an emergency-detecting vehicle warns the neighbor vehicles via an emergency warning message. Since the transmission range is limited, the warning message is broadcast in a multihop manner. Broadcast packets lead two challenges to forward the warning message in the vehicular network: redundancy of warning messages and competition with nonemergency transmissions. In this paper, we study and address the two major challenges to achieve low latency in delivery of the warning message. To reduce the intervehicle latency and end-to-end latency, which cause chain collisions, we propose a two-way intelligent broadcasting method with an adaptable distance-dependent backoff algorithm. Considering locations of vehicles, the proposed algorithm controls the broadcast of a warning message to reduce redundant EWM messages and adaptively chooses the contention window to compete with nonemergency transmission. Via simulations, we show that our proposed algorithm reduces the probability of rear-end crashes by 70% compared to previous algorithms by reducing the intervehicle delay. We also show that the end-to-end propagation delay of the warning message is reduced by 55%. PMID:25054181

Technical note: Efficient online source identification algorithm for integration within a contamination event management system

NASA Astrophysics Data System (ADS)

Deuerlein, Jochen; Meyer-Harries, Lea; Guth, Nicolai

2017-07-01

Drinking water distribution networks are part of critical infrastructures and are exposed to a number of different risks. One of them is the risk of unintended or deliberate contamination of the drinking water within the pipe network. Over the past decade research has focused on the development of new sensors that are able to detect malicious substances in the network and early warning systems for contamination. In addition to the optimal placement of sensors, the automatic identification of the source of a contamination is an important component of an early warning and event management system for security enhancement of water supply networks. Many publications deal with the algorithmic development; however, only little information exists about the integration within a comprehensive real-time event detection and management system. In the following the analytical solution and the software implementation of a real-time source identification module and its integration within a web-based event management system are described. The development was part of the SAFEWATER project, which was funded under FP 7 of the European Commission.

Combining Multiple Rupture Models in Real-Time for Earthquake Early Warning

NASA Astrophysics Data System (ADS)

Minson, S. E.; Wu, S.; Beck, J. L.; Heaton, T. H.

2015-12-01

The ShakeAlert earthquake early warning system for the west coast of the United States is designed to combine information from multiple independent earthquake analysis algorithms in order to provide the public with robust predictions of shaking intensity at each user's location before they are affected by strong shaking. The current contributing analyses come from algorithms that determine the origin time, epicenter, and magnitude of an earthquake (On-site, ElarmS, and Virtual Seismologist). A second generation of algorithms will provide seismic line source information (FinDer), as well as geodetically-constrained slip models (BEFORES, GPSlip, G-larmS, G-FAST). These new algorithms will provide more information about the spatial extent of the earthquake rupture and thus improve the quality of the resulting shaking forecasts.Each of the contributing algorithms exploits different features of the observed seismic and geodetic data, and thus each algorithm may perform differently for different data availability and earthquake source characteristics. Thus the ShakeAlert system requires a central mediator, called the Central Decision Module (CDM). The CDM acts to combine disparate earthquake source information into one unified shaking forecast. Here we will present a new design for the CDM that uses a Bayesian framework to combine earthquake reports from multiple analysis algorithms and compares them to observed shaking information in order to both assess the relative plausibility of each earthquake report and to create an improved unified shaking forecast complete with appropriate uncertainties. We will describe how these probabilistic shaking forecasts can be used to provide each user with a personalized decision-making tool that can help decide whether or not to take a protective action (such as opening fire house doors or stopping trains) based on that user's distance to the earthquake, vulnerability to shaking, false alarm tolerance, and time required to act.

Usage of Wireless Sensor Networks in a service based spatial data infrastructure for Landslide Monitoring and Early Warning

NASA Astrophysics Data System (ADS)

Arnhardt, C.; Fernandez-Steeger, T. M.; Walter, K.; Kallash, A.; Niemeyer, F.; Azzam, R.; Bill, R.

2007-12-01

The joint project Sensor based Landslide Early Warning System (SLEWS) aims at a systematic development of a prototyping alarm- and early warning system for the detection of mass movements by application of an ad hoc wireless sensor network (WSN). Next to the development of suitable sensor setups, sensor fusion and network fusion are applied to enhance data quality and reduce false alarm rates. Of special interest is the data retrieval, processing and visualization in GI-Systems. Therefore a suitable serviced based Spatial Data Infrastructure (SDI) will be developed with respect to existing and upcoming Open Geospatial Consortium (OGC) standards.The application of WSN provides a cheap and easy to set up solution for special monitoring and data gathering in large areas. Measurement data from different low-cost transducers for deformation observation (acceleration, displacement, tilting) is collected by distributed sensor nodes (motes), which interact separately and connect each other in a self-organizing manner. Data are collected and aggregated at the beacon (transmission station) and further operations like data pre-processing and compression can be performed. The WSN concept provides next to energy efficiency, miniaturization, real-time monitoring and remote operation, but also new monitoring strategies like sensor and network fusion. Since not only single sensors can be integrated at single motes either cross-validation or redundant sensor setups are possible to enhance data quality. The planned monitoring and information system will include a mobile infrastructure (information technologies and communication components) as well as methods and models to estimate surface deformation parameters (positioning systems). The measurements result in heterogeneous observation sets that have to be integrated in a common adjustment and filtering approach. Reliable real-time information will be obtained using a range of sensor input and algorithms, from which early warnings and prognosis may be derived. Implementation of sensor algorithms is an important task to form the business logic. This will be represented in self-contained web-based processing services (WPS). In the future different types of sensor networks can communicate via an infrastructure of OGC services using an interoperable way by standardized protocols as the Sensor Markup Language (SensorML) and Observations & Measurements Schema (O&M). Synchronous and asynchronous information services as the Sensor Alert Service (SAS) and the Web Notification Services (WNS) will provide defined users and user groups with time-critical readings from the observation site. Techniques using services for visualizing mapping data (WMS), meta data (CSW), vector (WFS) and raster data (WCS) will range from high detailed expert based output to fuzzy graphical warning elements.The expected results will be an advancement regarding classical alarm and early warning systems as the WSN are free scalable, extensible and easy to install.

Technical implementation plan for the ShakeAlert production system: an Earthquake Early Warning system for the West Coast of the United States

USGS Publications Warehouse

Given, Douglas D.; Cochran, Elizabeth S.; Heaton, Thomas; Hauksson, Egill; Allen, Richard; Hellweg, Peggy; Vidale, John; Bodin, Paul

2014-01-01

Earthquake Early Warning (EEW) systems can provide as much as tens of seconds of warning to people and automated systems before strong shaking arrives. The United States Geological Survey (USGS) and its partners are developing such an EEW system, called ShakeAlert, for the West Coast of the United States. This document describes the technical implementation of that system, which leverages existing stations and infrastructure of the Advanced National Seismic System (ANSS) regional networks to achieve this new capability. While significant progress has been made in developing the ShakeAlert early warning system, improved robustness of each component of the system and additional testing and certification are needed for the system to be reliable enough to issue public alerts. Major components of the system include dense networks of ground motion sensors, telecommunications from those sensors to central processing systems, algorithms for event detection and alert creation, and distribution systems to alert users. Capital investment costs for a West Coast EEW system are projected to be $38.3M, with additional annual maintenance and operations totaling $16.1M—in addition to current ANSS expenditures for earthquake monitoring. An EEW system is complementary to, but does not replace, other strategies to mitigate earthquake losses. The system has limitations: false and missed alerts are possible, and the area very near to an earthquake epicenter may receive little or no warning. However, such an EEW system would save lives, reduce injuries and damage, and improve community resilience by reducing longer-term economic losses for both public and private entities.

CISN ShakeAlert: Faster Warning Information Through Multiple Threshold Event Detection in the Virtual Seismologist (VS) Early Warning Algorithm

NASA Astrophysics Data System (ADS)

Cua, G. B.; Fischer, M.; Caprio, M.; Heaton, T. H.; Cisn Earthquake Early Warning Project Team

2010-12-01

The Virtual Seismologist (VS) earthquake early warning (EEW) algorithm is one of 3 EEW approaches being incorporated into the California Integrated Seismic Network (CISN) ShakeAlert system, a prototype EEW system that could potentially be implemented in California. The VS algorithm, implemented by the Swiss Seismological Service at ETH Zurich, is a Bayesian approach to EEW, wherein the most probable source estimate at any given time is a combination of contributions from a likehihood function that evolves in response to incoming data from the on-going earthquake, and selected prior information, which can include factors such as network topology, the Gutenberg-Richter relationship or previously observed seismicity. The VS codes have been running in real-time at the Southern California Seismic Network since July 2008, and at the Northern California Seismic Network since February 2009. We discuss recent enhancements to the VS EEW algorithm that are being integrated into CISN ShakeAlert. We developed and continue to test a multiple-threshold event detection scheme, which uses different association / location approaches depending on the peak amplitudes associated with an incoming P pick. With this scheme, an event with sufficiently high initial amplitudes can be declared on the basis of a single station, maximizing warning times for damaging events for which EEW is most relevant. Smaller, non-damaging events, which will have lower initial amplitudes, will require more picks to initiate an event declaration, with the goal of reducing false alarms. This transforms the VS codes from a regional EEW approach reliant on traditional location estimation (and the requirement of at least 4 picks as implemented by the Binder Earthworm phase associator) into an on-site/regional approach capable of providing a continuously evolving stream of EEW information starting from the first P-detection. Real-time and offline analysis on Swiss and California waveform datasets indicate that the multiple-threshold approach is faster and more reliable for larger events than the earlier version of the VS codes. In addition, we provide evolutionary estimates of the probability of false alarms (PFA), which is an envisioned output stream of the CISN ShakeAlert system. The real-time decision-making approach envisioned for CISN ShakeAlert users, where users specify a threshhold PFA in addition to thresholds on peak ground motion estimates, has the potential to increase the available warning time for users with high tolerance to false alarms without compromising the needs of users with lower tolerances to false alarms.

Probabilistic and Evolutionary Early Warning System: concepts, performances, and case-studies

NASA Astrophysics Data System (ADS)

Zollo, A.; Emolo, A.; Colombelli, S.; Elia, L.; Festa, G.; Martino, C.; Picozzi, M.

2013-12-01

PRESTo (PRobabilistic and Evolutionary early warning SysTem) is a software platform for Earthquake Early Warning that integrates algorithms for real-time earthquake location, magnitude estimation and damage assessment into a highly configurable and easily portable package. In its regional configuration, the software processes, in real-time, the 3-component acceleration data streams coming from seismic stations, for P-waves arrival detection and, in the case a quite large event is occurring, can promptly performs event detection and location, magnitude estimation and peak ground-motion prediction at target sites. The regional approach has been integrated with a threshold-based early warning method that allows, in the very first seconds after a moderate-to-large earthquake, to identify the most Probable Damaged Zone starting from the real-time measurement at near-source stations located at increasing distances from the earthquake epicenter, of the peak displacement (Pd) and predominant period of P-waves (τc), over a few-second long window after the P-wave arrival. Thus, each recording site independently provides an evolutionary alert level, according to the Pd and τc it measured, through a decisional table. Since 2009, PRESTo has been under continuous real-time testing using data streaming from the Iripinia Seismic Network (Southern Italy) and has produced a bulletin of some hundreds low magnitude events, including all the M≥2.5 earthquakes occurred in that period in Irpinia. Recently, PRESTo has been also implemented at the accelerometric network and broad-band networks in South Korea and in Romania, and off-line tested in Iberian Peninsula, in Turkey, in Israel, and in Japan. The feasibility of an Early Warning System at national scale, is currently under testing by studying the performances of the PRESTo platform for the Italian Accelerometric Network. Moreover, PRESTo is under experimentation in order to provide alert in a high-school located in the neighborhood of Naples at about 100 km from the Irpinia region.

Earthquake Early Warning ShakeAlert System: Testing and certification platform

USGS Publications Warehouse

Cochran, Elizabeth S.; Kohler, Monica D.; Given, Douglas; Guiwits, Stephen; Andrews, Jennifer; Meier, Men-Andrin; Ahmad, Mohammad; Henson, Ivan; Hartog, Renate; Smith, Deborah

2017-01-01

Earthquake early warning systems provide warnings to end users of incoming moderate to strong ground shaking from earthquakes. An earthquake early warning system, ShakeAlert, is providing alerts to beta end users in the western United States, specifically California, Oregon, and Washington. An essential aspect of the earthquake early warning system is the development of a framework to test modifications to code to ensure functionality and assess performance. In 2016, a Testing and Certification Platform (TCP) was included in the development of the Production Prototype version of ShakeAlert. The purpose of the TCP is to evaluate the robustness of candidate code that is proposed for deployment on ShakeAlert Production Prototype servers. TCP consists of two main components: a real‐time in situ test that replicates the real‐time production system and an offline playback system to replay test suites. The real‐time tests of system performance assess code optimization and stability. The offline tests comprise a stress test of candidate code to assess if the code is production ready. The test suite includes over 120 events including local, regional, and teleseismic historic earthquakes, recentering and calibration events, and other anomalous and potentially problematic signals. Two assessments of alert performance are conducted. First, point‐source assessments are undertaken to compare magnitude, epicentral location, and origin time with the Advanced National Seismic System Comprehensive Catalog, as well as to evaluate alert latency. Second, we describe assessment of the quality of ground‐motion predictions at end‐user sites by comparing predicted shaking intensities to ShakeMaps for historic events and implement a threshold‐based approach that assesses how often end users initiate the appropriate action, based on their ground‐shaking threshold. TCP has been developed to be a convenient streamlined procedure for objectively testing algorithms, and it has been designed with flexibility to accommodate significant changes in development of new or modified system code. It is expected that the TCP will continue to evolve along with the ShakeAlert system, and the framework we describe here provides one example of how earthquake early warning systems can be evaluated.

Health management system for rocket engines

NASA Technical Reports Server (NTRS)

Nemeth, Edward

1990-01-01

The functional framework of a failure detection algorithm for the Space Shuttle Main Engine (SSME) is developed. The basic algorithm is based only on existing SSME measurements. Supplemental measurements, expected to enhance failure detection effectiveness, are identified. To support the algorithm development, a figure of merit is defined to estimate the likelihood of SSME criticality 1 failure modes and the failure modes are ranked in order of likelihood of occurrence. Nine classes of failure detection strategies are evaluated and promising features are extracted as the basis for the failure detection algorithm. The failure detection algorithm provides early warning capabilities for a wide variety of SSME failure modes. Preliminary algorithm evaluation, using data from three SSME failures representing three different failure types, demonstrated indications of imminent catastrophic failure well in advance of redline cutoff in all three cases.

On the importance of risk knowledge for an end-to-end tsunami early warning system

NASA Astrophysics Data System (ADS)

Post, Joachim; Strunz, Günter; Riedlinger, Torsten; Mück, Matthias; Wegscheider, Stephanie; Zosseder, Kai; Steinmetz, Tilmann; Gebert, Niklas; Anwar, Herryal

2010-05-01

Warning systems commonly use information provided by networks of sensors able to monitor and detect impending disasters, aggregate and condense these information to provide reliable information to a decision maker whether to warn or not, disseminates the warning message and provide this information to people at risk. Ultimate aim is to enable those in danger to make decisions (e.g. initiate protective actions for buildings) and to take action to safe their lives. This involves very complex issues when considering all four elements of early warning systems (UNISDR-PPEW), namely (1) risk knowledge, (2) monitoring and warning service, (3) dissemination and communication, (4) response capability with the ultimate aim to gain as much time as possible to empower individuals and communities to act in an appropriate manner to reduce injury, loss of life, damage to property and the environment and loss of livelihoods. Commonly most warning systems feature strengths and main attention on the technical/structural dimension (monitoring & warning service, dissemination tools) with weaknesses and less attention on social/cultural dimension (e.g. human response capabilities, defined warning chain to and knowing what to do by the people). Also, the use of risk knowledge in early warning most often is treated in a theoretical manner (knowing that it is somehow important), yet less in an operational, practical sense. Risk assessments and risk maps help to motivate people, prioritise early warning system needs and guide preparations for response and disaster prevention activities. Beyond this risk knowledge can be seen as a tie between national level early warning and community level reaction schemes. This presentation focuses on results, key findings and lessons-learnt related to tsunami risk assessment in the context of early warning within the GITEWS (German-Indonesian Tsunami Early Warning) project. Here a novel methodology reflecting risk information needs in the early warning context has been worked out. The generated results contribute significantly in the fields of (1) warning decision and warning levels, (2) warning dissemination and warning message content, (3) early warning chain planning, (4) increasing response capabilities and protective systems, (5) emergency relief and (6) enhancing communities' awareness and preparedness towards tsunami threats. Additionally examples will be given on the potentials of an operational use of risk information in early warning systems as first experiences exist for the tsunami early warning center in Jakarta, Indonesia. Beside this the importance of linking national level early warning information with tsunami risk information available at the local level (e.g. linking warning message information on expected intensity with respective tsunami hazard zone maps at community level for effective evacuation) will be demonstrated through experiences gained in three pilot areas in Indonesia. The presentation seeks to provide new insights on benefits using risk information in early warning and will provide further evidence that practical use of risk information is an important and indispensable component of end-to-end early warning.

Never Use the Complete Search Space: a Concept to Enhance the Optimization Procedure for Monitoring Networks

NASA Astrophysics Data System (ADS)

Bode, F.; Reuschen, S.; Nowak, W.

2015-12-01

Drinking-water well catchments include many potential sources of contaminations like gas stations or agriculture. Finding optimal positions of early-warning monitoring wells is challenging because there are various parameters (and their uncertainties) that influence the reliability and optimality of any suggested monitoring location or monitoring network.The overall goal of this project is to develop and establish a concept to assess, design and optimize early-warning systems within well catchments. Such optimal monitoring networks need to optimize three competing objectives: a high detection probability, which can be reached by maximizing the "field of vision" of the monitoring network, a long early-warning time such that there is enough time left to install counter measures after first detection, and the overall operating costs of the monitoring network, which should ideally be reduced to a minimum. The method is based on numerical simulation of flow and transport in heterogeneous porous media coupled with geostatistics and Monte-Carlo, scenario analyses for real data, respectively, wrapped up within the framework of formal multi-objective optimization using a genetic algorithm.In order to speed up the optimization process and to better explore the Pareto-front, we developed a concept that forces the algorithm to search only in regions of the search space where promising solutions can be expected. We are going to show how to define these regions beforehand, using knowledge of the optimization problem, but also how to define them independently of problem attributes. With that, our method can be used with and/or without detailed knowledge of the objective functions.In summary, our study helps to improve optimization results in less optimization time by meaningful restrictions of the search space. These restrictions can be done independently of the optimization problem, but also in a problem-specific manner.

CISN ShakeAlert: Using early warnings for earthquakes in California

NASA Astrophysics Data System (ADS)

Vinci, M.; Hellweg, M.; Jones, L. M.; Khainovski, O.; Schwartz, K.; Lehrer, D.; Allen, R. M.; Neuhauser, D. S.

2009-12-01

Educated users who have developed response plans and procedures are just as important for an earthquake early warning (EEW) system as are the algorithms and computers that process the data and produce the warnings. In Japan, for example, the implementation of the EEW system which now provides advanced alerts of ground shaking included intense outreach efforts to both institutional and individual recipients. Alerts are now used in automatic control systems that stop trains, place sensitive equipment in safe mode and isolate hazards while the public takes cover. In California, the California Integrated Seismic Network (CISN) is now developing and implementing components of a prototype system for EEW, ShakeAlert. As this processing system is developed, we invite a suite of perspective users from critical industries and institutions throughout California to partner with us in developing useful ShakeAlert products and procedures. At the same time, we will support their efforts to determine and implement appropriate responses to an early warning of earthquake shaking. As a first step, in a collaboration with BART, we have developed a basic system allowing BART’s operation center to receive realtime ground shaking information from more than 150 seismic stations operating in the San Francisco Bay Area. BART engineers are implementing a display system for this information. Later phases will include the development of improved response procedures utilizing this information. We plan to continue this collaboration to include more sophisticated information from the prototype CISN ShakeAlert system.

Forecasting infectious disease emergence subject to seasonal forcing.

PubMed

Miller, Paige B; O'Dea, Eamon B; Rohani, Pejman; Drake, John M

2017-09-06

Despite high vaccination coverage, many childhood infections pose a growing threat to human populations. Accurate disease forecasting would be of tremendous value to public health. Forecasting disease emergence using early warning signals (EWS) is possible in non-seasonal models of infectious diseases. Here, we assessed whether EWS also anticipate disease emergence in seasonal models. We simulated the dynamics of an immunizing infectious pathogen approaching the tipping point to disease endemicity. To explore the effect of seasonality on the reliability of early warning statistics, we varied the amplitude of fluctuations around the average transmission. We proposed and analyzed two new early warning signals based on the wavelet spectrum. We measured the reliability of the early warning signals depending on the strength of their trend preceding the tipping point and then calculated the Area Under the Curve (AUC) statistic. Early warning signals were reliable when disease transmission was subject to seasonal forcing. Wavelet-based early warning signals were as reliable as other conventional early warning signals. We found that removing seasonal trends, prior to analysis, did not improve early warning statistics uniformly. Early warning signals anticipate the onset of critical transitions for infectious diseases which are subject to seasonal forcing. Wavelet-based early warning statistics can also be used to forecast infectious disease.

Enhanced early warning system impact on nursing practice: A phenomenological study.

PubMed

Burns, Kathleen A; Reber, Tracey; Theodore, Karen; Welch, Brenda; Roy, Debra; Siedlecki, Sandra L

2018-05-01

To determine how an enhanced early warning system has an impact on nursing practice. Early warning systems score physiologic measures and alert nurses to subtle changes in patient condition. Critics of early warning systems have expressed concern that nurses would rely on a score rather than assessment skills and critical thinking to determine the need for intervention. Enhancing early warning systems with innovative technology is still in its infancy, so the impact of an enhanced early warning system on nursing behaviours or practice has not yet been studied. Phenomenological design. Scripted, semistructured interviews were conducted in September 2015 with 25 medical/surgical nurses who used the enhanced early warning system. Data were analysed using thematic analysis techniques (coding and bracketing). Emerging themes were examined for relationships and a model describing the enhanced early warning system experience was developed. Nurses identified awareness leading to investigation and ease of prioritization as the enhanced early warning system's most important impact on their nursing practice. There was also an impact on organizational culture, with nurses reporting improved communication, increased collaboration, increased accountability and proactive responses to early changes in patient condition. Rather than hinder critical thinking, as many early warning systems' critics claim, nurses in this study found that the enhanced early warning system increased their awareness of changes in a patient's condition, resulting in earlier response and reassessment times. It also had an impact on the organization by improving communication and collaboration and supporting a culture of proactive rather than reactive response to early signs of deterioration. © 2017 John Wiley & Sons Ltd.

G-FAST Early Warning Potential for Great Earthquakes in Chile

NASA Astrophysics Data System (ADS)

Crowell, B.; Schmidt, D. A.; Baker, B. I.; Bodin, P.; Vidale, J. E.

2016-12-01

The importance of GNSS-based earthquake early warning for modeling large earthquakes has been studied extensively over the past decade and several such systems are currently under development. In the Pacific Northwest, we have developed the G-FAST GNSS-based earthquake early warning module for eventual inclusion in the US West-Coast wide ShakeAlert system. We have also created a test system that allows us to replay past and synthetic earthquakes to identify problems with both the network architecture and the algorithms. Between 2010 and 2016, there have been seven M > 8 earthquakes across the globe, of which three struck offshore Chile; the 27 February 2010 Mw 8.8 Maule, the 1 April 2014 Mw 8.2 Iquique, and the 16 September 2015 Mw 8.3 Illapel. Subsequent to these events, the Chilean national GNSS network operated by the Centro Sismologico Nacional (http://www.sismologia.cl/) greatly expanded to over 150 continuous GNSS stations, providing the best recordings of great earthquakes with GNSS outside of Japan. Here we report on retrospective G-FAST performance for those three great earthquakes in Chile. We discuss the interplay of location errors, latency, and data completeness with respect to the precision and timing of G-FAST earthquake source alerts as well as the computational demands of the system.

Focus Upon Implementing the GGOS Decadal Vision for Geohazards Monitoring

NASA Astrophysics Data System (ADS)

LaBrecque, John; Stangl, Gunter

2017-04-01

The Global Geodetic Observing System of the IAG identified present and future roles for Geodesy in the development and well being of the global society. The GGOS is focused upon the development of infrastructure, information, analysis, and educational systems to advance the International Global Reference Frame, the International Celestial Reference System, the International Height Reference System, atmospheric dynamics, sea level change and geohazards monitoring. The geohazards initiative is guided by an eleven nation working group initially focused upon the development and integration of regional multi-GNSS networks and analysis systems for earthquake and tsunami early warning. The opportunities and challenges being addressed by the Geohazards working group include regional network design, algorithm development and implementation, communications, funding, and international agreements on data access. This presentation will discuss in further detail these opportunities and challenges for the GGOS focus upon earthquake and tsunami early warning.

The Role of North American Aerospace Defense Command (NORAD) In Military Cyber Attack Warning

DTIC Science & Technology

2015-09-01

WARNING MISSIONS .....................................5  1.  Early North American Air Defense Warning ...................................5  2...BLANK xi LIST OF FIGURES Figure 1.   North American Distant Early Warning (DEW) Site. .......................................6  Figure 2.   Original... Early Warning (AEW) Aircraft .........................................11  Figure 7.   Headquarters NORAD and USNORTHCOM

Technical Note: An operational landslide early warning system at regional scale based on space-time-variable rainfall thresholds

NASA Astrophysics Data System (ADS)

Segoni, S.; Battistini, A.; Rossi, G.; Rosi, A.; Lagomarsino, D.; Catani, F.; Moretti, S.; Casagli, N.

2015-04-01

We set up an early warning system for rainfall-induced landslides in Tuscany (23 000 km2). The system is based on a set of state-of-the-art intensity-duration rainfall thresholds (Segoni et al., 2014b) and makes use of LAMI (Limited Area Model Italy) rainfall forecasts and real-time rainfall data provided by an automated network of more than 300 rain gauges. The system was implemented in a WebGIS to ease the operational use in civil protection procedures: it is simple and intuitive to consult, and it provides different outputs. When switching among different views, the system is able to focus both on monitoring of real-time data and on forecasting at different lead times up to 48 h. Moreover, the system can switch between a basic data view where a synoptic scenario of the hazard can be shown all over the region and a more in-depth view were the rainfall path of rain gauges can be displayed and constantly compared with rainfall thresholds. To better account for the variability of the geomorphological and meteorological settings encountered in Tuscany, the region is subdivided into 25 alert zones, each provided with a specific threshold. The warning system reflects this subdivision: using a network of more than 300 rain gauges, it allows for the monitoring of each alert zone separately so that warnings can be issued independently. An important feature of the warning system is that the visualization of the thresholds in the WebGIS interface may vary in time depending on when the starting time of the rainfall event is set. The starting time of the rainfall event is considered as a variable by the early warning system: whenever new rainfall data are available, a recursive algorithm identifies the starting time for which the rainfall path is closest to or overcomes the threshold. This is considered the most hazardous condition, and it is displayed by the WebGIS interface. The early warning system is used to forecast and monitor the landslide hazard in the whole region, providing specific alert levels for 25 distinct alert zones. In addition, the system can be used to gather, analyze, display, explore, interpret and store rainfall data, thus representing a potential support to both decision makers and scientists.

Automated Identification of Initial Storm Electrification and End-of-Storm Electrification Using Electric Field Mill Sensors

NASA Technical Reports Server (NTRS)

Maier, Launa M.; Huddleston, Lisa L.

2017-01-01

Kennedy Space Center (KSC) operations are located in a region which experiences one of the highest lightning densities across the United States. As a result, on average, KSC loses almost 30 minutes of operational availability each day for lightning sensitive activities. KSC is investigating using existing instrumentation and automated algorithms to improve the timeliness and accuracy of lightning warnings. Additionally, the automation routines will be warning on a grid to minimize under-warnings associated with not being located in the center of the warning area and over-warnings associated with encompassing too large an area. This study discusses utilization of electric field mill data to provide improved warning times. Specifically, this paper will demonstrate improved performance of an enveloping algorithm of the electric field mill data as compared with the electric field zero crossing to identify initial storm electrification. End-of-Storm-Oscillation (EOSO) identification algorithms will also be analyzed to identify performance improvement, if any, when compared with 30 minutes after the last lightning flash.

Earthquake Early Warning and Public Policy: Opportunities and Challenges

NASA Astrophysics Data System (ADS)

Goltz, J. D.; Bourque, L.; Tierney, K.; Riopelle, D.; Shoaf, K.; Seligson, H.; Flores, P.

2003-12-01

Development of an earthquake early warning capability and pilot project were objectives of TriNet, a 5-year (1997-2001) FEMA-funded project to develop a state-of-the-art digital seismic network in southern California. In parallel with research to assemble a protocol for rapid analysis of earthquake data and transmission of a signal by TriNet scientists and engineers, the public policy, communication and educational issues inherent in implementation of an earthquake early warning system were addressed by TriNet's outreach component. These studies included: 1) a survey that identified potential users of an earthquake early warning system and how an earthquake early warning might be used in responding to an event, 2) a review of warning systems and communication issues associated with other natural hazards and how lessons learned might be applied to an alerting system for earthquakes, 3) an analysis of organization, management and public policy issues that must be addressed if a broad-based warning system is to be developed and 4) a plan to provide earthquake early warnings to a small number of organizations in southern California as an experimental prototype. These studies provided needed insights into the social and cultural environment in which this new technology will be introduced, an environment with opportunities to enhance our response capabilities but also an environment with significant barriers to overcome to achieve a system that can be sustained and supported. In this presentation we will address the main public policy issues that were subjects of analysis in these studies. They include a discussion of the possible division of functions among organizations likely to be the principle partners in the management of an earthquake early warning system. Drawing on lessons learned from warning systems for other hazards, we will review the potential impacts of false alarms and missed events on warning system credibility, the acceptability of fully automated warning systems and equity issues associated with possible differential access to warnings. Finally, we will review the status of legal authorities and liabilities faced by organizations that assume various warning system roles and possible approaches to setting up a pilot project to introduce early warning. Our presentation will suggest that introducing an early warning system requires multi-disciplinary and multi-agency cooperation and thoughtful discussion among organizations likely to be providers and participants in an early warning system. Recalling our experience with earthquake prediction, we will look at early warning as a promising but unproven technology and recommend moving forward with caution and patience.

ACCEPT: Introduction of the Adverse Condition and Critical Event Prediction Toolbox

NASA Technical Reports Server (NTRS)

Martin, Rodney A.; Santanu, Das; Janakiraman, Vijay Manikandan; Hosein, Stefan

2015-01-01

The prediction of anomalies or adverse events is a challenging task, and there are a variety of methods which can be used to address the problem. In this paper, we introduce a generic framework developed in MATLAB (sup registered mark) called ACCEPT (Adverse Condition and Critical Event Prediction Toolbox). ACCEPT is an architectural framework designed to compare and contrast the performance of a variety of machine learning and early warning algorithms, and tests the capability of these algorithms to robustly predict the onset of adverse events in any time-series data generating systems or processes.

Safety Early Warning Research for Highway Construction Based on Case-Based Reasoning and Variable Fuzzy Sets

PubMed Central

Liu, Yan; Xu, Zhen-Jun

2013-01-01

As a high-risk subindustry involved in construction projects, highway construction safety has experienced major developments in the past 20 years, mainly due to the lack of safe early warnings in Chinese construction projects. By combining the current state of early warning technology with the requirements of the State Administration of Work Safety and using case-based reasoning (CBR), this paper expounds on the concept and flow of highway construction safety early warnings based on CBR. The present study provides solutions to three key issues, index selection, accident cause association analysis, and warning degree forecasting implementation, through the use of association rule mining, support vector machine classifiers, and variable fuzzy qualitative and quantitative change criterion modes, which fully cover the needs of safe early warning systems. Using a detailed description of the principles and advantages of each method and by proving the methods' effectiveness and ability to act together in safe early warning applications, effective means and intelligent technology for a safe highway construction early warning system are established. PMID:24191134

Safety early warning research for highway construction based on case-based reasoning and variable fuzzy sets.

PubMed

Liu, Yan; Yi, Ting-Hua; Xu, Zhen-Jun

2013-01-01

As a high-risk subindustry involved in construction projects, highway construction safety has experienced major developments in the past 20 years, mainly due to the lack of safe early warnings in Chinese construction projects. By combining the current state of early warning technology with the requirements of the State Administration of Work Safety and using case-based reasoning (CBR), this paper expounds on the concept and flow of highway construction safety early warnings based on CBR. The present study provides solutions to three key issues, index selection, accident cause association analysis, and warning degree forecasting implementation, through the use of association rule mining, support vector machine classifiers, and variable fuzzy qualitative and quantitative change criterion modes, which fully cover the needs of safe early warning systems. Using a detailed description of the principles and advantages of each method and by proving the methods' effectiveness and ability to act together in safe early warning applications, effective means and intelligent technology for a safe highway construction early warning system are established.

Study on Early-Warning System of Cotton Production in Hebei Province

NASA Astrophysics Data System (ADS)

Zhang, Runqing; Ma, Teng

Cotton production plays an important role in Hebei. It straightly influences cotton farmers’ life, agricultural production and national economic development as well. In recent years, due to cotton production frequently fluctuating, two situations, “difficult selling cotton” and “difficult buying cotton” have alternately occurred, and brought disadvantages to producers, businesses and national finance. Therefore, it is very crucial to research the early warning of cotton production for solving the problem of cotton production’s frequent fluctuation and ensuring the cotton industry’s sustainable development. This paper founds a signal lamp model of early warning through employing time-difference correlation analysis method to select early-warning indicators and statistical analysis method associated with empirical analysis to determine early-warning limits. Finally, it not only obtained warning conditions of cotton production from 1993 to 2006 and forecast 2007’s condition, but also put forward corresponding countermeasures to prevent cotton production from fluctuating. Furthermore, an early-warning software of cotton production is completed through computer programming on the basis of the early warning model above.

Report on dynamic speed harmonization and queue warning algorithm design.

DOT National Transportation Integrated Search

2014-02-01

This report provides a detailed description of the algorithms that will be used to generate harmonized recommended speeds and queue warning information in the proposed Intelligent Network Flow Optimization (INFLO) prototype. This document describes t...

Implementing Obstetric Early Warning Systems.

PubMed

Friedman, Alexander M; Campbell, Mary L; Kline, Carolyn R; Wiesner, Suzanne; D'Alton, Mary E; Shields, Laurence E

2018-04-01

Severe maternal morbidity and mortality are often preventable and obstetric early warning systems that alert care providers of potential impending critical illness may improve maternal safety. While literature on outcomes and test characteristics of maternal early warning systems is evolving, there is limited guidance on implementation. Given current interest in early warning systems and their potential role in care, the 2017 Society for Maternal-Fetal Medicine (SMFM) Annual Meeting dedicated a session to exploring early warning implementation across a wide range of hospital settings. This manuscript reports on key points from this session. While implementation experiences varied based on factors specific to individual sites, common themes relevant to all hospitals presenting were identified. Successful implementation of early warnings systems requires administrative and leadership support, dedication of resources, improved coordination between nurses, providers, and ancillary staff, optimization of information technology, effective education, evaluation of and change in hospital culture and practices, and support in provider decision-making. Evolving data on outcomes on early warning systems suggest that maternal risk may be reduced. To effectively reduce maternal, risk early warning systems that capture deterioration from a broad range of conditions may be required in addition to bundles tailored to specific conditions such as hemorrhage, thromboembolism, and hypertension.

Knowledge of stroke risk factors and early warning signs of stroke among students enrolled in allied health programs: a pilot study.

PubMed

Milner, Abby; Lewis, William J; Ellis, Charles

2008-01-01

The inclusion of stroke education modules early in medical school curricula has resulted in improved stroke knowledge in graduate physicians. The success of these programs suggests that allied health professions programs should also consider strategies to improve stroke knowledge in students preparing for allied health careers that also require knowledge of stroke risk factors and early warning signs. Currently, little is known about stroke knowledge in students enrolled in allied health professions programs. 208 first- and second-year students enrolled in allied health programs completed a survey of stroke risk factors and early warning signs of stroke. Risk factor knowledge - 99% identified smoking as a risk factor; 67% identified diabetes; 93% identified high cholesterol; 89% identified age; and 92% identified physical inactivity. Less than 50% of the students identified all 5 risk factors. There were no differences between first- and second-year students in risk factor knowledge. Early warning signs and first response knowledge - 89% recognized sudden confusion or trouble speaking; 94% recognized sudden facial, arm, or leg weakness; 65% recognized sudden vision loss; 82% recognized sudden trouble walking; and 73% recognized sudden headache as early warning signs of stroke. Eighty-one percent recognized calling 9-1-1 as the appropriate first action. However, only 25% recognized all five early warning signs and only 20% recognized all five early warning signs and would call 9-1-1 as the first action. There were differences between first- and second-year students in recognizing 3 of 5 early warning signs and appropriate first action to call 9-1-1. Most students recognized individual stroke risk factors and early warning signs but few recognized multiple risk factors and early warning signs of stroke.

Early warning of limit-exceeding concentrations of cyanobacteria and cyanotoxins in drinking water reservoirs by inferential modelling.

PubMed

Recknagel, Friedrich; Orr, Philip T; Bartkow, Michael; Swanepoel, Annelie; Cao, Hongqing

2017-11-01

An early warning scheme is proposed that runs ensembles of inferential models for predicting the cyanobacterial population dynamics and cyanotoxin concentrations in drinking water reservoirs on a diel basis driven by in situ sonde water quality data. When the 10- to 30-day-ahead predicted concentrations of cyanobacteria cells or cyanotoxins exceed pre-defined limit values, an early warning automatically activates an action plan considering in-lake control, e.g. intermittent mixing and ad hoc water treatment in water works, respectively. Case studies of the sub-tropical Lake Wivenhoe (Australia) and the Mediterranean Vaal Reservoir (South Africa) demonstrate that ensembles of inferential models developed by the hybrid evolutionary algorithm HEA are capable of up to 30days forecasts of cyanobacteria and cyanotoxins using data collected in situ. The resulting models for Dolicospermum circinale displayed validity for up to 10days ahead, whilst concentrations of Cylindrospermopsis raciborskii and microcystins were successfully predicted up to 30days ahead. Implementing the proposed scheme for drinking water reservoirs enhances current water quality monitoring practices by solely utilising in situ monitoring data, in addition to cyanobacteria and cyanotoxin measurements. Access to routinely measured cyanotoxin data allows for development of models that predict explicitly cyanotoxin concentrations that avoid to inadvertently model and predict non-toxic cyanobacterial strains. Copyright © 2017 Elsevier B.V. All rights reserved.

Automated recognition and tracking of aerosol threat plumes with an IR camera pod

NASA Astrophysics Data System (ADS)

Fauth, Ryan; Powell, Christopher; Gruber, Thomas; Clapp, Dan

2012-06-01

Protection of fixed sites from chemical, biological, or radiological aerosol plume attacks depends on early warning so that there is time to take mitigating actions. Early warning requires continuous, autonomous, and rapid coverage of large surrounding areas; however, this must be done at an affordable cost. Once a potential threat plume is detected though, a different type of sensor (e.g., a more expensive, slower sensor) may be cued for identification purposes, but the problem is to quickly identify all of the potential threats around the fixed site of interest. To address this problem of low cost, persistent, wide area surveillance, an IR camera pod and multi-image stitching and processing algorithms have been developed for automatic recognition and tracking of aerosol plumes. A rugged, modular, static pod design, which accommodates as many as four micro-bolometer IR cameras for 45deg to 180deg of azimuth coverage, is presented. Various OpenCV1 based image-processing algorithms, including stitching of multiple adjacent FOVs, recognition of aerosol plume objects, and the tracking of aerosol plumes, are presented using process block diagrams and sample field test results, including chemical and biological simulant plumes. Methods for dealing with the background removal, brightness equalization between images, and focus quality for optimal plume tracking are also discussed.

Design and internal validation of an obstetric early warning score: secondary analysis of the Intensive Care National Audit and Research Centre Case Mix Programme database.

PubMed

Carle, C; Alexander, P; Columb, M; Johal, J

2013-04-01

We designed and internally validated an aggregate weighted early warning scoring system specific to the obstetric population that has the potential for use in the ward environment. Direct obstetric admissions from the Intensive Care National Audit and Research Centre's Case Mix Programme Database were randomly allocated to model development (n = 2240) or validation (n = 2200) sets. Physiological variables collected during the first 24 h of critical care admission were analysed. Logistic regression analysis for mortality in the model development set was initially used to create a statistically based early warning score. The statistical score was then modified to create a clinically acceptable early warning score. Important features of this clinical obstetric early warning score are that the variables are weighted according to their statistical importance, a surrogate for the FI O2 /Pa O2 relationship is included, conscious level is assessed using a simplified alert/not alert variable, and the score, trigger thresholds and response are consistent with the new non-obstetric National Early Warning Score system. The statistical and clinical early warning scores were internally validated using the validation set. The area under the receiver operating characteristic curve was 0.995 (95% CI 0.992-0.998) for the statistical score and 0.957 (95% CI 0.923-0.991) for the clinical score. Pre-existing empirically designed early warning scores were also validated in the same way for comparison. The area under the receiver operating characteristic curve was 0.955 (95% CI 0.922-0.988) for Swanton et al.'s Modified Early Obstetric Warning System, 0.937 (95% CI 0.884-0.991) for the obstetric early warning score suggested in the 2003-2005 Report on Confidential Enquiries into Maternal Deaths in the UK, and 0.973 (95% CI 0.957-0.989) for the non-obstetric National Early Warning Score. This highlights that the new clinical obstetric early warning score has an excellent ability to discriminate survivors from non-survivors in this critical care data set. Further work is needed to validate our new clinical early warning score externally in the obstetric ward environment. Anaesthesia © 2013 The Association of Anaesthetists of Great Britain and Ireland.

Early Warning System Implementation Guide: For Use with the National High School Center's Early Warning System Tool v2.0

ERIC Educational Resources Information Center

Therriault, Susan Bowles; Heppen, Jessica; O'Cummings, Mindee; Fryer, Lindsay; Johnson, Amy

2010-01-01

This Early Warning System (EWS) Implementation Guide is a supporting document for schools and districts that are implementing the National High School Center's Early Warning System (EWS) Tool v2.0. Developed by the National High School Center at the American Institutes for Research (AIR), the guide and tool support the establishment and…

Implementation and testing of a sensor-netting algorithm for early warning and high confidence C/B threat detection

NASA Astrophysics Data System (ADS)

Gruber, Thomas; Grim, Larry; Fauth, Ryan; Tercha, Brian; Powell, Chris; Steinhardt, Kristin

2011-05-01

Large networks of disparate chemical/biological (C/B) sensors, MET sensors, and intelligence, surveillance, and reconnaissance (ISR) sensors reporting to various command/display locations can lead to conflicting threat information, questions of alarm confidence, and a confused situational awareness. Sensor netting algorithms (SNA) are being developed to resolve these conflicts and to report high confidence consensus threat map data products on a common operating picture (COP) display. A data fusion algorithm design was completed in a Phase I SBIR effort and development continues in the Phase II SBIR effort. The initial implementation and testing of the algorithm has produced some performance results. The algorithm accepts point and/or standoff sensor data, and event detection data (e.g., the location of an explosion) from various ISR sensors (e.g., acoustic, infrared cameras, etc.). These input data are preprocessed to assign estimated uncertainty to each incoming piece of data. The data are then sent to a weighted tomography process to obtain a consensus threat map, including estimated threat concentration level uncertainty. The threat map is then tested for consistency and the overall confidence for the map result is estimated. The map and confidence results are displayed on a COP. The benefits of a modular implementation of the algorithm and comparisons of fused / un-fused data results will be presented. The metrics for judging the sensor-netting algorithm performance are warning time, threat map accuracy (as compared to ground truth), false alarm rate, and false alarm rate v. reported threat confidence level.

Application of the Risk-Based Early Warning Method in a Fracture-Karst Water Source, North China.

PubMed

Guo, Yongli; Wu, Qing; Li, Changsuo; Zhao, Zhenhua; Sun, Bin; He, Shiyi; Jiang, Guanghui; Zhai, Yuanzheng; Guo, Fang

2018-03-01

  The paper proposes a risk-based early warning considering characteristics of fracture-karst aquifer in North China and applied it in a super-large fracture-karst water source. Groundwater vulnerability, types of land use, water abundance, transmissivity and spatial temporal variation of groundwater quality were chosen as indexes of the method. Weights of factors were obtained by using AHP method based on relative importance of factors, maps of factors were zoned by GIS, early warning map was conducted based on extension theory with the help of GIS, ENVI+IDL. The early warning map fused five factors very well, serious and tremendous warning areas are mainly located in northwest and east with high or relatively high transmissivity and groundwater pollutant loading, and obviously deteriorated or deteriorated trend of petroleum. The early warning map warns people where more attention should be paid, and the paper guides decision making to take appropriate protection actions in different warning levels areas.

Early identification systems for emerging foodborne hazards.

PubMed

Marvin, H J P; Kleter, G A; Prandini, A; Dekkers, S; Bolton, D J

2009-05-01

This paper provides a non-exhausting overview of early warning systems for emerging foodborne hazards that are operating in the various places in the world. Special attention is given to endpoint-focussed early warning systems (i.e. ECDC, ISIS and GPHIN) and hazard-focussed early warning systems (i.e. FVO, RASFF and OIE) and their merit to successfully identify a food safety problem in an early stage is discussed. Besides these early warning systems which are based on monitoring of either disease symptoms or hazards, also early warning systems and/or activities that intend to predict the occurrence of a food safety hazard in its very beginning of development or before that are described. Examples are trend analysis, horizon scanning, early warning systems for mycotoxins in maize and/or wheat and information exchange networks (e.g. OIE and GIEWS). Furthermore, recent initiatives that aim to develop predictive early warning systems based on the holistic principle are discussed. The assumption of the researchers applying this principle is that developments outside the food production chain that are either directly or indirectly related to the development of a particular food safety hazard may also provide valuable information to predict the development of this hazard.

Automatic early warning of tail biting in pigs: 3D cameras can detect lowered tail posture before an outbreak

PubMed Central

Jack, Mhairi; Futro, Agnieszka; Talbot, Darren; Zhu, Qiming; Barclay, David; Baxter, Emma M.

2018-01-01

Tail biting is a major welfare and economic problem for indoor pig producers worldwide. Low tail posture is an early warning sign which could reduce tail biting unpredictability. Taking a precision livestock farming approach, we used Time-of-flight 3D cameras, processing data with machine vision algorithms, to automate the measurement of pig tail posture. Validation of the 3D algorithm found an accuracy of 73.9% at detecting low vs. not low tails (Sensitivity 88.4%, Specificity 66.8%). Twenty-three groups of 29 pigs per group were reared with intact (not docked) tails under typical commercial conditions over 8 batches. 15 groups had tail biting outbreaks, following which enrichment was added to pens and biters and/or victims were removed and treated. 3D data from outbreak groups showed the proportion of low tail detections increased pre-outbreak and declined post-outbreak. Pre-outbreak, the increase in low tails occurred at an increasing rate over time, and the proportion of low tails was higher one week pre-outbreak (-1) than 2 weeks pre-outbreak (-2). Within each batch, an outbreak and a non-outbreak control group were identified. Outbreak groups had more 3D low tail detections in weeks -1, +1 and +2 than their matched controls. Comparing 3D tail posture and tail injury scoring data, a greater proportion of low tails was associated with more injured pigs. Low tails might indicate more than just tail biting as tail posture varied between groups and over time and the proportion of low tails increased when pigs were moved to a new pen. Our findings demonstrate the potential for a 3D machine vision system to automate tail posture detection and provide early warning of tail biting on farm. PMID:29617403

ElarmS Earthquake Early Warning System 2016 Performance and New Research

NASA Astrophysics Data System (ADS)

Chung, A. I.; Allen, R. M.; Hellweg, M.; Henson, I. H.; Neuhauser, D. S.

2016-12-01

The ElarmS earthquake early warning system has been detecting earthquakes throughout California since 2007. It is one of the algorithms that contributes to the West Coast ShakeAlert, a prototype earthquake early warning system being developed for the US West Coast. ElarmS is also running in the Pacific Northwest, and in Israel, Chile, Turkey, and Peru in test mode. We summarize the performance of the ElarmS system over the past year and review some of the more problematic events that the system has encountered. During the first half of 2016 (2016-01-01 through 2016-07-21), ElarmS successfully alerted on all events with ANSS catalog magnitudes M>3 in the Los Angeles area. The mean alert time for these 9 events was just 4.84 seconds. In the San Francisco Bay Area, ElarmS detected 26 events with ANSS catalog magnitudes M>3. The alert times for these events is 9.12 seconds. The alert times are longer in the Bay Area than in the Los Angeles area due to the sparser network of stations in the Bay Area. 7 Bay Area events were not detected by ElarmS. These events occurred in areas where there is less dense station coverage. In addition, ElarmS sent alerts for 13 of the 16 moderately-sized (ANSS catalog magnitudes M>4) events that occurred throughout the state of California. One of those missed events was a M4.5 that occurred far offshore in the northernmost part of the state. The other two missed events occurred inland in regions with sparse station coverage. Over the past year, we have worked towards the implementation of a new filterbank teleseismic filter algorithm, which we will discuss. Other than teleseismic events, a significant cause of false alerts and severely mislocated events is spurious triggers being associated with triggers from a real earthquake. Here, we address new approaches to filtering out problematic triggers.

Assessing the performance of regional landslide early warning models: the EDuMaP method

NASA Astrophysics Data System (ADS)

Calvello, M.; Piciullo, L.

2016-01-01

A schematic of the components of regional early warning systems for rainfall-induced landslides is herein proposed, based on a clear distinction between warning models and warning systems. According to this framework an early warning system comprises a warning model as well as a monitoring and warning strategy, a communication strategy and an emergency plan. The paper proposes the evaluation of regional landslide warning models by means of an original approach, called the "event, duration matrix, performance" (EDuMaP) method, comprising three successive steps: identification and analysis of the events, i.e., landslide events and warning events derived from available landslides and warnings databases; definition and computation of a duration matrix, whose elements report the time associated with the occurrence of landslide events in relation to the occurrence of warning events, in their respective classes; evaluation of the early warning model performance by means of performance criteria and indicators applied to the duration matrix. During the first step the analyst identifies and classifies the landslide and warning events, according to their spatial and temporal characteristics, by means of a number of model parameters. In the second step, the analyst computes a time-based duration matrix with a number of rows and columns equal to the number of classes defined for the warning and landslide events, respectively. In the third step, the analyst computes a series of model performance indicators derived from a set of performance criteria, which need to be defined by considering, once again, the features of the warning model. The applicability, potentialities and limitations of the EDuMaP method are tested and discussed using real landslides and warning data from the municipal early warning system operating in Rio de Janeiro (Brazil).

Fast Moment Magnitude Determination from P-wave Trains for Bucharest Rapid Early Warning System (BREWS)

NASA Astrophysics Data System (ADS)

Lizurek, Grzegorz; Marmureanu, Alexandru; Wiszniowski, Jan

2017-03-01

Bucharest, with a population of approximately 2 million people, has suffered damage from earthquakes in the Vrancea seismic zone, which is located about 170 km from Bucharest, at a depth of 80-200 km. Consequently, an earthquake early warning system (Bucharest Rapid earthquake Early Warning System or BREWS) was constructed to provide some warning about impending shaking from large earthquakes in the Vrancea zone. In order to provide quick estimates of magnitude, seismic moment was first determined from P-waves and then a moment magnitude was determined from the moment. However, this magnitude may not be consistent with previous estimates of magnitude from the Romanian Seismic Network. This paper introduces the algorithm using P-wave spectral levels and compares them with catalog estimates. The testing procedure used waveforms from about 90 events with catalog magnitudes from 3.5 to 5.4. Corrections to the P-wave determined magnitudes according to dominant intermediate depth events mechanism were tested for November 22, 2014, M5.6 and October 17, M6 events. The corrections worked well, but unveiled overestimation of the average magnitude result of about 0.2 magnitude unit in the case of shallow depth event ( H < 60 km). The P-wave spectral approach allows for the relatively fast estimates of magnitude for use in BREWS. The average correction taking into account the most common focal mechanism for radiation pattern coefficient may lead to overestimation of the magnitude for shallow events of about 0.2 magnitude unit. However, in case of events of intermediate depth of M6 the resulting M w is underestimated at about 0.1-0.2. We conclude that our P-wave spectral approach is sufficiently robust for the needs of BREWS for both shallow and intermediate depth events.

Ballistic Missile Early Warning System Clear Air Force Station, ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

Ballistic Missile Early Warning System - Clear Air Force Station, Ballistic Missile Early Warning System Site II, One mile west of mile marker 293.5 on Parks Highway, 5 miles southwest of Anderson, Anderson, Denali Borough, AK

Application of Geographical Information Systems and Remote Sensing technologies for assessing and monitoring malaria risk.

PubMed

Ceccato, P; Connor, S J; Jeanne, I; Thomson, M C

2005-03-01

Despite over 30 years of scientific research, algorithm development and multitudes of publications relating Remote Sensing (RS) information with the spatial and temporal distribution of malaria, it is only in recent years that operational products have been adopted by malaria control decision-makers. The time is ripe for the wealth of research knowledge and products from developed countries be made available to the decision-makers in malarious regions of the globe where this information is urgently needed. This paper reviews the capability of RS to provide useful information for operational malaria early warning systems. It also reviews the requirements for monitoring the major components influencing emergence of malaria and provides examples of applications that have been made. Discussion of the issues that have impeded implementation on a global scale and how those barriers are disappearing with recent economic, technological and political developments are explored; and help pave the way for implementation of an integrated Malaria Early Warning System framework using RS technologies.

A proposed Primary Health Early Warning Score (PHEWS) with emphasis on early detection of sepsis in the elderly.

PubMed

Anderson, Ian

2016-03-01

There are several secondary care early warning scores which alert for severe illness including sepsis. None are specifically adjusted for primary care. A Primary Health Early Warning Score (PHEWS) is proposed which incorporates practical parameters from both secondary and primary care.

Estimation for aerial detection effectiveness with cooperation efficiency factors of early-warning aircraft in early-warning detection SoS under BSC framework

NASA Astrophysics Data System (ADS)

Zhu, Feng; Hu, Xiaofeng; He, Xiaoyuan; Guo, Rui; Li, Kaiming; Yang, Lu

2017-11-01

In the military field, the performance evaluation of early-warning aircraft deployment or construction is always an important problem needing to be explored. As an effective approach of enterprise management and performance evaluation, Balanced Score Card (BSC) attracts more and more attentions and is studied more and more widely all over the world. It can also bring feasible ideas and technical approaches for studying the issue of the performance evaluation of the deployment or construction of early-warning aircraft which is the important component in early-warning detection system of systems (SoS). Therefore, the deep explored researches are carried out based on the previously research works. On the basis of the characteristics of space exploration and aerial detection effectiveness of early-warning detection SoS and the cardinal principle of BSC are analyzed simply, and the performance evaluation framework of the deployment or construction of early-warning aircraft is given, under this framework, aimed at the evaluation issue of aerial detection effectiveness of early-warning detection SoS with the cooperation efficiency factors of the early-warning aircraft and other land based radars, the evaluation indexes are further designed and the relative evaluation model is further established, especially the evaluation radar chart being also drawn to obtain the evaluation results from a direct sight angle. Finally, some practical computer simulations are launched to prove the validity and feasibility of the research thinking and technologic approaches which are proposed in the paper.

People-centred landslide early warning systems in the context of risk management

NASA Astrophysics Data System (ADS)

Haß, S.; Asch, K.; Fernandez-Steeger, T.; Arnhardt, C.

2009-04-01

In the current hazard research people-centred warning becomes more and more important, because different types of organizations and groups have to be involved in the warning process. This fact has to be taken into account when developing early warning systems. The effectiveness of early warning depends not only on technical capabilities but also on the preparedness of decision makers and their immediate response on how to act in case of emergency. Hence early warning systems have to be regarded in the context of an integrated and holistic risk management. Disaster Risk Reduction (DRR) measures include people-centred, timely and understandable warning. Further responsible authorities have to be identified in advance and standards for risk communication have to be established. Up to now, hazard and risk assessment for geohazards focuses on the development of inventory, susceptibility, hazard and risk maps. But often, especially in Europe, there are no institutional structures for managing geohazards and in addition there is a lack of an authority that is legally obliged to alarm on landslides at national or regional level. One of the main characteristics within the warning process for natural hazards e.g. in Germany is the split of responsibility between scientific authorities (wissenschaftliche Fachbehörde) and enforcement authorities (Vollzugsbehörde). The scientific authority provides the experts who define the methods and measures for monitoring and evaluate the hazard level. The main focus is the acquisition and evaluation of data and subsequently the distribution of information. The enforcement authority issues official warnings about dangerous natural phenomena. Hence the information chain in the context of early warning ranges over two different institutions, the forecast service and the warning service. But there doesn't exist a framework for warning processes in terms of landslides as yet. The concept for managing natural disasters is often reduced to hazard assessment and emergency response. Great importance is attached to the scientific understanding of hazards and protective structures, while analysis of socio-economic impacts and risk assessment are not considered enough. The reduction of vulnerability has to be taken into greater account. Also the information needs of different stakeholders have to be identified at an early stage and should be integrated in the development of early warning systems. The content of the warning message must be simple, understandable and should cover instructions on how to react. Further the timeliness of the messages has to be guarented. In this context the aim of the landslide monitoring and early warning system SLEWS (Sensor Based Landslide Early Warning System) is to integrate the above mentioned aspects of a holistic disaster and risk management. The technology of spatial data infrastructures and web services provides the use of multiple communication channels within an early warning system. Thus people-centred early warning messages and information about slope stability can be sent in nearly real-time. It has to be underlined that the technological information process is just one element of an effective warning system. Moreover the warning system has also to be considered as a social system and has to make allowance to socio-economic and gender aspects : «[...] Develop early warning systems that are people centered, in particular systems whose warnings are timely and understandable to those at risk, which take into account the demographic, gender, cultural and livelihood characteristics of the target audiences, including guidance on how to act upon warnings, and that support effective operations by disaster managers and other decision makers » (Hyogo Framework, 2005) References : UNITED NATIONS INTERNATIONAL STRATEGY FOR DISASTER REDUCTION SECRETARIAT (UNISDR) (2006): Developing early warning systems: a checklist, Third international conference on early warning (EWC III): from concept to action: 27-29 March 2006, Bonn, Germany. Geneva, Switzerland: International Strategy for Disaster Reduction. WORLD CONFERENCE ON DISASTER REDUCTION (2005) : Report of the World Conference on Disaster Reduction: Kobe, Hyogo, Japan, 18-22 January 2005. Geneva, Switzerland, Secretariat, World Conference on Disaster Reduction. INTER-AGENCY SECRETARIAT OF THE ISDR & GLOBAL PLATFORM FOR DISASTER RISK REDUCTION (2007): Disaster risk reduction: 2007 global review. Geneva, UN, ISDR.

Alaskan Air Defense and Early Warning Systems Clear Air ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

Alaskan Air Defense and Early Warning Systems - Clear Air Force Station, Ballistic Missile Early Warning System Site II, One mile west of mile marker 293.5 on Parks Highway, 5 miles southwest of Anderson, Anderson, Denali Borough, AK

Assessing the performance of regional landslide early warning models: the EDuMaP method

NASA Astrophysics Data System (ADS)

Calvello, M.; Piciullo, L.

2015-10-01

The paper proposes the evaluation of the technical performance of a regional landslide early warning system by means of an original approach, called EDuMaP method, comprising three successive steps: identification and analysis of the Events (E), i.e. landslide events and warning events derived from available landslides and warnings databases; definition and computation of a Duration Matrix (DuMa), whose elements report the time associated with the occurrence of landslide events in relation to the occurrence of warning events, in their respective classes; evaluation of the early warning model Performance (P) by means of performance criteria and indicators applied to the duration matrix. During the first step, the analyst takes into account the features of the warning model by means of ten input parameters, which are used to identify and classify landslide and warning events according to their spatial and temporal characteristics. In the second step, the analyst computes a time-based duration matrix having a number of rows and columns equal to the number of classes defined for the warning and landslide events, respectively. In the third step, the analyst computes a series of model performance indicators derived from a set of performance criteria, which need to be defined by considering, once again, the features of the warning model. The proposed method is based on a framework clearly distinguishing between local and regional landslide early warning systems as well as among correlation laws, warning models and warning systems. The applicability, potentialities and limitations of the EDuMaP method are tested and discussed using real landslides and warnings data from the municipal early warning system operating in Rio de Janeiro (Brazil).

A Risk-Based Multi-Objective Optimization Concept for Early-Warning Monitoring Networks

NASA Astrophysics Data System (ADS)

Bode, F.; Loschko, M.; Nowak, W.

2014-12-01

Groundwater is a resource for drinking water and hence needs to be protected from contaminations. However, many well catchments include an inventory of known and unknown risk sources which cannot be eliminated, especially in urban regions. As matter of risk control, all these risk sources should be monitored. A one-to-one monitoring situation for each risk source would lead to a cost explosion and is even impossible for unknown risk sources. However, smart optimization concepts could help to find promising low-cost monitoring network designs.In this work we develop a concept to plan monitoring networks using multi-objective optimization. Our considered objectives are to maximize the probability of detecting all contaminations and the early warning time and to minimize the installation and operating costs of the monitoring network. A qualitative risk ranking is used to prioritize the known risk sources for monitoring. The unknown risk sources can neither be located nor ranked. Instead, we represent them by a virtual line of risk sources surrounding the production well.We classify risk sources into four different categories: severe, medium and tolerable for known risk sources and an extra category for the unknown ones. With that, early warning time and detection probability become individual objectives for each risk class. Thus, decision makers can identify monitoring networks which are valid for controlling the top risk sources, and evaluate the capabilities (or search for least-cost upgrade) to also cover moderate, tolerable and unknown risk sources. Monitoring networks which are valid for the remaining risk also cover all other risk sources but the early-warning time suffers.The data provided for the optimization algorithm are calculated in a preprocessing step by a flow and transport model. Uncertainties due to hydro(geo)logical phenomena are taken into account by Monte-Carlo simulations. To avoid numerical dispersion during the transport simulations we use the particle-tracking random walk method.

The effectiveness of physiologically based early warning or track and trigger systems after triage in adult patients presenting to emergency departments: a systematic review.

PubMed

Wuytack, Francesca; Meskell, Pauline; Conway, Aislinn; McDaid, Fiona; Santesso, Nancy; Hickey, Fergal G; Gillespie, Paddy; Raymakers, Adam J N; Smith, Valerie; Devane, Declan

2017-12-06

Changes to physiological parameters precede deterioration of ill patients. Early warning and track and trigger systems (TTS) use routine physiological measurements with pre-specified thresholds to identify deteriorating patients and trigger appropriate and timely escalation of care. Patients presenting to the emergency department (ED) are undiagnosed, undifferentiated and of varying acuity, yet the effectiveness and cost-effectiveness of using early warning systems and TTS in this setting is unclear. We aimed to systematically review the evidence on the use, development/validation, clinical effectiveness and cost-effectiveness of physiologically based early warning systems and TTS for the detection of deterioration in adult patients presenting to EDs. We searched for any study design in scientific databases and grey literature resources up to March 2016. Two reviewers independently screened results and conducted quality assessment. One reviewer extracted data with independent verification of 50% by a second reviewer. Only information available in English was included. Due to the heterogeneity of reporting across studies, results were synthesised narratively and in evidence tables. We identified 6397 citations of which 47 studies and 1 clinical trial registration were included. Although early warning systems are increasingly used in EDs, compliance varies. One non-randomised controlled trial found that using an early warning system in the ED may lead to a change in patient management but may not reduce adverse events; however, this is uncertain, considering the very low quality of evidence. Twenty-eight different early warning systems were developed/validated in 36 studies. There is relatively good evidence on the predictive ability of certain early warning systems on mortality and ICU/hospital admission. No health economic data were identified. Early warning systems seem to predict adverse outcomes in adult patients of varying acuity presenting to the ED but there is a lack of high quality comparative studies to examine the effect of using early warning systems on patient outcomes. Such studies should include health economics assessments.

Robust Kalman filter design for predictive wind shear detection

NASA Technical Reports Server (NTRS)

Stratton, Alexander D.; Stengel, Robert F.

1991-01-01

Severe, low-altitude wind shear is a threat to aviation safety. Airborne sensors under development measure the radial component of wind along a line directly in front of an aircraft. In this paper, optimal estimation theory is used to define a detection algorithm to warn of hazardous wind shear from these sensors. To achieve robustness, a wind shear detection algorithm must distinguish threatening wind shear from less hazardous gustiness, despite variations in wind shear structure. This paper presents statistical analysis methods to refine wind shear detection algorithm robustness. Computational methods predict the ability to warn of severe wind shear and avoid false warning. Comparative capability of the detection algorithm as a function of its design parameters is determined, identifying designs that provide robust detection of severe wind shear.

Data Delivery Latency Improvements And First Steps Towards The Distributed Computing Of The Caltech/USGS Southern California Seismic Network Earthquake Early Warning System

NASA Astrophysics Data System (ADS)

Stubailo, I.; Watkins, M.; Devora, A.; Bhadha, R. J.; Hauksson, E.; Thomas, V. I.

2016-12-01

The USGS/Caltech Southern California Seismic Network (SCSN) is a modern digital ground motion seismic network. It develops and maintains Earthquake Early Warning (EEW) data collection and delivery systems in southern California as well as real-time EEW algorithms. Recently, Behr et al., SRL, 2016 analyzed data from several regional seismic networks deployed around the globe. They showed that the SCSN was the network with the smallest data communication delays or latency. Since then, we have reduced further the telemetry delays for many of the 330 current sites. The latency has been reduced on average from 2-6 sec to 0.4 seconds by tuning the datalogger parameters and/or deploying software upgrades. Recognizing the latency data as one of the crucial parameters in EEW, we have started archiving the per-packet latencies in mseed format for all the participating sites in a similar way it is traditionally done for the seismic waveform data. The archived latency values enable us to understand and document long-term changes in performance of the telemetry links. We can also retroactively investigate how latent the waveform data were during a specific event or during a specific time period. In addition the near-real time latency values are useful for monitoring and displaying the real-time station latency, in particular to compare different telemetry technologies. A future step to reduce the latency is to deploy the algorithms on the dataloggers at the seismic stations and transmit either the final solutions or intermediate parameters to a central processing center. To implement this approach, we are developing a stand-alone version of the OnSite algorithm to run on the dataloggers in the field. This will increase the resiliency of the SCSN to potential telemetry restrictions in the immediate aftermath of a large earthquake, either by allowing local alarming by the single station, or permitting transmission of lightweight parametric information rather than continuous waveform data to the central processing facility. State-of-the-art development of Internet of Things (IoT) tools and platforms, which can be used to distribute and maintain software on a large number of remote devices are making this approach to earthquake early warning more feasible.

Urban Flood Prevention and Early Warning System in Jinan City

NASA Astrophysics Data System (ADS)

Feng, Shiyuan; Li, Qingguo

2018-06-01

The system construction of urban flood control and disaster reduction in China is facing pressure and challenge from new urban water disaster. Under the circumstances that it is difficult to build high standards of flood protection engineering measures in urban areas, it is particularly important to carry out urban flood early warning. In Jinan City, a representative inland area, based on the index system of early warning of flood in Jinan urban area, the method of fuzzy comprehensive evaluation was adopted to evaluate the level of early warning. Based on the cumulative rainfall of 3 hours, the CAflood simulation results based on cellular automaton model of urban flooding were used as evaluation indexes to realize the accuracy and integration of urban flood control early warning.

A lightning-based nowcast-warning approach for short-duration rainfall events: Development and testing over Beijing during the warm seasons of 2006-2007

NASA Astrophysics Data System (ADS)

Wu, Fan; Cui, Xiaopeng; Zhang, Da-Lin

2018-06-01

Nowcasting short-duration (i.e., <6 h) rainfall (SDR) events is examined using total [i.e., cloud-to-ground (CG) and intra-cloud (IC)] lightning observations over the Beijing Metropolitan Region (BMR) during the warm seasons of 2006-2007. A total of 928 moderate and 554 intense SDR events, i.e., with the respective hourly rainfall rates (HRR) of 10-20 and ≥20 mm h-1, are utilized to estimate sharp-increasing rates in rainfall and lightning flash, termed as rainfall and lightning jumps, respectively. By optimizing the parameters in a lightning jump and a rainfall jump algorithm, their different jump intensity grades are verified for the above two categories of SDR events. Then, their corresponding graded nowcast-warning models are developed for the moderate and intense SDR events, respectively, with a low-grade warning for hitting more SDR events and a high-grade warning for reducing false alarms. Any issued warning in the nowcast-warning models is designed to last for 2 h after the occurrence of a lightning jump. It is demonstrated that the low-grade warnings can have the probability of detection (POD) of 67.8% (87.0%) and the high-grade warnings have the false alarms ratio (FAR) of 27.0% (22.2%) for the moderate (intense) SDR events, with an averaged lead time of 36.7 (52.0) min. The nowcast-warning models are further validated using three typical heavy-rain-producing storms that are independent from those used to develop the models. Results show that the nowcast-warning models can provide encouraging early warnings for the associated SDR events from the regional to meso-γ scales, indicating that they have a great potential in being applied to the other regions where high-resolution total lightning observations are available.

Technology-Based Early Warning Systems for Bipolar Disorder: A Conceptual Framework

PubMed Central

Torous, John; Thompson, Wesley

2016-01-01

Recognition and timely action around “warning signs” of illness exacerbation is central to the self-management of bipolar disorder. Due to its heterogeneity and fluctuating course, passive and active mobile technologies have been increasingly evaluated as adjunctive or standalone tools to predict and prevent risk of worsening of course in bipolar disorder. As predictive analytics approaches to big data from mobile health (mHealth) applications and ancillary sensors advance, it is likely that early warning systems will increasingly become available to patients. Such systems could reduce the amount of time spent experiencing symptoms and diminish the immense disability experienced by people with bipolar disorder. However, in addition to the challenges in validating such systems, we argue that early warning systems may not be without harms. Probabilistic warnings may be delivered to individuals who may not be able to interpret the warning, have limited information about what behaviors to change, or are unprepared to or cannot feasibly act due to time or logistic constraints. We propose five essential elements for early warning systems and provide a conceptual framework for designing, incorporating stakeholder input, and validating early warning systems for bipolar disorder with a focus on pragmatic considerations. PMID:27604265

Tsunami early warning in the Mediterranean: role, structure and tricks of pre-computed tsunami simulation databases and matching/forecasting algorithms

NASA Astrophysics Data System (ADS)

Armigliato, Alberto; Pagnoni, Gianluca; Tinti, Stefano

2014-05-01

The general idea that pre-computed simulated scenario databases can play a key role in conceiving tsunami early warning systems is commonly accepted by now. But it was only in the last decade that it started to be applied to the Mediterranean region, taking special impulse from initiatives like the GDACS and from recently concluded EU-funded projects such as TRIDEC and NearToWarn. With reference to these two projects and with the possibility of further developing this research line in the frame of the FP7 ASTARTE project, we discuss some results we obtained regarding two major topics, namely the strategies applicable to the tsunami scenario database building and the design and performance assessment of a timely and "reliable" elementary-scenario combination algorithm to be run in real-time. As for the first theme, we take advantage of the experience gained in the test areas of Western Iberia, Rhodes (Greece) and Cyprus to illustrate the criteria with which a "Matching Scenario Database" (MSDB) can be built. These involve 1) the choice of the main tectonic tsunamigenic sources (or areas), 2) their tessellation with matrices of elementary faults whose dimension heavily depend on the particular studied area and must be a compromise between the needs to represent the tsunamigenic area in sufficient detail and of limiting the number of scenarios to be simulated, 3) the computation of the scenarios themselves, 4) the choice of the relevant simulation outputs and the standardisation of their formats. Regarding the matching/forecast algorithm, we want it to select and combine the MSDB elements based on the initial earthquake magnitude and location estimate, and to produce a forecast of (at least) the tsunami arrival time, amplitude and period at the closest tide-level sensors and in all needed forecast points. We discuss the performance of the algorithm in terms of the time needed to produce the forecast after the earthquake is detected. In particular, we analyse the different contributions of a number of factors such as the efficient code development and availability of cutting-edge hardware to run the code itself, the wise selection of the MSDB outputs to be combined, the choice of the forecast points where water elevation time series must be taken into account, and few others.

[Ecological security early-warning in Zhoushan Islands based on variable weight model].

PubMed

Zhou, Bin; Zhong, Lin-sheng; Chen, Tian; Zhou, Rui

2015-06-01

Ecological security early warning, as an important content of ecological security research, is of indicating significance in maintaining regional ecological security. Based on driving force, pressure, state, impact and response (D-P-S-I-R) framework model, this paper took Zhoushan Islands in Zhejiang Province as an example to construct the ecological security early warning index system, test degrees of ecological security early warning of Zhoushan Islands from 2000 to 2012 by using the method of variable weight model, and forecast ecological security state of 2013-2018 by Markov prediction method. The results showed that the variable weight model could meet the study needs of ecological security early warning of Zhoushan Islands. There was a fluctuant rising ecological security early warning index from 0.286 to 0.484 in Zhoushan Islands between year 2000 and 2012, in which the security grade turned from "serious alert" into " medium alert" and the indicator light turned from "orange" to "yellow". The degree of ecological security warning was "medium alert" with the light of "yellow" for Zhoushan Islands from 2013 to 2018. These findings could provide a reference for ecological security maintenance of Zhoushan Islands.

A Sustainable Early Warning System for Climate Change Impacts on Water Quality Management

NASA Astrophysics Data System (ADS)

Lee, T.; Tung, C.; Chung, N.

2007-12-01

In this era of rapid social and technological change leading to interesting life complexity and environmental displacement, both positive and negative effects among ecosystems call for a balance in which there are impacts by climate changes. Early warning systems for climate change impacts are necessary in order to allow society as a whole to properly and usefully assimilate the masses of new information and knowledge. Therefore, our research addresses to build up a sustainable early warning mechanism. The main goal is to mitigate the cumulative impacts on the environment of climate change and enhance adaptive capacities. An effective early warning system has been proven for protection. However, there is a problem that estimate future climate changes would be faced with high uncertainty. In general, take estimations for climate change impacts would use the data from General Circulation Models and take the analysis as the Intergovernmental Panel on Climate Change declared. We follow the course of the method for analyzing climate change impacts and attempt to accomplish the sustainable early warning system for water quality management. Climate changes impact not only on individual situation but on short-term variation and long-term gradually changes. This kind characteristic should adopt the suitable warning system for long-term formulation and short- term operation. To continue the on-going research of the long-term early warning system for climate change impacts on water quality management, the short-term early warning system is established by using local observation data for reappraising the warning issue. The combination of long-term and short-term system can provide more circumstantial details. In Taiwan, a number of studies have revealed that climate change impacts on water quality, especially in arid period, the concentration of biological oxygen demand may turn into worse. Rapid population growth would also inflict injury on its assimilative capacity to degenerate. To concern about those items, the sustainable early warning system is established and the initiative fall into the following categories: considering the implications for policies, applying adaptive strategies and informing the new climate changes. By setting up the framework of early warning system expectantly can defend stream area from impacts damaging and in sure the sustainable development.

49 CFR Appendix C to Part 512 - Early Warning Reporting Class Determinations

Code of Federal Regulations, 2011 CFR

2011-10-01

... 49 Transportation 6 2011-10-01 2011-10-01 false Early Warning Reporting Class Determinations C Appendix C to Part 512 Transportation Other Regulations Relating to Transportation (Continued) NATIONAL.... 512, App. C Appendix C to Part 512—Early Warning Reporting Class Determinations (a) The Chief Counsel...

49 CFR Appendix C to Part 512 - Early Warning Reporting Class Determinations

Code of Federal Regulations, 2012 CFR

2012-10-01

... 49 Transportation 6 2012-10-01 2012-10-01 false Early Warning Reporting Class Determinations C Appendix C to Part 512 Transportation Other Regulations Relating to Transportation (Continued) NATIONAL.... 512, App. C Appendix C to Part 512—Early Warning Reporting Class Determinations (a) The Chief Counsel...

49 CFR Appendix C to Part 512 - Early Warning Reporting Class Determinations

Code of Federal Regulations, 2010 CFR

2010-10-01

... 49 Transportation 6 2010-10-01 2010-10-01 false Early Warning Reporting Class Determinations C Appendix C to Part 512 Transportation Other Regulations Relating to Transportation (Continued) NATIONAL.... 512, App. C Appendix C to Part 512—Early Warning Reporting Class Determinations (a) The Chief Counsel...

49 CFR Appendix C to Part 512 - Early Warning Reporting Class Determinations

Code of Federal Regulations, 2013 CFR

2013-10-01

... 49 Transportation 6 2013-10-01 2013-10-01 false Early Warning Reporting Class Determinations C Appendix C to Part 512 Transportation Other Regulations Relating to Transportation (Continued) NATIONAL.... 512, App. C Appendix C to Part 512—Early Warning Reporting Class Determinations (a) The Chief Counsel...

49 CFR Appendix C to Part 512 - Early Warning Reporting Class Determinations

Code of Federal Regulations, 2014 CFR

2014-10-01

... 49 Transportation 6 2014-10-01 2014-10-01 false Early Warning Reporting Class Determinations C Appendix C to Part 512 Transportation Other Regulations Relating to Transportation (Continued) NATIONAL.... 512, App. C Appendix C to Part 512—Early Warning Reporting Class Determinations (a) The Chief Counsel...

The Role of Cash Flow in Financial Early Warning of Agricultural Enterprises Based on Logistic Model

NASA Astrophysics Data System (ADS)

Sun, Fengru

2018-01-01

This paper chooses the agricultural listed companies as the research object, compares the financial situation of the enterprise and the theory of financial early warning, combines the financial status of the agricultural listed companies, selects the relevant cash flow indicators, discusses the application of the Logistic financial early warning model in the agricultural listed companies, Agricultural enterprises get better development. Research on financial early warning of agricultural listed companies will help the agricultural listed companies to predict the financial crisis. Financial early warning model is simple to establish, operational and strong, the use of financial early warning model, to help enterprises in the financial crisis before taking rapid and effective measures, which can avoid losses. Help enterprises to discover signs of deterioration of the financial situation in time to maintain the sustainable development of agricultural enterprises. In addition, through the financial early warning model, investors can correctly identify the financial situation of agricultural enterprises, and can evaluate the financial situation of agricultural enterprises and to help investors to invest in scientific and rational, beneficial to investors to analyze the safety of investment. But also help the relevant regulatory agencies to effectively monitor the market and promote the healthy and stable development of the market.

Earthquake Early Warning Management based on Client-Server using Primary Wave data from Vibrating Sensor

NASA Astrophysics Data System (ADS)

Laumal, F. E.; Nope, K. B. N.; Peli, Y. S.

2018-01-01

Early warning is a warning mechanism before an actual incident occurs, can be implemented on natural events such as tsunamis or earthquakes. Earthquakes are classified in tectonic and volcanic types depend on the source and nature. The tremor in the form of energy propagates in all directions as Primary and Secondary waves. Primary wave as initial earthquake vibrations propagates longitudinally, while the secondary wave propagates like as a sinusoidal wave after Primary, destructive and as a real earthquake. To process the primary vibration data captured by the earthquake sensor, a network management required client computer to receives primary data from sensors, authenticate and forward to a server computer to set up an early warning system. With the water propagation concept, a method of early warning system has been determined in which some sensors are located on the same line, sending initial vibrations as primary data on the same scale and the server recommended to the alarm sound as an early warning.

Massachusetts Early Warning Indicator System (EWIS). "Technical Descriptions of Risk Model Development": Early and Late Elementary Age Groupings (Grades 1-6)

ERIC Educational Resources Information Center

Massachusetts Department of Elementary and Secondary Education, 2013

2013-01-01

The Massachusetts Department of Elementary and Secondary Education (Department) created the grades 1-12 Early Warning Indicator System (EWIS) in response to district interest in the Early Warning Indicator Index (EWII) that the Department previously created for rising grade 9 students. Districts shared that the EWII data were helpful, but also…

Organizing Schools to Address Early Warning Indicators (EWIs): Common Practices and Challenges

ERIC Educational Resources Information Center

Davis, Marcia; Herzog, Liza; Legters, Nettie

2013-01-01

An early warning system is an intentional process whereby school personnel collectively analyze student data to monitor students at risk of falling off track for graduation and to provide the interventions and resources to intervene. We studied the process of monitoring the early warning indicators and implementing interventions to ascertain…

78 FR 78321 - Early Warning Reporting, Foreign Defect Reporting, and Motor Vehicle and Equipment Recall...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-12-26

... DEPARTMENT OF TRANSPORTATION National Highway Traffic Safety Administration 49 CFR Parts 573, 577, and 579 [Docket No. NHTSA--2012-0068; Notice 3] RIN 2127-AK72 Early Warning Reporting, Foreign Defect... final rule. Id. Manufacturers with early warning reporting (EWR) accounts may obtain a copy of the VIN...

Looming auditory collision warnings for driving.

PubMed

Gray, Rob

2011-02-01

A driving simulator was used to compare the effectiveness of increasing intensity (looming) auditory warning signals with other types of auditory warnings. Auditory warnings have been shown to speed driver reaction time in rear-end collision situations; however, it is not clear which type of signal is the most effective. Although verbal and symbolic (e.g., a car horn) warnings have faster response times than abstract warnings, they often lead to more response errors. Participants (N=20) experienced four nonlooming auditory warnings (constant intensity, pulsed, ramped, and car horn), three looming auditory warnings ("veridical," "early," and "late"), and a no-warning condition. In 80% of the trials, warnings were activated when a critical response was required, and in 20% of the trials, the warnings were false alarms. For the early (late) looming warnings, the rate of change of intensity signaled a time to collision (TTC) that was shorter (longer) than the actual TTC. Veridical looming and car horn warnings had significantly faster brake reaction times (BRT) compared with the other nonlooming warnings (by 80 to 160 ms). However, the number of braking responses in false alarm conditions was significantly greater for the car horn. BRT increased significantly and systematically as the TTC signaled by the looming warning was changed from early to veridical to late. Looming auditory warnings produce the best combination of response speed and accuracy. The results indicate that looming auditory warnings can be used to effectively warn a driver about an impending collision.

Validation of a Pediatric Early Warning Score in Hospitalized Pediatric Oncology and Hematopoietic Stem Cell Transplant Patients.

PubMed

Agulnik, Asya; Forbes, Peter W; Stenquist, Nicole; Rodriguez-Galindo, Carlos; Kleinman, Monica

2016-04-01

To evaluate the correlation of a Pediatric Early Warning Score with unplanned transfer to the PICU in hospitalized oncology and hematopoietic stem cell transplant patients. We performed a retrospective matched case-control study, comparing the highest documented Pediatric Early Warning Score within 24 hours prior to unplanned PICU transfers in hospitalized pediatric oncology and hematopoietic stem cell transplant patients between September 2011 and December 2013. Controls were patients who remained on the inpatient unit and were matched 2:1 using age, condition (oncology vs hematopoietic stem cell transplant), and length of hospital stay. Pediatric Early Warning Scores were documented by nursing staff at least every 4 hours as part of routine care. Need for transfer was determined by a PICU physician called to evaluate the patient. A large tertiary/quaternary free-standing academic children's hospital. One hundred ten hospitalized pediatric oncology patients (42 oncology, 68 hematopoietic stem cell transplant) requiring unplanned PICU transfer and 220 matched controls. None. Using the highest score in the 24 hours prior to transfer for cases and a matched time period for controls, the Pediatric Early Warning Score was highly correlated with the need for PICU transfer overall (area under the receiver operating characteristic = 0.96), and in the oncology and hematopoietic stem cell transplant groups individually (area under the receiver operating characteristic = 0.95 and 0.96, respectively). The difference in Pediatric Early Warning Score results between the cases and controls was noted as early as 24 hours prior to PICU admission. Seventeen patients died (15.4%). Patients with higher Pediatric Early Warning Scores prior to transfer had increased PICU mortality (p = 0.028) and length of stay (p = 0.004). We demonstrate that our institution's Pediatric Early Warning Score is highly correlated with the need for unplanned PICU transfer in hospitalized oncology and hematopoietic stem cell transplant patients. Furthermore, we found an association between higher scores and PICU mortality. This is the first validation of a Pediatric Early Warning Score specific to the pediatric oncology and hematopoietic stem cell transplant populations, and supports the use of Pediatric Early Warning Scores as a method of early identification of clinical deterioration in this high-risk population.

Exploring the Role of Social Memory of Floods for Designing Flood Early Warning Operations

NASA Astrophysics Data System (ADS)

Girons Lopez, Marc; Di Baldassarre, Giuliano; Grabs, Thomas; Halldin, Sven; Seibert, Jan

2016-04-01

Early warning systems are an important tool for natural disaster mitigation practices, especially for flooding events. Warnings rely on near-future forecasts to provide time to take preventive actions before a flood occurs, thus reducing potential losses. However, on top of the technical capacities, successful warnings require an efficient coordination and communication among a range of different actors and stakeholders. The complexity of integrating the technical and social spheres of warning systems has, however, resulted in system designs neglecting a number of important aspects such as social awareness of floods thus leading to suboptimal results. A better understanding of the interactions and feedbacks among the different elements of early warning systems is therefore needed to improve their efficiency and therefore social resilience. When designing an early warning system two important decisions need to be made regarding (i) the hazard magnitude at and from which a warning should be issued and (ii) the degree of confidence required for issuing a warning. The first decision is usually taken based on the social vulnerability and climatic variability while the second one is related to the performance (i.e. accuracy) of the forecasting tools. Consequently, by estimating the vulnerability and the accuracy of the forecasts, these two variables can be optimized to minimize the costs and losses. Important parameters with a strong influence on the efficiency of warning systems such as social awareness are however not considered in their design. In this study we present a theoretical exploration of the impact of social awareness on the design of early warning systems. For this purpose we use a definition of social memory of flood events as a proxy for flood risk awareness and test its effect on the optimization of the warning system design variables. Understanding the impact of social awareness on warning system design is important to make more robust warnings that can better adapt to different social settings and more efficiently reduce vulnerability.

GOES-R Geostationary Lightning Mapper Performance Specifications and Algorithms

NASA Technical Reports Server (NTRS)

Mach, Douglas M.; Goodman, Steven J.; Blakeslee, Richard J.; Koshak, William J.; Petersen, William A.; Boldi, Robert A.; Carey, Lawrence D.; Bateman, Monte G.; Buchler, Dennis E.; McCaul, E. William, Jr.

2008-01-01

The Geostationary Lightning Mapper (GLM) is a single channel, near-IR imager/optical transient event detector, used to detect, locate and measure total lightning activity over the full-disk. The next generation NOAA Geostationary Operational Environmental Satellite (GOES-R) series will carry a GLM that will provide continuous day and night observations of lightning. The mission objectives for the GLM are to: (1) Provide continuous, full-disk lightning measurements for storm warning and nowcasting, (2) Provide early warning of tornadic activity, and (2) Accumulate a long-term database to track decadal changes of lightning. The GLM owes its heritage to the NASA Lightning Imaging Sensor (1997- present) and the Optical Transient Detector (1995-2000), which were developed for the Earth Observing System and have produced a combined 13 year data record of global lightning activity. GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2 algorithms and applications. The science data will consist of lightning "events", "groups", and "flashes". The algorithm is being designed to be an efficient user of the computational resources. This may include parallelization of the code and the concept of sub-dividing the GLM FOV into regions to be processed in parallel. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional test beds (e.g., Lightning Mapping Arrays in North Alabama, Oklahoma, Central Florida, and the Washington DC Metropolitan area) are being used to develop the prelaunch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution.

Managing Risks? Early Warning Systems for Climate Change

NASA Astrophysics Data System (ADS)

Sitati, A. M.; Zommers, Z. A.; Habilov, M.

2014-12-01

Early warning systems are a tool with which to minimize risks posed by climate related hazards. Although great strides have been made in developing early warning systems most deal with one hazard, only provide short-term warnings and do not reach the most vulnerable. This presentation will review research results of the United Nations Environment Programme's CLIM-WARN project. The project seeks to identify how governments can better communicate risks by designing multi-hazard early warning systems that deliver actionable warnings across timescales. Household surveys and focus group discussions were conducted in 36 communities in Kenya, Ghana and Burkina Faso in order to identify relevant climate related hazards, current response strategies and early warning needs. Preliminary results show significant variability in both risks and needs within and between countries. For instance, floods are more frequent in rural western parts of Kenya. Droughts are frequent in the north while populations in urban areas face a range of hazards - floods, droughts, disease outbreaks - that sometimes occur simultaneously. The majority of the rural population, especially women, the disabled and the elderly, do not have access to modern media such as radio, television, or internet. While 55% of rural populace never watches television, 64% of urban respondents watch television on a daily basis. Communities have different concepts of how to design warning systems. It will be a challenge for national governments to create systems that accommodate such diversity yet provide standard quality of service to all. There is a need for flexible and forward-looking early warning systems that deliver broader information about risks. Information disseminated through the system could not only include details of hazards, but also long-term adaptation options, general education, and health information, thus increasingly both capabilities and response options.

CISN ShakeAlert: Accounting for site amplification effects and quantifying time and spatial dependence of uncertainty estimates in the Virtual Seismologist earthquake early warning algorithm

NASA Astrophysics Data System (ADS)

Caprio, M.; Cua, G. B.; Wiemer, S.; Fischer, M.; Heaton, T. H.; CISN EEW Team

2011-12-01

The Virtual Seismologist (VS) earthquake early warning (EEW) algorithm is one of 3 EEW approaches being incorporated into the California Integrated Seismic Network (CISN) ShakeAlert system, a prototype EEW system being tested in real-time in California. The VS algorithm, implemented by the Swiss Seismological Service at ETH Zurich, is a Bayesian approach to EEW, wherein the most probable source estimate at any given time is a combination of contributions from a likehihood function that evolves in response to incoming data from the on-going earthquake, and selected prior information, which can include factors such as network topology, the Gutenberg-Richter relationship or previously observed seismicity. The VS codes have been running in real-time at the Southern California Seismic Network (SCSN) since July 2008, and at the Northern California Seismic Network (NCSN) since February 2009. With the aim of improving the convergence of real-time VS magnitude estimates to network magnitudes, we evaluate various empirical and Vs30-based approaches to accounting for site amplification. Empirical station corrections for SCSN stations are derived from M>3.0 events from 2005 through 2009. We evaluate the performance of the various approaches using an independent 2010 dataset. In addition, we analyze real-time VS performance from 2008 to the present to quantify the time and spatial dependence of VS uncertainty estimates. We also summarize real-time VS performance for significant 2011 events in California. Improved magnitude and uncertainty estimates potentially increase the utility of EEW information for end-users, particularly those intending to automate damage-mitigating actions based on real-time information.

Early warning system for Douglas-fir tussock moth outbreaks in the Western United States.

Treesearch

Gary E. Daterman; John M. Wenz; Katharine A. Sheehan

2004-01-01

The Early Warning System is a pheromone-based trapping system used to detect outbreaks of Douglas-fir tussock moth (DFTM, Orgyia pseudotsugata) in the western United States. Millions of acres are susceptible to DFTM defoliation, but Early Warning System monitoring focuses attention only on the relatively limited areas where outbreaks may be...

Combining multiple earthquake models in real time for earthquake early warning

USGS Publications Warehouse

Minson, Sarah E.; Wu, Stephen; Beck, James L; Heaton, Thomas H.

2017-01-01

The ultimate goal of earthquake early warning (EEW) is to provide local shaking information to users before the strong shaking from an earthquake reaches their location. This is accomplished by operating one or more real‐time analyses that attempt to predict shaking intensity, often by estimating the earthquake’s location and magnitude and then predicting the ground motion from that point source. Other EEW algorithms use finite rupture models or may directly estimate ground motion without first solving for an earthquake source. EEW performance could be improved if the information from these diverse and independent prediction models could be combined into one unified, ground‐motion prediction. In this article, we set the forecast shaking at each location as the common ground to combine all these predictions and introduce a Bayesian approach to creating better ground‐motion predictions. We also describe how this methodology could be used to build a new generation of EEW systems that provide optimal decisions customized for each user based on the user’s individual false‐alarm tolerance and the time necessary for that user to react.

Body size shifts and early warning signals precede the historic collapse of whale stocks.

PubMed

Clements, Christopher F; Blanchard, Julia L; Nash, Kirsty L; Hindell, Mark A; Ozgul, Arpat

2017-06-22

Predicting population declines is a key challenge in the face of global environmental change. Abundance-based early warning signals have been shown to precede population collapses; however, such signals are sensitive to the low reliability of abundance estimates. Here, using historical data on whales harvested during the 20th century, we demonstrate that early warning signals can be present not only in the abundance data, but also in the more reliable body size data of wild populations. We show that during the period of commercial whaling, the mean body size of caught whales declined dramatically (by up to 4 m over a 70-year period), leading to early warning signals being detectable up to 40 years before the global collapse of whale stocks. Combining abundance and body size data can reduce the length of the time series required to predict collapse, and decrease the chances of false positive early warning signals.

Landslide risk mitigation by means of early warning systems

NASA Astrophysics Data System (ADS)

Calvello, Michele

2017-04-01

Among the many options available to mitigate landslide risk, early warning systems may be used where, in specific circumstances, the risk to life increases above tolerable levels. A coherent framework to classify and analyse landslide early warning systems (LEWS) is herein presented. Once the objectives of an early warning strategy are defined depending on the scale of analysis and the type of landslides to address, the process of designing and managing a LEWS should synergically employ technical and social skills. A classification scheme for the main components of LEWSs is proposed for weather-induced landslides. The scheme is based on a clear distinction among: i) the landslide model, i.e. a functional relationship between weather characteristics and landslide events considering the geotechnical, geomorphological and hydro-geological characterization of the area as well as an adequate monitoring strategy; ii) the warning model, i.e. the landslide model plus procedures to define the warning events and to issue the warnings; iii) the warning system, i.e. the warning model plus warning dissemination procedures, communication and education tools, strategies for community involvement and emergency plans. Each component of a LEWS is related to a number of actors involved with their deployment, operational activities and management. For instance, communication and education, community involvement and emergency plans are all significantly influenced by people's risk perception and by operational aspects system managers need to address in cooperation with scientists.

A new air quality monitoring and early warning system: Air quality assessment and air pollutant concentration prediction.

PubMed

Yang, Zhongshan; Wang, Jian

2017-10-01

Air pollution in many countries is worsening with industrialization and urbanization, resulting in climate change and affecting people's health, thus, making the work of policymakers more difficult. It is therefore both urgent and necessary to establish amore scientific air quality monitoring and early warning system to evaluate the degree of air pollution objectively, and predict pollutant concentrations accurately. However, the integration of air quality assessment and air pollutant concentration prediction to establish an air quality system is not common. In this paper, we propose a new air quality monitoring and early warning system, including an assessment module and forecasting module. In the air quality assessment module, fuzzy comprehensive evaluation is used to determine the main pollutants and evaluate the degree of air pollution more scientifically. In the air pollutant concentration prediction module, a novel hybridization model combining complementary ensemble empirical mode decomposition, a modified cuckoo search and differential evolution algorithm, and an Elman neural network, is proposed to improve the forecasting accuracy of six main air pollutant concentrations. To verify the effectiveness of this system, pollutant data for two cities in China are used. The result of the fuzzy comprehensive evaluation shows that the major air pollutants in Xi'an and Jinan are PM 10 and PM 2.5 respectively, and that the air quality of Xi'an is better than that of Jinan. The forecasting results indicate that the proposed hybrid model is remarkably superior to all benchmark models on account of its higher prediction accuracy and stability. Copyright © 2017 Elsevier Inc. All rights reserved.

Tsunami Detection by High-Frequency Radar Beyond the Continental Shelf

NASA Astrophysics Data System (ADS)

Grilli, Stéphan T.; Grosdidier, Samuel; Guérin, Charles-Antoine

2016-12-01

Where coastal tsunami hazard is governed by near-field sources, such as submarine mass failures or meteo-tsunamis, tsunami propagation times may be too small for a detection based on deep or shallow water buoys. To offer sufficient warning time, it has been proposed to implement early warning systems relying on high-frequency (HF) radar remote sensing, that can provide a dense spatial coverage as far offshore as 200-300 km (e.g., for Diginext Ltd.'s Stradivarius radar). Shore-based HF radars have been used to measure nearshore currents (e.g., CODAR SeaSonde® system; http://www.codar.com/), by inverting the Doppler spectral shifts, these cause on ocean waves at the Bragg frequency. Both modeling work and an analysis of radar data following the Tohoku 2011 tsunami, have shown that, given proper detection algorithms, such radars could be used to detect tsunami-induced currents and issue a warning. However, long wave physics is such that tsunami currents will only rise above noise and background currents (i.e., be at least 10-15 cm/s), and become detectable, in fairly shallow water which would limit the direct detection of tsunami currents by HF radar to nearshore areas, unless there is a very wide shallow shelf. Here, we use numerical simulations of both HF radar remote sensing and tsunami propagation to develop and validate a new type of tsunami detection algorithm that does not have these limitations. To simulate the radar backscattered signal, we develop a numerical model including second-order effects in both wind waves and radar signal, with the wave angular frequency being modulated by a time-varying surface current, combining tsunami and background currents. In each "radar cell", the model represents wind waves with random phases and amplitudes extracted from a specified (wind speed dependent) energy density frequency spectrum, and includes effects of random environmental noise and background current; phases, noise, and background current are extracted from independent Gaussian distributions. The principle of the new algorithm is to compute correlations of HF radar signals measured/simulated in many pairs of distant "cells" located along the same tsunami wave ray, shifted in time by the tsunami propagation time between these cell locations; both rays and travel time are easily obtained as a function of long wave phase speed and local bathymetry. It is expected that, in the presence of a tsunami current, correlations computed as a function of range and an additional time lag will show a narrow elevated peak near the zero time lag, whereas no pattern in correlation will be observed in the absence of a tsunami current; this is because surface waves and background current are uncorrelated between pair of cells, particularly when time-shifted by the long-wave propagation time. This change in correlation pattern can be used as a threshold for tsunami detection. To validate the algorithm, we first identify key features of tsunami propagation in the Western Mediterranean Basin, where Stradivarius is deployed, by way of direct numerical simulations with a long wave model. Then, for the purpose of validating the algorithm we only model HF radar detection for idealized tsunami wave trains and bathymetry, but verify that such idealized case studies capture well the salient tsunami wave physics. Results show that, in the presence of strong background currents, the proposed method still allows detecting a tsunami with currents as low as 0.05 m/s, whereas a standard direct inversion based on radar signal Doppler spectra fails to reproduce tsunami currents weaker than 0.15-0.2 m/s. Hence, the new algorithm allows detecting tsunami arrival in deeper water, beyond the shelf and further away from the coast, and providing an early warning. Because the standard detection of tsunami currents works well at short range, we envision that, in a field situation, the new algorithm could complement the standard approach of direct near-field detection by providing a warning that a tsunami is approaching, at larger range and in greater depth. This warning would then be confirmed at shorter range by a direct inversion of tsunami currents, from which the magnitude of the tsunami would also estimated. Hence, both algorithms would be complementary. In future work, the algorithm will be applied to actual tsunami case studies performed using a state-of-the-art long wave model, such as briefly presented here in the Mediterranean Basin.

Pre-Launch Algorithms and Risk Reduction in Support of the Geostationary Lightning Mapper for GOES-R and Beyond

NASA Technical Reports Server (NTRS)

Goodman, Steven; Blakeslee, Richard; Koshak, William

2008-01-01

The Geostationary Lightning Mapper (GLM) is a single channel, near-IR optical transient event detector, used to detect, locate and measure total lightning activity over the full-disk as part of a 3-axis stabilized, geostationary weather satellite system. The next generation NOAA Geostationary Operational Environmental Satellite (GOES-R) series with a planned launch in 2014 will carry a GLM that will provide continuous day and night observations of lightning from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fully operational. The mission objectives for the GLM are to 1) provide continuous,full-disk lightning measurements for storm warning and Nowcasting, 2) provide early warning of tornado activity, and 3) accumulate a long-term database to track decadal changes of lightning. The GLM owes its heritage to the NASA Lightning Imaging Sensor (1997-Present) and the Optical Transient Detector (1995-2000), which were developed for the Earth Observing System and have produced a combined 13 year data record of global lightning activity. Instrument formulation studies were completed in March 2007 and the implementation phase to develop a prototype model and up to four flight units is expected to begin in latter part of the year. In parallel with the instrument development, a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2B algorithms and applications. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional test beds (e.g., Lightning Mapping Arrays in North Alabama and the Washington DC Metropolitan area) are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. Real time lightning mapping data provided to selected National Weather Service forecast offices in Southern and Eastern Region are also improving our understanding of the application of these data in the severe storm warning process and help to accelerate the development of the pre-launch algorithms and Nowcasting applications.

Pre-Launch Algorithms and Risk Reduction in Support of the Geostationary Lightning Mapper for GOES-R and Beyond

NASA Technical Reports Server (NTRS)

Goodman, Steven; Blakeslee, Richard; Koshak, William; Petersen, Walt; Buechler, Dennis; Krehbiel, Paul; Gatlin, Patrick; Zubrick, Steven

2008-01-01

The Geostationary Lightning Mapper (GLM) is a single channel, near-IR optical transient event detector, used to detect, locate and measure total lightning activity over the full-disk as part of a 3-axis stabilized, geostationary weather satellite system. The next generation NOAA Geostationary Operational Environmental Satellite (GOES-R) series with a planned launch in 2014 will carry a GLM that will provide continuous day and night observations of lightning from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fully operational.The mission objectives for the GLM are to 1) provide continuous,full-disk lightning measurements for storm warning and Nowcasting, 2) provide early warning of tornadic activity, and 3) accumulate a long-term database to track decadal changes of lightning. The GLM owes its heritage to the NASA Lightning Imaging Sensor (1997-Present) and the Optical Transient Detector (1995-2000), which were developed for the Earth Observing System and have produced a combined 13 year data record of global lightning activity. Instrument formulation studies were completed in March 2007 and the implementation phase to develop a prototype model and up to four flight units is expected to begin in latter part of the year. In parallel with the instrument development, a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2B algorithms and applications. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) sate]lite and regional test beds (e.g., Lightning Mapping Arrays in North Alabama and the Washington DC Metropolitan area) are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. Real time lightning mapping data provided to selected National Weather Service forecast offices in Southern and Eastern Region are also improving our understanding of the application of these data in the severe storm warning process and help to accelerate the development of the pre-launch algorithms and Nowcasting applications. Abstract for the 3 rd Conference on Meteorological

Accuracy of a pediatric early warning score in the recognition of clinical deterioration.

PubMed

Miranda, Juliana de Oliveira Freitas; Camargo, Climene Laura de; Nascimento, Carlito Lopes; Portela, Daniel Sales; Monaghan, Alan

2017-07-10

to evaluate the accuracy of the version of the Brighton Pediatric Early Warning Score translated and adapted for the Brazilian context, in the recognition of clinical deterioration. a diagnostic test study to measure the accuracy of the Brighton Pediatric Early Warning Score for the Brazilian context, in relation to a reference standard. The sample consisted of 271 children, aged 0 to 10 years, blindly evaluated by a nurse and a physician, specialists in pediatrics, with interval of 5 to 10 minutes between the evaluations, for the application of the Brighton Pediatric Early Warning Score for the Brazilian context and of the reference standard. The data were processed and analyzed using the Statistical Package for the Social Sciences and VassarStats.net programs. The performance of the Brighton Pediatric Early Warning Score for the Brazilian context was evaluated through the indicators of sensitivity, specificity, predictive values, area under the ROC curve, likelihood ratios and post-test probability. the Brighton Pediatric Early Warning Score for the Brazilian context showed sensitivity of 73.9%, specificity of 95.5%, positive predictive value of 73.3%, negative predictive value of 94.7%, area under Receiver Operating Characteristic Curve of 91.9% and the positive post-test probability was 80%. the Brighton Pediatric Early Warning Score for the Brazilian context, presented good performance, considered valid for the recognition of clinical deterioration warning signs of the children studied. avaliar a acurácia da versão traduzida e adaptada do Brighton Paediatric Early Warning Score para o contexto brasileiro, no reconhecimento da deterioração clínica. estudo de teste diagnóstico para medir a acurácia do Brighton Paediatric Early Warning Score, para o contexto brasileiro, em relação a um padrão de referência. A amostra foi composta por 271 crianças de 0 a 10 anos, avaliadas de forma cega por uma enfermeira e um médico, especialistas em pediatria, com intervalo de 5 a 10 minutos entre as avaliações, para aplicação do Brighton Paediatric Early Warning Score, para o contexto brasileiro e do padrão de referência. Os dados foram processados e analisados nos programas Statistical Package for the Social Sciences e VassarStats.net. O desempenho do Brighton Paediatric Early Warning Score para o contexto brasileiro foi avaliado por meio dos indicadores de sensibilidade, especificidade, valores preditivos, área sob a curva ROC, razões de probabilidades e probabilidade pós-teste. o Brighton Paediatric Early Warning Score para o contexto brasileiro apresentou sensibilidade de 73,9%, especificidade de 95,5%, valor preditivo positivo de 73,3%, valor preditivo negativo de 94,7%, área sob a Receiver Operating Characteristic Curve de 91,9% e a probabilidade pós-teste positivo foi de 80%. o Brighton Paediatric Early Warning Score, para o contexto brasileiro, apresentou bom desempenho, considerado válido para o reconhecimento de sinais de alerta de deterioração clínica das crianças estudadas. evaluar la precisión de la versión traducida y adaptada del Brighton Paediatric Early Warning Score para el contexto brasileño, en el reconocimiento de la deterioración clínica. estudio de test diagnóstico para medir la precisión del Brighton Paediatric Early Warning Score para el contexto brasileño, en relación a un estándar de referencia. La muestra estuvo compuesta por 271 niños de 0 a 10 años, evaluadas de forma ciega por especialistas en pediatría, una enfermera y un médico, con intervalo de 5 a 10 minutos entre las evaluaciones, para aplicación del Brighton Paediatric Early Warning Score para el contexto brasileño. Los datos fueron procesados y analizados en los programas Statistical Package for the Social Sciences y VassarStats.net. El desempeño del Brighton Paediatric Early Warning Score para el contexto brasileño fue evaluado por medio de los indicadores de sensibilidad, especificidad, valores predictivos, área debajo de la curva ROC, razones de probabilidades y probabilidad postest. el Brighton Paediatric Early Warning Score para el contexto brasileño presentó sensibilidad de 73,9%, especificidad de 95,5%, valor predictivo positivo de 73,3%, valor predictivo negativo de 94,7%, área bajo la Receiver Operating Characteristic Curve de 91,9% y la probabilidad postest positivo fue de 80%. el Brighton Paediatric Early Warning Score para el contexto brasileño, presentó buen desempeño, considerado válido para el reconocimiento de señales de alerta de deterioración clínica de los niños estudiados.

A national survey of obstetric early warning systems in the United Kingdom: five years on.

PubMed

Isaacs, R A; Wee, M Y K; Bick, D E; Beake, S; Sheppard, Z A; Thomas, S; Hundley, V; Smith, G B; van Teijlingen, E; Thomas, P W

2014-07-01

The Confidential Enquiries into Maternal Deaths in the UK have recommended obstetric early warning systems for early identification of clinical deterioration to reduce maternal morbidity and mortality. This survey explored early warning systems currently used by maternity units in the UK. An electronic questionnaire was sent to all 205 lead obstetric anaesthetists under the auspices of the Obstetric Anaesthetists' Association, generating 130 (63%) responses. All respondents reported use of an obstetric early warning system, compared with 19% in a similar survey in 2007. Respondents agreed that the six most important physiological parameters to record were respiratory rate, heart rate, temperature, systolic and diastolic blood pressure and oxygen saturation. One hundred and eighteen (91%) lead anaesthetists agreed that early warning systems helped to prevent obstetric morbidity. Staffing pressures were perceived as the greatest barrier to their use, and improved audit, education and training for healthcare professionals were identified as priority areas. © 2014 The Association of Anaesthetists of Great Britain and Ireland.

Floods in 2002 and 2013: comparing flood warnings and emergency measures from the perspective of affected parties

NASA Astrophysics Data System (ADS)

Kreibich, Heidi; Pech, Ina; Schröter, Kai; Müller, Meike; Thieken, Annegret

2016-04-01

Early warning is essential for protecting people and mitigating damage in case of flood events. However, early warning is only helpful if the flood-endangered parties are reached by the warning and if they know how to react effectively. Finding suitable methods for communicating helpful warnings to the "last mile" remains a challenge, but not much information is available. Surveys were undertaken after the August 2002 and the June 2013 floods in Germany, asking affected private households and companies about warnings they received and emergency measures they undertook. Results show, that in 2002 early warning did not work well: in too many areas warnings came too late or were too imprecise and many people (27%) and companies (45%) did not receive a flood warning. Afterwards, the warning systems were significantly improved, so that in 2013 only a small share of the affected people (7%) and companies (7 %) was not reached by any warning. Additionally, private households and companies were hardly aware of the flood risk in the Elbe catchment before 2002, mainly due to a lack of flood experience. For instance, in 2002 only 14% of private households clearly knew how to protect themselves and their assets when the warning reached them, in 2013 this fraction was 46 %. Although the share of companies which had an emergency plan in place had increased from 10 % in 2002 to 26 % in 2013, and the share of those conducting regular emergency exercises had increased from 4 % to 13 %, there is still plenty of room for improvement. Therefore, integrated early warning systems from monitoring through to the reaction of the affected parties as well as effective risk and emergency communication need continuous further improvement to protect people and mitigate residual risks in case of floods.

Neonatal Early Warning Tools for recognising and responding to clinical deterioration in neonates cared for in the maternity setting: A retrospective case-control study.

PubMed

Paliwoda, Michelle; New, Karen; Bogossian, Fiona

2016-09-01

All newborns are at risk of deterioration as a result of failing to make the transition to extra uterine life. Signs of deterioration can be subtle and easily missed. It has been postulated that the use of an Early Warning Tool may assist clinicians in recognising and responding to signs of deterioration earlier in neonates, thereby preventing a serious adverse event. To examine whether observations from a Standard Observation Tool, applied to three neonatal Early Warning Tools, would hypothetically trigger an escalation of care more frequently than actual escalation of care using the Standard Observation Tool. A retrospective case-control study. A maternity unit in a tertiary public hospital in Australia. Neonates born in 2013 of greater than or equal to 34(+0) weeks gestation, admitted directly to the maternity ward from their birthing location and whose subsequent deterioration required admission to the neonatal unit, were identified as cases from databases of the study hospital. Each case was matched with three controls, inborn during the same period and who did not experience deterioration and neonatal unit admission. Clinical and physiological data recorded on a Standard Observation Tool, from time of admission to the maternity ward, for cases and controls were charted onto each of three Early Warning Tools. The primary outcome was whether the tool 'triggered an escalation of care'. Descriptive statistics (n, %, Mean and SD) were employed. Cases (n=26) comprised late preterm, early term and post-term neonates and matched by gestational age group with 3 controls (n=78). Overall, the Standard Observation Tool triggered an escalation of care for 92.3% of cases compared to the Early Warning Tools; New South Wales Health 80.8%, United Kingdom Newborn Early Warning Chart 57.7% and The Australian Capital Territory Neonatal Early Warning Score 11.5%. Subgroup analysis by gestational age found differences between the tools in hypothetically triggering an escalation of care. The Standard Observation Tool triggered an escalation of care more frequently than the Early Warning Tools, which may be as a result of behavioural data captured on the Standard Observation Tool and escalated, which could not be on the Early Warning Tools. Findings demonstrate that a single tool applied to all gestational age ranges may not be effective in identifying early deterioration or may over trigger an escalation of care. Further research is required into the sensitivity and specificity of Early Warning Tools in neonatal sub-populations. Copyright © 2016 Elsevier Ltd. All rights reserved.

Early warning signals of regime shifts in coupled human–environment systems

PubMed Central

Bauch, Chris T.; Sigdel, Ram; Pharaon, Joe; Anand, Madhur

2016-01-01

In complex systems, a critical transition is a shift in a system’s dynamical regime from its current state to a strongly contrasting state as external conditions move beyond a tipping point. These transitions are often preceded by characteristic early warning signals such as increased system variability. However, early warning signals in complex, coupled human–environment systems (HESs) remain little studied. Here, we compare critical transitions and their early warning signals in a coupled HES model to an equivalent environment model uncoupled from the human system. We parameterize the HES model, using social and ecological data from old-growth forests in Oregon. We find that the coupled HES exhibits a richer variety of dynamics and regime shifts than the uncoupled environment system. Moreover, the early warning signals in the coupled HES can be ambiguous, heralding either an era of ecosystem conservationism or collapse of both forest ecosystems and conservationism. The presence of human feedback in the coupled HES can also mitigate the early warning signal, making it more difficult to detect the oncoming regime shift. We furthermore show how the coupled HES can be “doomed to criticality”: Strategic human interactions cause the system to remain perpetually in the vicinity of a collapse threshold, as humans become complacent when the resource seems protected but respond rapidly when it is under immediate threat. We conclude that the opportunities, benefits, and challenges of modeling regime shifts and early warning signals in coupled HESs merit further research. PMID:27815533

Adapting ElarmS Earthquake Early Warnings for Cascadia: Development and Testing of ShakeAlerts in the Pacific Northwest

NASA Astrophysics Data System (ADS)

Hartog, J. R.; Kress, V. C.; Thomas, T.; Malone, S. D.; Henson, I. H.; Neuhauser, D. S.

2013-12-01

As a first step in establishing an earthquake early warning system in Cascadia, we have installed the ElarmS component of the ShakeAlert system at the Pacific Northwest Seismic Network. In Cascadia our initial focus is primarily on the development of a seismo-geodetic-based real-time finite fault rupture algorithm to detect and characterize a large plate-boundary rupture in progress (see Crowell et. al., this session). In this regard the goal of the purely seismic-data-based ElarmS implementation is to 'trigger' the finite fault rupture algorithm. At the same time, however, the Cascadian ElarmS will also produce warnings for smaller onshore crustal earthquakes. While warnings from these smaller and closer earthquakes will provide shorter warning times for communities, and for less dramatic earthquakes, we intend to use them for educational purposes, and to coordinate with our regional and collaborating partners. They will also help to guide us to shorten data latencies and learn where additional instrumentation is most needed to increase performance. The accuracy of ElarmS in Cascadia is another major concern, because the current ElarmS model presumes an initial focal depth for earthquakes of 8 km based on California experience, while in Cascadia earthquakes of major concern may be as deep as 50 km, and/or occur beyond the western fringe of the seismic network. To this purpose our testing protocol is aimed at determining what changes are required to ensure top performance of an ElarmS-based warning system in Cascadia. Because of Cascadia's relatively low seismicity rate, and the paucity of data from plate boundary earthquakes there of any size, we have prioritized the development of a test system. The test system permits us to: 1) replay segments of actual seismic waveform data recorded from the PNSN and contributing seismic network stations to represent both earthquakes and noise conditions, and 2) broadcast synthetic data into the system to simulate signals we anticipate from earthquakes for which we have no actual ground motion recordings. The test system lets us also simulate various error conditions (latent and/or out-of-sequence data, telemetry drop-outs, etc.) to explore how to protect the system from them. We have also been testing the ElarmS system on real-time seismic network data for about 6 months as of the time of writing of this abstract. Using 268 channels of streaming strong motion and broad-band data, the system has produced very few false alarms and generally performed well for earthquakes between about magnitudes 2.5 and 4.5. Warning times are shorter (and the 'blind zone' smaller) in parts of the network where station density is higher and/or telemetry more fleet. One significant problem we find is that the discriminant used in northern California to differentiate local earthquake signals from teleseisms often fails in Cascadia. We are working to produce a valid teleseism detector.

Web-based Tsunami Early Warning System with instant Tsunami Propagation Calculations in the GPU Cloud

NASA Astrophysics Data System (ADS)

Hammitzsch, M.; Spazier, J.; Reißland, S.

2014-12-01

Usually, tsunami early warning and mitigation systems (TWS or TEWS) are based on several software components deployed in a client-server based infrastructure. The vast majority of systems importantly include desktop-based clients with a graphical user interface (GUI) for the operators in early warning centers. However, in times of cloud computing and ubiquitous computing the use of concepts and paradigms, introduced by continuously evolving approaches in information and communications technology (ICT), have to be considered even for early warning systems (EWS). Based on the experiences and the knowledge gained in three research projects - 'German Indonesian Tsunami Early Warning System' (GITEWS), 'Distant Early Warning System' (DEWS), and 'Collaborative, Complex, and Critical Decision-Support in Evolving Crises' (TRIDEC) - new technologies are exploited to implement a cloud-based and web-based prototype to open up new prospects for EWS. This prototype, named 'TRIDEC Cloud', merges several complementary external and in-house cloud-based services into one platform for automated background computation with graphics processing units (GPU), for web-mapping of hazard specific geospatial data, and for serving relevant functionality to handle, share, and communicate threat specific information in a collaborative and distributed environment. The prototype in its current version addresses tsunami early warning and mitigation. The integration of GPU accelerated tsunami simulation computations have been an integral part of this prototype to foster early warning with on-demand tsunami predictions based on actual source parameters. However, the platform is meant for researchers around the world to make use of the cloud-based GPU computation to analyze other types of geohazards and natural hazards and react upon the computed situation picture with a web-based GUI in a web browser at remote sites. The current website is an early alpha version for demonstration purposes to give the concept a whirl and to shape science's future. Further functionality, improvements and possible profound changes have to implemented successively based on the users' evolving needs.

Massachusetts Early Warning Indicator System (EWIS). "Technical Descriptions of Risk Model Development": Middle and High School Age Groupings (Grades 7-12)

ERIC Educational Resources Information Center

Massachusetts Department of Elementary and Secondary Education, 2013

2013-01-01

The Massachusetts Department of Elementary and Secondary Education (Department) created the grades 1-12 Early Warning Indicator System (EWIS) in response to district interest in the Early Warning Indicator Index (EWII) that the Department previously created for rising grade 9 students. Districts shared that the EWII data were helpful, but also…

Infrared dim-small target tracking via singular value decomposition and improved Kernelized correlation filter

NASA Astrophysics Data System (ADS)

Qian, Kun; Zhou, Huixin; Rong, Shenghui; Wang, Bingjian; Cheng, Kuanhong

2017-05-01

Infrared small target tracking plays an important role in applications including military reconnaissance, early warning and terminal guidance. In this paper, an effective algorithm based on the Singular Value Decomposition (SVD) and the improved Kernelized Correlation Filter (KCF) is presented for infrared small target tracking. Firstly, the super performance of the SVD-based algorithm is that it takes advantage of the target's global information and obtains a background estimation of an infrared image. A dim target is enhanced by subtracting the corresponding estimated background with update from the original image. Secondly, the KCF algorithm is combined with Gaussian Curvature Filter (GCF) to eliminate the excursion problem. The GCF technology is adopted to preserve the edge and eliminate the noise of the base sample in the KCF algorithm, helping to calculate the classifier parameter for a small target. At last, the target position is estimated with a response map, which is obtained via the kernelized classifier. Experimental results demonstrate that the presented algorithm performs favorably in terms of efficiency and accuracy, compared with several state-of-the-art algorithms.

Firefly Algorithm in detection of TEC seismo-ionospheric anomalies

NASA Astrophysics Data System (ADS)

Akhoondzadeh, Mehdi

2015-07-01

Anomaly detection in time series of different earthquake precursors is an essential introduction to create an early warning system with an allowable uncertainty. Since these time series are more often non linear, complex and massive, therefore the applied predictor method should be able to detect the discord patterns from a large data in a short time. This study acknowledges Firefly Algorithm (FA) as a simple and robust predictor to detect the TEC (Total Electron Content) seismo-ionospheric anomalies around the time of the some powerful earthquakes including Chile (27 February 2010), Varzeghan (11 August 2012) and Saravan (16 April 2013). Outstanding anomalies were observed 7 and 5 days before the Chile and Varzeghan earthquakes, respectively and also 3 and 8 days prior to the Saravan earthquake.

Prediction of Flood Warning in Taiwan Using Nonlinear SVM with Simulated Annealing Algorithm

NASA Astrophysics Data System (ADS)

Lee, C.

2013-12-01

The issue of the floods is important in Taiwan. It is because the narrow and high topography of the island make lots of rivers steep in Taiwan. The tropical depression likes typhoon always causes rivers to flood. Prediction of river flow under the extreme rainfall circumstances is important for government to announce the warning of flood. Every time typhoon passed through Taiwan, there were always floods along some rivers. The warning is classified to three levels according to the warning water levels in Taiwan. The propose of this study is to predict the level of floods warning from the information of precipitation, rainfall duration and slope of riverbed. To classify the level of floods warning by the above-mentioned information and modeling the problems, a machine learning model, nonlinear Support vector machine (SVM), is formulated to classify the level of floods warning. In addition, simulated annealing (SA), a probabilistic heuristic algorithm, is used to determine the optimal parameter of the SVM model. A case study of flooding-trend rivers of different gradients in Taiwan is conducted. The contribution of this SVM model with simulated annealing is capable of making efficient announcement for flood warning and keeping the danger of flood from residents along the rivers.

Non-invasive, home-based electroencephalography hypoglycaemia warning system for personal monitoring using skin surface electrodes: a single-case feasibility study.

PubMed

Clewett, Christopher J; Langley, Phillip; Bateson, Anthony D; Asghar, Aziz; Wilkinson, Antony J

2016-03-01

Hypoglycaemia unawareness is a common condition associated with increased risk of severe hypoglycaemia. The purpose of the authors' study was to develop a simple to use, home-based and non-invasive hypoglycaemia warning system based on electroencephalography (EEG), and to demonstrate its use in a single-case feasibility study. A participant with type 1 diabetes forms a single-person case study where blood sugar levels and EEG were recorded. EEG was recorded using skin surface electrodes placed behind the ear located within the T3 region by the participant in the home. EEG was analysed retrospectively to develop an algorithm which would trigger a warning if EEG changes associated with hypoglycaemia onset were detected. All hypoglycaemia events were detected by the EEG hypoglycaemia warning algorithm. Warnings were triggered with blood glucose concentration levels at or below 4.2 mmol/l in this participant and no warnings were issued when in euglycaemia. The feasibility of a non-invasive EEG-based hypoglycaemia warning system for personal monitoring in the home has been demonstrated in a single case study. The results suggest that further studies are warranted to evaluate the system prospectively in a larger group of participants.

CyberShake-derived ground-motion prediction models for the Los Angeles region with application to earthquake early warning

USGS Publications Warehouse

Bose, Maren; Graves, Robert; Gill, David; Callaghan, Scott; Maechling, Phillip J.

2014-01-01

Real-time applications such as earthquake early warning (EEW) typically use empirical ground-motion prediction equations (GMPEs) along with event magnitude and source-to-site distances to estimate expected shaking levels. In this simplified approach, effects due to finite-fault geometry, directivity and site and basin response are often generalized, which may lead to a significant under- or overestimation of shaking from large earthquakes (M > 6.5) in some locations. For enhanced site-specific ground-motion predictions considering 3-D wave-propagation effects, we develop support vector regression (SVR) models from the SCEC CyberShake low-frequency (<0.5 Hz) and broad-band (0–10 Hz) data sets. CyberShake encompasses 3-D wave-propagation simulations of >415 000 finite-fault rupture scenarios (6.5 ≤ M ≤ 8.5) for southern California defined in UCERF 2.0. We use CyberShake to demonstrate the application of synthetic waveform data to EEW as a ‘proof of concept’, being aware that these simulations are not yet fully validated and might not appropriately sample the range of rupture uncertainty. Our regression models predict the maximum and the temporal evolution of instrumental intensity (MMI) at 71 selected test sites using only the hypocentre, magnitude and rupture ratio, which characterizes uni- and bilateral rupture propagation. Our regression approach is completely data-driven (where here the CyberShake simulations are considered data) and does not enforce pre-defined functional forms or dependencies among input parameters. The models were established from a subset (∼20 per cent) of CyberShake simulations, but can explain MMI values of all >400 k rupture scenarios with a standard deviation of about 0.4 intensity units. We apply our models to determine threshold magnitudes (and warning times) for various active faults in southern California that earthquakes need to exceed to cause at least ‘moderate’, ‘strong’ or ‘very strong’ shaking in the Los Angeles (LA) basin. These thresholds are used to construct a simple and robust EEW algorithm: to declare a warning, the algorithm only needs to locate the earthquake and to verify that the corresponding magnitude threshold is exceeded. The models predict that a relatively moderate M6.5–7 earthquake along the Palos Verdes, Newport-Inglewood/Rose Canyon, Elsinore or San Jacinto faults with a rupture propagating towards LA could cause ‘very strong’ to ‘severe’ shaking in the LA basin; however, warning times for these events could exceed 30 s.

Early Warning Signals of Ecological Transitions: Methods for Spatial Patterns

PubMed Central

Brock, William A.; Carpenter, Stephen R.; Ellison, Aaron M.; Livina, Valerie N.; Seekell, David A.; Scheffer, Marten; van Nes, Egbert H.; Dakos, Vasilis

2014-01-01

A number of ecosystems can exhibit abrupt shifts between alternative stable states. Because of their important ecological and economic consequences, recent research has focused on devising early warning signals for anticipating such abrupt ecological transitions. In particular, theoretical studies show that changes in spatial characteristics of the system could provide early warnings of approaching transitions. However, the empirical validation of these indicators lag behind their theoretical developments. Here, we summarize a range of currently available spatial early warning signals, suggest potential null models to interpret their trends, and apply them to three simulated spatial data sets of systems undergoing an abrupt transition. In addition to providing a step-by-step methodology for applying these signals to spatial data sets, we propose a statistical toolbox that may be used to help detect approaching transitions in a wide range of spatial data. We hope that our methodology together with the computer codes will stimulate the application and testing of spatial early warning signals on real spatial data. PMID:24658137

The role of integrating natural and social science concepts for risk governance and the design of people-centred early warning systems. Case study from the German-Indonesian Tsunami Early Warning System Project (GITEWS)

NASA Astrophysics Data System (ADS)

Gebert, Niklas; Post, Joachim

2010-05-01

The development of early warning systems are one of the key domains of adaptation to global environmental change and contribute very much to the development of societal reaction and adaptive capacities to deal with extreme events. Especially, Indonesia is highly exposed to tsunami. In average every three years small and medium size tsunamis occur in the region causing damage and death. In the aftermath of the Indian Ocean Tsunami 2004, the German and Indonesian government agreed on a joint cooperation to develop a People Centered End-to-End Early Warning System (GITEWS). The analysis of risk and vulnerability, as an important step in risk (and early warning) governance, is a precondition for the design of effective early warning structures by delivering the knowledge base for developing institutionalized quick response mechanisms of organizations involved in the issuing of a tsunami warning, and of populations exposed to react to warnings and to manage evacuation before the first tsunami wave hits. Thus, a special challenge for developing countries is the governance of complex cross-sectoral and cross-scale institutional, social and spatial processes and requirements for the conceptualization, implementation and optimization of a people centered tsunami early warning system. In support of this, the risk and vulnerability assessment of the case study aims at identifying those factors that constitute the causal structure of the (dis)functionality between the technological warning and the social response system causing loss of life during an emergency situation: Which social groups are likely to be less able to receive and respond to an early warning alert? And, are people able to evacuate in due time? Here, only an interdisciplinary research approach is capable to analyze the socio-spatial and environmental conditions of vulnerability and risk and to produce valuable results for decision makers and civil society to manage tsunami risk in the early warning context. This requires the integration of natural / spatial and social science concepts, methods and data: E.g. a scenario based approach for tsunami inundation modeling was developed to provide decision makers with options to decide up to what level they aim to protect their people and territory, on the contrary household surveys were conducted for the spatial analysis of the evacuation preparedness of the population as a function of place specific hazard, risk, warning and evacuation perception; remote sensing was applied for the spatial analysis (land-use) of the socio-physical conditions of a city and region for evacuation; and existing social / population statistics were combined with land-use data for the precise spatial mapping of the population exposed to tsunami risks. Only by utilizing such a comprehensive assessment approach valuable information for risk governance can be generated. The results are mapped using GIS and designed according to the specific needs of different end-users, such as public authorities involved in the design of warning dissemination strategies, land-use planners (shelter planning, road network configuration) and NGOs mandated to provide education for the general public on tsunami risk and evacuation behavior. The case study of the city of Padang (one of the pilot areas of GITEWS), Indonesia clearly show, that only by intersecting social (vulnerability) and natural hazards research a comprehensive picture on tsunami risk can be provided with which risk governance in the early warning context can be conducted in a comprehensive, systemic and sustainable manner.

Study on the early warning mechanism for the security of blast furnace hearths

NASA Astrophysics Data System (ADS)

Zhao, Hong-bo; Huo, Shou-feng; Cheng, Shu-sen

2013-04-01

The campaign life of blast furnace (BF) hearths has become the limiting factor for safety and high efficiency production of modern BFs. However, the early warning mechanism of hearth security has not been clear. In this article, based on heat transfer calculations, heat flux and erosion monitoring, the features of heat flux and erosion were analyzed and compared among different types of hearths. The primary detecting elements, mathematical models, evaluating standards, and warning methods were discussed. A novel early warning mechanism with the three-level quantificational standards was proposed for BF hearth security.

Research on early-warning index of the spatial temperature field in concrete dams.

PubMed

Yang, Guang; Gu, Chongshi; Bao, Tengfei; Cui, Zhenming; Kan, Kan

2016-01-01

Warning indicators of the dam body's temperature are required for the real-time monitoring of the service conditions of concrete dams to ensure safety and normal operations. Warnings theories are traditionally targeted at a single point which have limitations, and the scientific warning theories on global behavior of the temperature field are non-existent. In this paper, first, in 3D space, the behavior of temperature field has regional dissimilarity. Through the Ward spatial clustering method, the temperature field was divided into regions. Second, the degree of order and degree of disorder of the temperature monitoring points were defined by the probability method. Third, the weight values of monitoring points of each regions were explored via projection pursuit. Forth, a temperature entropy expression that can describe degree of order of the spatial temperature field in concrete dams was established. Fifth, the early-warning index of temperature entropy was set up according to the calculated sequential value of temperature entropy. Finally, project cases verified the feasibility of the proposed theories. The early-warning index of temperature entropy is conducive to the improvement of early-warning ability and safety management levels during the operation of high concrete dams.

Building Better Drought Resilience Through Improved Monitoring and Early Warning: Learning From Stakeholders in Europe, the USA, and Australia

NASA Astrophysics Data System (ADS)

Stahl, K.; Hannaford, J.; Bachmair, S.; Tijdeman, E.; Collins, K.; Svoboda, M.; Knutson, C. L.; Wall, N.; Smith, K. H.; Bernadt, T.; Crossman, N. D.; Overton, I. C.; Barker, L. J.; Acreman, M. C.

2016-12-01

With climate projections suggesting that droughts will intensify in many regions in future, improved drought risk management may reduce potential threats to freshwater security across the globe. One aspect that has been called for in this respect is an improvement of the linkage of drought monitoring and early warning, which currently focuses largely on indicators from meteorology and hydrology, to drought impacts on environment and society. However, a survey of existing monitoring and early warning systems globally, that we report on in this contribution, demonstrates that although impacts are being monitored, there is limited work, and certainly little consensus, on how to best achieve this linkage. The Belmont Forum project DrIVER (Drought impacts: Vulnerability thresholds in monitoring and early-warning research) carried out a number of stakeholder workshops in North America, Europe and Australia to elaborate on options for such improvements. A first round of workshops explored current drought management practices among a very diverse range of stakeholders, and their expectations from monitoring and early warning systems (particularly regarding impact characterization). The workshops revealed some disconnects between the indices used in the public early warning systems and those used by local decision-makers, e.g. to trigger drought measures. Follow-up workshops then explored how the links between information at these different scales can be bridged and applied. Impact information plays a key role in this task. This contribution draws on the lessons learned from the transdisciplinary interactions in DrIVER, to enhance the usability of drought monitoring and early-warning systems and other risk management strategies.

Impact of social preparedness on flood early warning systems

NASA Astrophysics Data System (ADS)

Girons Lopez, M.; Di Baldassarre, G.; Seibert, J.

2017-01-01

Flood early warning systems play a major role in the disaster risk reduction paradigm as cost-effective methods to mitigate flood disaster damage. The connections and feedbacks between the hydrological and social spheres of early warning systems are increasingly being considered as key aspects for successful flood mitigation. The behavior of the public and first responders during flood situations, determined by their preparedness, is heavily influenced by many behavioral traits such as perceived benefits, risk awareness, or even denial. In this study, we use the recency of flood experiences as a proxy for social preparedness to assess its impact on the efficiency of flood early warning systems through a simple stylized model and implemented this model using a simple mathematical description. The main findings, which are based on synthetic data, point to the importance of social preparedness for flood loss mitigation, especially in circumstances where the technical forecasting and warning capabilities are limited. Furthermore, we found that efforts to promote and preserve social preparedness may help to reduce disaster-induced losses by almost one half. The findings provide important insights into the role of social preparedness that may help guide decision-making in the field of flood early warning systems.

The Financial Benefit of Early Flood Warnings in Europe

NASA Astrophysics Data System (ADS)

Pappenberger, Florian; Cloke, Hannah L.; Wetterhall, Fredrik; Parker, Dennis J.; Richardson, David; Thielen, Jutta

2015-04-01

Effective disaster risk management relies on science based solutions to close the gap between prevention and preparedness measures. The outcome of consultations on the UNIDSR post-2015 framework for disaster risk reduction highlight the need for cross-border early warning systems to strengthen the preparedness phases of disaster risk management in order to save people's lives and property and reduce the overall impact of severe events. In particular, continental and global scale flood forecasting systems provide vital information to various decision makers with which early warnings of floods can be made. Here the potential monetary benefits of early flood warnings using the example of the European Flood Awareness System (EFAS) are calculated based on pan-European Flood damage data and calculations of potential flood damage reductions. The benefits are of the order of 400 Euro for every 1 Euro invested. Because of the uncertainties which accompany the calculation, a large sensitivity analysis is performed in order to develop an envelope of possible financial benefits. Current EFAS system skill is compared against perfect forecasts to demonstrate the importance of further improving the skill of the forecasts. Improving the response to warnings is also essential in reaping the benefits of flood early warnings.

School disengagement as a predictor of dropout, delinquency, and problem substance use during adolescence and early adulthood.

PubMed

Henry, Kimberly L; Knight, Kelly E; Thornberry, Terence P

2012-02-01

Over the past 5 years, a great deal of attention has been paid to the development of early warning systems for dropout prevention. These warning systems use a set of indicators based on official school records to identify youth at risk for dropout and then appropriately target intervention. The current study builds on this work by assessing the extent to which a school disengagement warning index predicts not only dropout but also other problem behaviors during middle adolescence, late adolescence, and early adulthood. Data from the Rochester Youth Development Study (N = 911, 73% male, 68% African American, and 17% Latino) were used to examine the effects of a school disengagement warning index based on official 8th and 9th grade school records on subsequent dropout, as well as serious delinquency, official offending, and problem substance use during middle adolescence, late adolescence, and early adulthood. Results indicate that the school disengagement warning index is robustly related to dropout as well as serious problem behaviors across the three developmental stages, even after controlling for important potential confounders. High school dropout mediates the effect of the warning index on serious problem behaviors in early adulthood.

School Disengagement as a Predictor of Dropout, Delinquency, and Problem Substance Use during Adolescence and Early Adulthood

PubMed Central

Henry, Kimberly L.; Knight, Kelly E.; Thornberry, Terence P.

2015-01-01

Over the past five years, a great deal of attention has been paid to the development of early warning systems for dropout prevention. These warning systems use a set of indicators based on official school records to identify youth at risk for dropout and then appropriately target intervention. The current study builds on this work by assessing the extent to which a school disengagement warning index predicts not only dropout but also other problem behaviors during middle adolescence, late adolescence, and early adulthood. Data from the Rochester Youth Development Study (n=911, 73% male, 68% African American, and 17% Latino) were used to examine the effects of a school disengagement warning index based on official 8th and 9th grade school records on subsequent dropout, as well as serious delinquency, official offending, and problem substance use during middle adolescence, late adolescence, and early adulthood. Results indicate that the school disengagement warning index is robustly related to dropout as well as serious problem behaviors across the three developmental stages, even after controlling for important potential confounders. High school dropout mediates the effect of the warning index on serious problem behaviors in early adulthood. PMID:21523389

[Tourism ecological security early warning of Zhangjiajie, China based on the improved TOPSIS method and the grey GM (1,1)model].

PubMed

Xu, Mei; Liu, Chun la; Li, Dan; Zhong, Xiao Lin

2017-11-01

Tourism ecological security early warning is of great significance both to the coordination of ecological environment protection and tourism industry rapid development in tourism destination, and the sustainable and healthy development of regional social and economy. Firstly, based on the DPSIR model, the tourism ecological security early warning index system of Zhangjiajie was constructed from 5 aspects, which were driving force, pressure, state, impact and response. Then, by using the improved TOPSIS method, the tourism ecological security situation of Zhangjiajie from 2001 to 2014 was analyzed. Lastly, by using the grey GM (1,1) model, the tourism ecological security evolution trend of 2015-2020 was predicted. The results indicated that, on the whole, the close degree of Zhangjiajie's tourism ecological security showed a slightly upward trend during 2001-2014, the warning degree was the moderate warning. In terms of each subsystem, warning degree of the driving force system and the pressure system of Zhangjiajie's tourism ecological secu-rity were on the rise, which evolved from light warning to heavy warning; warning degree of the state system and the impact system had not changed so much, and had been in the moderate warning; warning degree of the response system was on the decline, which changed from huge warning to no warning during 2001-2014. According to the current development trend, the close degree of Zhangjiajie's tourism ecological security would rise further in 2015-2020, and the warning degree would turn from moderate warning into light warning, but the task of coordinating the relationship between tourism development and ecological construction and environmental protection would be still arduous.

Early detection of ecosystem regime shifts: a multiple method evaluation for management application.

PubMed

Lindegren, Martin; Dakos, Vasilis; Gröger, Joachim P; Gårdmark, Anna; Kornilovs, Georgs; Otto, Saskia A; Möllmann, Christian

2012-01-01

Critical transitions between alternative stable states have been shown to occur across an array of complex systems. While our ability to identify abrupt regime shifts in natural ecosystems has improved, detection of potential early-warning signals previous to such shifts is still very limited. Using real monitoring data of a key ecosystem component, we here apply multiple early-warning indicators in order to assess their ability to forewarn a major ecosystem regime shift in the Central Baltic Sea. We show that some indicators and methods can result in clear early-warning signals, while other methods may have limited utility in ecosystem-based management as they show no or weak potential for early-warning. We therefore propose a multiple method approach for early detection of ecosystem regime shifts in monitoring data that may be useful in informing timely management actions in the face of ecosystem change.

Early Detection of Ecosystem Regime Shifts: A Multiple Method Evaluation for Management Application

PubMed Central

Lindegren, Martin; Dakos, Vasilis; Gröger, Joachim P.; Gårdmark, Anna; Kornilovs, Georgs; Otto, Saskia A.; Möllmann, Christian

2012-01-01

Critical transitions between alternative stable states have been shown to occur across an array of complex systems. While our ability to identify abrupt regime shifts in natural ecosystems has improved, detection of potential early-warning signals previous to such shifts is still very limited. Using real monitoring data of a key ecosystem component, we here apply multiple early-warning indicators in order to assess their ability to forewarn a major ecosystem regime shift in the Central Baltic Sea. We show that some indicators and methods can result in clear early-warning signals, while other methods may have limited utility in ecosystem-based management as they show no or weak potential for early-warning. We therefore propose a multiple method approach for early detection of ecosystem regime shifts in monitoring data that may be useful in informing timely management actions in the face of ecosystem change. PMID:22808007

How do I know if I’ve improved my continental scale flood early warning system?

NASA Astrophysics Data System (ADS)

Cloke, Hannah L.; Pappenberger, Florian; Smith, Paul J.; Wetterhall, Fredrik

2017-04-01

Flood early warning systems mitigate damages and loss of life and are an economically efficient way of enhancing disaster resilience. The use of continental scale flood early warning systems is rapidly growing. The European Flood Awareness System (EFAS) is a pan-European flood early warning system forced by a multi-model ensemble of numerical weather predictions. Responses to scientific and technical changes can be complex in these computationally expensive continental scale systems, and improvements need to be tested by evaluating runs of the whole system. It is demonstrated here that forecast skill is not correlated with the value of warnings. In order to tell if the system has been improved an evaluation strategy is required that considers both forecast skill and warning value. The combination of a multi-forcing ensemble of EFAS flood forecasts is evaluated with a new skill-value strategy. The full multi-forcing ensemble is recommended for operational forecasting, but, there are spatial variations in the optimal forecast combination. Results indicate that optimizing forecasts based on value rather than skill alters the optimal forcing combination and the forecast performance. Also indicated is that model diversity and ensemble size are both important in achieving best overall performance. The use of several evaluation measures that consider both skill and value is strongly recommended when considering improvements to early warning systems.

Constructing early warning information release system in towns enterprise clean production

NASA Astrophysics Data System (ADS)

Yuwen, Huixin; He, Xueqiu; Qian, Xinming; Yuan, Mengqi

2017-08-01

China’s industry boom has not only brought unprecedented prosperity, but also caused the gradual depletion of various resources and the worsening of the natural environment. Experts admit that China is facing serious environmental problem, but they believe that they can seek a new path to overcome it through joint efforts. Early warning information release and clean production are the important concepts in addressing the imminent crisis. Early warning information release system can monitor and forecast the risk that affects the clean production. The author drawn the experiences and lessons from developed countries, combined with China’s reality, put forward countermeasures and suggestions about constructing early warning information release system in process of Chinese town-scaled enterprises clean production.

Early warning signal for interior crises in excitable systems.

PubMed

Karnatak, Rajat; Kantz, Holger; Bialonski, Stephan

2017-10-01

The ability to reliably predict critical transitions in dynamical systems is a long-standing goal of diverse scientific communities. Previous work focused on early warning signals related to local bifurcations (critical slowing down) and nonbifurcation-type transitions. We extend this toolbox and report on a characteristic scaling behavior (critical attractor growth) which is indicative of an impending global bifurcation, an interior crisis in excitable systems. We demonstrate our early warning signal in a conceptual climate model as well as in a model of coupled neurons known to exhibit extreme events. We observed critical attractor growth prior to interior crises of chaotic as well as strange-nonchaotic attractors. These observations promise to extend the classes of transitions that can be predicted via early warning signals.

Landslide susceptibility and early warning model for shallow landslide in Taiwan

NASA Astrophysics Data System (ADS)

Huang, Chun-Ming; Wei, Lun-Wei; Chi, Chun-Chi; Chang, Kan-Tsun; Lee, Chyi-Tyi

2017-04-01

This study aims to development a regional susceptibility model and warning threshold as well as the establishment of early warning system in order to prevent and reduce the losses caused by rainfall-induced shallow landslides in Taiwan. For the purpose of practical application, Taiwan is divided into nearly 185,000 slope units. The susceptibility and warning threshold of each slope unit were analyzed as basic information for disaster prevention. The geological characteristics, mechanism and the occurrence time of landslides were recorded for more than 900 cases through field investigation and interview of residents in order to discuss the relationship between landslides and rainfall. Logistic regression analysis was performed to evaluate the landslide susceptibility and an I3-R24 rainfall threshold model was proposed for the early warning of landslides. The validations of recent landslide cases show that the model was suitable for the warning of regional shallow landslide and most of the cases can be warned 3 to 6 hours in advanced. We also propose a slope unit area weighted method to establish local rainfall threshold on landslide for vulnerable villages in order to improve the practical application. Validations of the local rainfall threshold also show a good agreement to the occurrence time reported by newspapers. Finally, a web based "Rainfall-induced Landslide Early Warning System" is built and connected to real-time radar rainfall data so that landslide real-time warning can be achieved. Keywords: landslide, susceptibility analysis, rainfall threshold

A new type of tri-axial accelerometers with high dynamic range MEMS for earthquake early warning

NASA Astrophysics Data System (ADS)

Peng, Chaoyong; Chen, Yang; Chen, Quansheng; Yang, Jiansi; Wang, Hongti; Zhu, Xiaoyi; Xu, Zhiqiang; Zheng, Yu

2017-03-01

Earthquake Early Warning System (EEWS) has shown its efficiency for earthquake damage mitigation. As the progress of low-cost Micro Electro Mechanical System (MEMS), many types of MEMS-based accelerometers have been developed and widely used in deploying large-scale, dense seismic networks for EEWS. However, the noise performance of these commercially available MEMS is still insufficient for weak seismic signals, leading to the large scatter of early-warning parameters estimation. In this study, we developed a new type of tri-axial accelerometer based on high dynamic range MEMS with low noise level using for EEWS. It is a MEMS-integrated data logger with built-in seismological processing. The device is built on a custom-tailored Linux 2.6.27 operating system and the method for automatic detecting seismic events is STA/LTA algorithms. When a seismic event is detected, peak ground parameters of all data components will be calculated at an interval of 1 s, and τc-Pd values will be evaluated using the initial 3 s of P wave. These values will then be organized as a trigger packet actively sent to the processing center for event combining detection. The output data of all three components are calibrated to sensitivity 500 counts/cm/s2. Several tests and a real field test deployment were performed to obtain the performances of this device. The results show that the dynamic range can reach 98 dB for the vertical component and 99 dB for the horizontal components, and majority of bias temperature coefficients are lower than 200 μg/°C. In addition, the results of event detection and real field deployment have shown its capabilities for EEWS and rapid intensity reporting.

MyShake: A smartphone seismic network for earthquake early warning and beyond

PubMed Central

Kong, Qingkai; Allen, Richard M.; Schreier, Louis; Kwon, Young-Woo

2016-01-01

Large magnitude earthquakes in urban environments continue to kill and injure tens to hundreds of thousands of people, inflicting lasting societal and economic disasters. Earthquake early warning (EEW) provides seconds to minutes of warning, allowing people to move to safe zones and automated slowdown and shutdown of transit and other machinery. The handful of EEW systems operating around the world use traditional seismic and geodetic networks that exist only in a few nations. Smartphones are much more prevalent than traditional networks and contain accelerometers that can also be used to detect earthquakes. We report on the development of a new type of seismic system, MyShake, that harnesses personal/private smartphone sensors to collect data and analyze earthquakes. We show that smartphones can record magnitude 5 earthquakes at distances of 10 km or less and develop an on-phone detection capability to separate earthquakes from other everyday shakes. Our proof-of-concept system then collects earthquake data at a central site where a network detection algorithm confirms that an earthquake is under way and estimates the location and magnitude in real time. This information can then be used to issue an alert of forthcoming ground shaking. MyShake could be used to enhance EEW in regions with traditional networks and could provide the only EEW capability in regions without. In addition, the seismic waveforms recorded could be used to deliver rapid microseism maps, study impacts on buildings, and possibly image shallow earth structure and earthquake rupture kinematics. PMID:26933682

MyShake: A smartphone seismic network for earthquake early warning and beyond.

PubMed

Kong, Qingkai; Allen, Richard M; Schreier, Louis; Kwon, Young-Woo

2016-02-01

Large magnitude earthquakes in urban environments continue to kill and injure tens to hundreds of thousands of people, inflicting lasting societal and economic disasters. Earthquake early warning (EEW) provides seconds to minutes of warning, allowing people to move to safe zones and automated slowdown and shutdown of transit and other machinery. The handful of EEW systems operating around the world use traditional seismic and geodetic networks that exist only in a few nations. Smartphones are much more prevalent than traditional networks and contain accelerometers that can also be used to detect earthquakes. We report on the development of a new type of seismic system, MyShake, that harnesses personal/private smartphone sensors to collect data and analyze earthquakes. We show that smartphones can record magnitude 5 earthquakes at distances of 10 km or less and develop an on-phone detection capability to separate earthquakes from other everyday shakes. Our proof-of-concept system then collects earthquake data at a central site where a network detection algorithm confirms that an earthquake is under way and estimates the location and magnitude in real time. This information can then be used to issue an alert of forthcoming ground shaking. MyShake could be used to enhance EEW in regions with traditional networks and could provide the only EEW capability in regions without. In addition, the seismic waveforms recorded could be used to deliver rapid microseism maps, study impacts on buildings, and possibly image shallow earth structure and earthquake rupture kinematics.

Towards a certification process for tsunami early warning systems

NASA Astrophysics Data System (ADS)

Löwe, Peter; Wächter, Jochen; Hammitzsch, Martin

2013-04-01

The natural disaster of the Boxing Day Tsunami of 2004 was followed by an information catastrophe. Crucial early warning information could not be delivered to the communities under imminent threat, resulting in over 240,000 casualties in 14 countries. This tragedy sparked the development of a new generation of integrated modular Tsunami Early Warning Systems (TEWS). While significant advances were accomplished in the past years, recent events, like the Chile 2010 and the Tohoku 2011 tsunami demonstrate that the key technical challenge for Tsunami Early Warning research on the supranational scale still lies in the timely issuing of status information and reliable early warning messages in a proven workflow. A second challenge stems from the main objective of the Intergovernmental Oceanographic Commission of UNESCO (IOC) Tsunami Programme, the integration of national TEWS towards ocean-wide networks: Each of the increasing number of integrated Tsunami Early Warning Centres has to cope with the continuing evolution of sensors, hardware and software while having to maintain reliable inter-center information exchange services. To avoid future information catastrophes, the performance of all components, ranging from individual sensors, to Warning Centers within their particular end-to-end Warning System Environments, and up to federated Systems of Tsunami Warning Systems has to be regularly validated against defined criteria. Since 2004, GFZ German Research Centre for Geosciences (GFZ) has built up expertise in the field of TEWS. Within GFZ, the Centre for GeoInformation Technology (CeGIT) has focused its work on the geoinformatics aspects of TEWS in two projects already, being the German Indonesian Tsunami Early Warning System (GITEWS) and the Distant Early Warning System (DEWS). This activity is continued in the TRIDEC project (Collaborative, Complex, and Critical Decision Processes in Evolving Crises) funded under the European Union's seventh Framework Programme (FP7). TRIDEC focuses on real-time intelligent information management in Earth management and its long-term application: The technical development is based on mature system architecture models and industry standards. The use of standards already applies to the operation of individual TRIDEC reference installations and their interlinking into an integrated service infrastructure for supranational warning services. This is a first step towards best practices and service lifecycles for Early Warning Centre IT service management, including Service Level Agreements (SLA) and Service Certification. While on a global scale the integration of TEWS progresses towards Systems of Systems (SoS), there is still an absence of accredited and reliable certifications for national TEWS or regional Tsunami Early Warning Systems of Systems (TEWSoS). Concepts for TEWS operations have already been published under the guidance of the IOC, and can now be complemented by the recent research advances concerning SoS architecture. Combined with feedback from the real world, such as the NEAMwave 2012 Tsunami exercise in the Mediterranean, this can serve as a starting point to formulate initial requirements for TEWS and TEWSoS certification: Certification activities will cover the establishment of new TEWS and TEWSoS, and also both maintenance and enhancement of existing TEWS/TEWSoS. While the IOC is expected to take a central role in the development of the certification strategy, it remains to be defined which bodies will actually conduct the certification process. Certification requirements and results are likely to become a valuable information source for various target groups, ranging from national policy decision makers, government agency planners, national and local government preparedness officials, TWC staff members, Disaster Responders, the media and the insurance industry.

PPP Sliding Window Algorithm and Its Application in Deformation Monitoring.

PubMed

Song, Weiwei; Zhang, Rui; Yao, Yibin; Liu, Yanyan; Hu, Yuming

2016-05-31

Compared with the double-difference relative positioning method, the precise point positioning (PPP) algorithm can avoid the selection of a static reference station and directly measure the three-dimensional position changes at the observation site and exhibit superiority in a variety of deformation monitoring applications. However, because of the influence of various observing errors, the accuracy of PPP is generally at the cm-dm level, which cannot meet the requirements needed for high precision deformation monitoring. For most of the monitoring applications, the observation stations maintain stationary, which can be provided as a priori constraint information. In this paper, a new PPP algorithm based on a sliding window was proposed to improve the positioning accuracy. Firstly, data from IGS tracking station was processed using both traditional and new PPP algorithm; the results showed that the new algorithm can effectively improve positioning accuracy, especially for the elevation direction. Then, an earthquake simulation platform was used to simulate an earthquake event; the results illustrated that the new algorithm can effectively detect the vibrations change of a reference station during an earthquake. At last, the observed Wenchuan earthquake experimental results showed that the new algorithm was feasible to monitor the real earthquakes and provide early-warning alerts.

Forests and Phenology: Designing the Early Warning System to Understand Forest Change

NASA Astrophysics Data System (ADS)

Pierce, T.; Phillips, M. B.; Hargrove, W. W.; Dobson, G.; Hicks, J.; Hutchins, M.; Lichtenstein, K.

2010-12-01

Vegetative phenology is the study of plant development and changes with the seasons, such as the greening-up and browning-down of forests, and how these events are influenced by variations in climate. A National Phenology Data Set, based on Moderate Resolution Imaging Spectroradiometer satellite images covering 2002 through 2009, is now available from work by NASA, the US Forest Service, and Oak Ridge National Laboratory. This new data set provides an easily interpretable product useful for detecting changes to the landscape due to long-term factors such as climate change, as well as finding areas affected by short-term forest threats such as insects or disease. The Early Warning System (EWS) is a toolset being developed by the US Forest Service and the University of North Carolina-Asheville to support distribution and use of the National Phenology Data Set. The Early Warning System will help research scientists, US Forest Service personnel, forest and natural resources managers, decision makers, and the public in the use of phenology data to better understand unexpected change within our nation’s forests. These changes could have multiple natural sources such as insects, disease, or storm damage, or may be due to human-induced events, like thinning, harvest, forest conversion to agriculture, or residential and commercial use. The primary goal of the Early Warning System is to provide a seamless integration between monitoring, detection, early warning and prediction of these forest disturbances as observed through phenological data. The system consists of PC and web-based components that are structured to support four user stages of increasing knowledge and data sophistication. Building Literacy: This stage of the Early Warning System educates potential users about the system, why the system should be used, and the fundamentals about the data the system uses. The channels for this education include a website, interactive tutorials, pamphlets, and other technology transfer methodologies. Achieving Context and Meaning: To provide deeper meaning and knowledge about the Early Warning System to users, this stage of the Early Warning System provides more information about specific examples of disturbances seen in the phenological data, as well the spatial and temporal context to these disturbances. The main components of this stage are specific case studies of forest disturbances. Accessing Data: This component of the Early Warning System includes products for research scientists, the aerial detection survey sketch mapper community, and others who will access and analyze the Early Warning System and phenological data. Products and data will be available through online GIS mashups and WMS and KML downloads. Utilizing Services: The final stage of the Early Warning System supports the analysis of phenological data and serves the results to those end users, including forest managers, the forest industry, and the public, who need to locate past, present, and potential forest disturbances. The main components of this stage include data-driven web tools, automated analysis processes, and end user training programs.

ShakeAlert—An earthquake early warning system for the United States west coast

USGS Publications Warehouse

Burkett, Erin R.; Given, Douglas D.; Jones, Lucile M.

2014-08-29

Earthquake early warning systems use earthquake science and the technology of monitoring systems to alert devices and people when shaking waves generated by an earthquake are expected to arrive at their location. The seconds to minutes of advance warning can allow people and systems to take actions to protect life and property from destructive shaking. The U.S. Geological Survey (USGS), in collaboration with several partners, has been working to develop an early warning system for the United States. ShakeAlert, a system currently under development, is designed to cover the West Coast States of California, Oregon, and Washington.

Infrared small target detection technology based on OpenCV

NASA Astrophysics Data System (ADS)

Liu, Lei; Huang, Zhijian

2013-05-01

Accurate and fast detection of infrared (IR) dim target has very important meaning for infrared precise guidance, early warning, video surveillance, etc. In this paper, some basic principles and the implementing flow charts of a series of algorithms for target detection are described. These algorithms are traditional two-frame difference method, improved three-frame difference method, background estimate and frame difference fusion method, and building background with neighborhood mean method. On the foundation of above works, an infrared target detection software platform which is developed by OpenCV and MFC is introduced. Three kinds of tracking algorithms are integrated in this software. In order to explain the software clearly, the framework and the function are described in this paper. At last, the experiments are performed for some real-life IR images. The whole algorithm implementing processes and results are analyzed, and those algorithms for detection targets are evaluated from the two aspects of subjective and objective. The results prove that the proposed method has satisfying detection effectiveness and robustness. Meanwhile, it has high detection efficiency and can be used for real-time detection.

Infrared small target detection technology based on OpenCV

NASA Astrophysics Data System (ADS)

Liu, Lei; Huang, Zhijian

2013-09-01

Accurate and fast detection of infrared (IR) dim target has very important meaning for infrared precise guidance, early warning, video surveillance, etc. In this paper, some basic principles and the implementing flow charts of a series of algorithms for target detection are described. These algorithms are traditional two-frame difference method, improved three-frame difference method, background estimate and frame difference fusion method, and building background with neighborhood mean method. On the foundation of above works, an infrared target detection software platform which is developed by OpenCV and MFC is introduced. Three kinds of tracking algorithms are integrated in this software. In order to explain the software clearly, the framework and the function are described in this paper. At last, the experiments are performed for some real-life IR images. The whole algorithm implementing processes and results are analyzed, and those algorithms for detection targets are evaluated from the two aspects of subjective and objective. The results prove that the proposed method has satisfying detection effectiveness and robustness. Meanwhile, it has high detection efficiency and can be used for real-time detection.

Using SMAP data to improve drought early warning over the US Great Plains

NASA Astrophysics Data System (ADS)

Fu, R.; Fernando, N.; Tang, W.

2015-12-01

A drought prone region such as the Great Plains of the United States (US GP) requires credible and actionable drought early warning. Such information cannot simply be extracted from available climate forecasts because of their large uncertainties at regional scales, and unclear connections to the needs of the decision makers. In particular, current dynamic seasonal predictions and climate projections, such as those produced by the NOAA North American Multi-Model Ensemble experiment (NMME) are much more reliable for winter and spring than for the summer season for the US GP. To mitigate the weaknesses of dynamic prediction/projections, we have identified three key processes behind the spring-to-summer dry memory through observational studies, as the scientific basis for a statistical drought early warning system. This system uses percentile soil moisture anomalies in spring as a key input to provide a probabilistic summer drought early warning. The latter outperforms the dynamic prediction over the US Southern Plains and has been used by the Texas state water agency to support state drought preparedness. A main source of uncertainty for this drought early warning system is the soil moisture input obtained from the NOAA Climate Forecasting System (CFS). We are testing use of the beta version of NASA Soil Moisture Active Passive (SMAP) soil moisture data, along with the Soil Moisture and Ocean Salinity (SMOS), and the long-term Essential Climate Variable Soil Moisture (ECV-SM) soil moisture data, to reduce this uncertainty. Preliminary results based on ECV-SM suggests satellite based soil moisture data could improve early warning of rainfall anomalies over the western US GP with less dense vegetation. The skill degrades over the eastern US GP where denser vegetation is found. We evaluate our SMAP-based drought early warning for 2015 summer against observations.

An early warning system for groundwater pollution based on the assessment of groundwater pollution risks.

NASA Astrophysics Data System (ADS)

Zhang, Weihong.; Zhao, Yongsheng; Hong, Mei; Guo, Xiaodong

2009-04-01

Groundwater pollution usually is complex and concealed, remediation of which is difficult, high cost, time-consuming, and ineffective. An early warning system for groundwater pollution is needed that detects groundwater quality problems and gets the information necessary to make sound decisions before massive groundwater quality degradation occurs. Groundwater pollution early warning were performed by considering comprehensively the current groundwater quality, groundwater quality varying trend and groundwater pollution risk . The map of the basic quality of the groundwater was obtained by fuzzy comprehensive evaluation or BP neural network evaluation. Based on multi-annual groundwater monitoring datasets, Water quality state in sometime of the future was forecasted using time-sequenced analyzing methods. Water quality varying trend was analyzed by Spearman's rank correlative coefficient.The relative risk map of groundwater pollution was estimated through a procedure that identifies, cell by cell,the values of three factors, that is inherent vulnerability, load risk of pollution source and contamination hazard. DRASTIC method was used to assess inherent vulnerability of aquifer. Load risk of pollution source was analyzed based on the potential of contamination and pollution degree. Assessment index of load risk of pollution source which involves the variety of pollution source, quantity of contaminants, releasing potential of pollutants, and distance were determined. The load risks of all sources considered by GIS overlay technology. Early warning model of groundwater pollution combined with ComGIS technology organically, the regional groundwater pollution early-warning information system was developed, and applied it into Qiqiha'er groundwater early warning. It can be used to evaluate current water quality, to forecast water quality changing trend, and to analyze space-time influencing range of groundwater quality by natural process and human activities. Keywords: groundwater pollution, early warning, aquifer vulnerability, pollution load, pollution risk, ComGIS

How one might miss early warning signals of critical transitions in time series data: A systematic study of two major currency pairs

PubMed Central

Ciamarra, Massimo Pica; Cheong, Siew Ann

2018-01-01

There is growing interest in the use of critical slowing down and critical fluctuations as early warning signals for critical transitions in different complex systems. However, while some studies found them effective, others found the opposite. In this paper, we investigated why this might be so, by testing three commonly used indicators: lag-1 autocorrelation, variance, and low-frequency power spectrum at anticipating critical transitions in the very-high-frequency time series data of the Australian Dollar-Japanese Yen and Swiss Franc-Japanese Yen exchange rates. Besides testing rising trends in these indicators at a strict level of confidence using the Kendall-tau test, we also required statistically significant early warning signals to be concurrent in the three indicators, which must rise to appreciable values. We then found for our data set the optimum parameters for discovering critical transitions, and showed that the set of critical transitions found is generally insensitive to variations in the parameters. Suspecting that negative results in the literature are the results of low data frequencies, we created time series with time intervals over three orders of magnitude from the raw data, and tested them for early warning signals. Early warning signals can be reliably found only if the time interval of the data is shorter than the time scale of critical transitions in our complex system of interest. Finally, we compared the set of time windows with statistically significant early warning signals with the set of time windows followed by large movements, to conclude that the early warning signals indeed provide reliable information on impending critical transitions. This reliability becomes more compelling statistically the more events we test. PMID:29538373

How one might miss early warning signals of critical transitions in time series data: A systematic study of two major currency pairs.

PubMed

Wen, Haoyu; Ciamarra, Massimo Pica; Cheong, Siew Ann

2018-01-01

There is growing interest in the use of critical slowing down and critical fluctuations as early warning signals for critical transitions in different complex systems. However, while some studies found them effective, others found the opposite. In this paper, we investigated why this might be so, by testing three commonly used indicators: lag-1 autocorrelation, variance, and low-frequency power spectrum at anticipating critical transitions in the very-high-frequency time series data of the Australian Dollar-Japanese Yen and Swiss Franc-Japanese Yen exchange rates. Besides testing rising trends in these indicators at a strict level of confidence using the Kendall-tau test, we also required statistically significant early warning signals to be concurrent in the three indicators, which must rise to appreciable values. We then found for our data set the optimum parameters for discovering critical transitions, and showed that the set of critical transitions found is generally insensitive to variations in the parameters. Suspecting that negative results in the literature are the results of low data frequencies, we created time series with time intervals over three orders of magnitude from the raw data, and tested them for early warning signals. Early warning signals can be reliably found only if the time interval of the data is shorter than the time scale of critical transitions in our complex system of interest. Finally, we compared the set of time windows with statistically significant early warning signals with the set of time windows followed by large movements, to conclude that the early warning signals indeed provide reliable information on impending critical transitions. This reliability becomes more compelling statistically the more events we test.

Hypoglycemia early alarm systems based on recursive autoregressive partial least squares models.

PubMed

Bayrak, Elif Seyma; Turksoy, Kamuran; Cinar, Ali; Quinn, Lauretta; Littlejohn, Elizabeth; Rollins, Derrick

2013-01-01

Hypoglycemia caused by intensive insulin therapy is a major challenge for artificial pancreas systems. Early detection and prevention of potential hypoglycemia are essential for the acceptance of fully automated artificial pancreas systems. Many of the proposed alarm systems are based on interpretation of recent values or trends in glucose values. In the present study, subject-specific linear models are introduced to capture glucose variations and predict future blood glucose concentrations. These models can be used in early alarm systems of potential hypoglycemia. A recursive autoregressive partial least squares (RARPLS) algorithm is used to model the continuous glucose monitoring sensor data and predict future glucose concentrations for use in hypoglycemia alarm systems. The partial least squares models constructed are updated recursively at each sampling step with a moving window. An early hypoglycemia alarm algorithm using these models is proposed and evaluated. Glucose prediction models based on real-time filtered data has a root mean squared error of 7.79 and a sum of squares of glucose prediction error of 7.35% for six-step-ahead (30 min) glucose predictions. The early alarm systems based on RARPLS shows good performance. A sensitivity of 86% and a false alarm rate of 0.42 false positive/day are obtained for the early alarm system based on six-step-ahead predicted glucose values with an average early detection time of 25.25 min. The RARPLS models developed provide satisfactory glucose prediction with relatively smaller error than other proposed algorithms and are good candidates to forecast and warn about potential hypoglycemia unless preventive action is taken far in advance. © 2012 Diabetes Technology Society.

Hypoglycemia Early Alarm Systems Based on Recursive Autoregressive Partial Least Squares Models

PubMed Central

Bayrak, Elif Seyma; Turksoy, Kamuran; Cinar, Ali; Quinn, Lauretta; Littlejohn, Elizabeth; Rollins, Derrick

2013-01-01

Background Hypoglycemia caused by intensive insulin therapy is a major challenge for artificial pancreas systems. Early detection and prevention of potential hypoglycemia are essential for the acceptance of fully automated artificial pancreas systems. Many of the proposed alarm systems are based on interpretation of recent values or trends in glucose values. In the present study, subject-specific linear models are introduced to capture glucose variations and predict future blood glucose concentrations. These models can be used in early alarm systems of potential hypoglycemia. Methods A recursive autoregressive partial least squares (RARPLS) algorithm is used to model the continuous glucose monitoring sensor data and predict future glucose concentrations for use in hypoglycemia alarm systems. The partial least squares models constructed are updated recursively at each sampling step with a moving window. An early hypoglycemia alarm algorithm using these models is proposed and evaluated. Results Glucose prediction models based on real-time filtered data has a root mean squared error of 7.79 and a sum of squares of glucose prediction error of 7.35% for six-step-ahead (30 min) glucose predictions. The early alarm systems based on RARPLS shows good performance. A sensitivity of 86% and a false alarm rate of 0.42 false positive/day are obtained for the early alarm system based on six-step-ahead predicted glucose values with an average early detection time of 25.25 min. Conclusions The RARPLS models developed provide satisfactory glucose prediction with relatively smaller error than other proposed algorithms and are good candidates to forecast and warn about potential hypoglycemia unless preventive action is taken far in advance. PMID:23439179

[Evaluation and analysis of monitoring and early warning functions of the occupational disease reporting system in China].

PubMed

Zhu, Xiaojun; Li, Tao; Liu, Mengxuan

2015-06-01

To evaluate the monitoring and early warning functions of the occupational disease reporting system right now in China, and to analyze their influencing factors. An improved audit tool (ODIT) was used to score the monitoring and early warning functions with a total score of 10. The nine indices were completeness of information on the reporting form, coverage of the reporting system, accessibility of criteria or guidelines for diagnosis, education and training for physicians, completeness of the reporting system, statistical methods, investigation of special cases, release of monitoring information, and release of early warning information. According to the evaluation, the occupational disease reporting system in China had a score of 5.5 in monitoring existing occupational diseases with a low score for release of monitoring information; the reporting system had a score of 6.5 in early warning of newly occurring occupational diseases with low scores for education and training for physicians as well as completeness of the reporting system. The occupational disease reporting system in China still does not have full function in monitoring and early warning. It is the education and participation of physicians from general hospitals in the diagnosis and treatment of occupational diseases and suspected occupational diseases that need to be enhanced. In addition, the problem of monitoring the incidence of occupational diseases needs to be solved as soon as possible.

A SDMS Model: Early Warning Coordination Centres

NASA Astrophysics Data System (ADS)

Santos-Reyes, Jaime

2010-05-01

Following the tsunami disaster in 2004, the General Secretary of the United Nations (UN) Kofi Annan called for a global early warning system for all hazards and for all communities. He also requested the ISDR (International Strategy fort Disaster Reduction) and its UN partners to conduct a global survey of capacities, gaps and opportunities in relation to early warning systems. The produced report, "Global survey of Early Warning Systems", concluded that there are many gaps and shortcomings and that much progress has been made on early warning systems and great capabilities are available around the world. However, it may be argued that an early warning system (EWS) may not be enough to prevent fatalities due to a natural hazard; i.e., it should be seen as part of a ‘wider' or total system. Furthermore, an EWS may work very well when assessed individually but it is not clear whether it will contribute to accomplish the purpose of the ‘total disaster management system'; i.e., to prevent fatalities. For instance, a regional EWS may only work if it is well co-ordinated with the local warning and emergency response systems that ensure that the warning is received, communicated and acted upon by the potentially affected communities. It may be argued that without these local measures being in place, a regional EWS will have little impact in saving lives. Researchers argued that unless people are warned in remote areas, the technology is useless; for instance McGuire [5] argues that: "I have no doubt that the technical element of the warning system will work very well,"…"But there has to be an effective and efficient communications cascade from the warning centre to the fisherman on the beach and his family and the bar owners." Similarly, McFadden [6] states that: "There's no point in spending all the money on a fancy monitoring and a fancy analysis system unless we can make sure the infrastructure for the broadcast system is there,"… "That's going to require a lot of work. If it's a tsunami, you've got to get it down to the last Joe on the beach. This is the stuff that is really very hard." Given the above, the paper argues that there is a need for a systemic approach to early warning centres. Systemic means looking upon things as a system; systemic means seeing pattern and inter-relationship within a complex whole; i.e., to see events as products of the working of a system. System may be defined as a whole which is made of parts and relationships. Given this, ‘failure' may be seen as the product of a system and, within that, see death/injury/property loss etc. as results of the working of systems. This paper proposes a preliminary model of ‘early warning coordination centres' (EWCC); it should be highlighted that an EWCC is a subsystem of the Systemic Disaster Management System (SDMS) model.

Famines in Africa: is early warning early enough?

PubMed Central

Kim, Jeeyon Janet; Guha-Sapir, Debarati

2012-01-01

Following the second Sahelian famine in 1984–1985, major investments were made to establish Early Warning Systems. These systems help to ensure that timely warnings and vulnerability information are available to decision makers to anticipate and avert food crises. In the recent crisis in the Horn of Africa, alarming levels of acute malnutrition were documented from March 2010, and by August 2010, an impending food crisis was forecast. Despite these measures, the situation remained unrecognised, and further deteriorated causing malnutrition levels to grow in severity and scope. By the time the United Nations officially declared famine on 20 July 2011, and the humanitarian community sluggishly went into response mode, levels of malnutrition and mortality exceeded catastrophic levels. At this time, an estimated 11 million people were in desperate and immediate need for food. With warnings of food crises in the Sahel, South Sudan, and forecast of the drought returning to the Horn, there is an immediate need to institutionalize change in the health response during humanitarian emergencies. Early warning systems are only effective if they trigger an early response. PMID:22745628

Famines in Africa: is early warning early enough?

PubMed

Kim, Jeeyon Janet; Guha-Sapir, Debarati

2012-01-01

Following the second Sahelian famine in 1984-1985, major investments were made to establish Early Warning Systems. These systems help to ensure that timely warnings and vulnerability information are available to decision makers to anticipate and avert food crises. In the recent crisis in the Horn of Africa, alarming levels of acute malnutrition were documented from March 2010, and by August 2010, an impending food crisis was forecast. Despite these measures, the situation remained unrecognised, and further deteriorated causing malnutrition levels to grow in severity and scope. By the time the United Nations officially declared famine on 20 July 2011, and the humanitarian community sluggishly went into response mode, levels of malnutrition and mortality exceeded catastrophic levels. At this time, an estimated 11 million people were in desperate and immediate need for food. With warnings of food crises in the Sahel, South Sudan, and forecast of the drought returning to the Horn, there is an immediate need to institutionalize change in the health response during humanitarian emergencies. Early warning systems are only effective if they trigger an early response.

Implementing drought early warning systems: policy lessons and future needs

NASA Astrophysics Data System (ADS)

Iglesias, Ana; Werner, Micha; Maia, Rodrigo; Garrote, Luis; Nyabeze, Washington

2014-05-01

Drought forecasting and Warning provides the potential of reducing impacts to society due to drought events. The implementation of effective drought forecasting and warning, however, requires not only science to support reliable forecasting, but also adequate policy and societal response. Here we propose a protocol to develop drought forecasting and early warning based in the international cooperation of African and European institutions in the DEWFORA project (EC, 7th Framework Programme). The protocol includes four major phases that address the scientific knowledge and the social capacity to use the knowledge: (a) What is the science available? Evaluating how signs of impending drought can be detected and predicted, defining risk levels, and analysing of the signs of drought in an integrated vulnerability approach. (b) What are the societal capacities? In this the institutional framework that enables policy development is evaluated. The protocol gathers information on vulnerability and pending hazard in advance so that early warnings can be declared at sufficient lead time and drought mitigation planning can be implemented at an early stage. (c) How can science be translated into policy? Linking science indicators into the actions/interventions that society needs to implement, and evaluating how policy is implemented. Key limitations to planning for drought are the social capacities to implement early warning systems. Vulnerability assessment contributes to identify these limitations and therefore provides crucial information to policy development. Based on the assessment of vulnerability we suggest thresholds for management actions to respond to drought forecasts and link predictive indicators to relevant potential mitigation strategies. Vulnerability assessment is crucial to identify relief, coping and management responses that contribute to a more resilient society. (d) How can society benefit from the forecast? Evaluating how information is provided to potentially affected groups, and how mitigation strategies can be taken in response. This paper presents an outline of the protocol that was developed in the DEWFORA project, outlining the complementary roles of science, policy and societal uptake in effective drought forecasting and warning. A consensus on the need to emphasise the social component of early warning was reached when testing the DEWFORA early warning system protocol among experts from 18 countries.

CoopEUS Case Study: Tsunami Modelling and Early Warning Systems for Near Source Areas (Mediterranean, Juan de Fuca).

NASA Astrophysics Data System (ADS)

Beranzoli, Laura; Best, Mairi; Chierici, Francesco; Embriaco, Davide; Galbraith, Nan; Heeseman, Martin; Kelley, Deborah; Pirenne, Benoit; Scofield, Oscar; Weller, Robert

2015-04-01

There is a need for tsunami modeling and early warning systems for near-source areas. For example this is a common public safety threat in the Mediterranean and Juan de Fuca/NE Pacific Coast of N.A.; Regions covered by the EMSO, OOI, and ONC ocean observatories. Through the CoopEUS international cooperation project, a number of environmental research infrastructures have come together to coordinate efforts on environmental challenges; this tsunami case study tackles one such challenge. There is a mutual need of tsunami event field data and modeling to deepen our experience in testing methodology and developing real-time data processing. Tsunami field data are already available for past events, part of this use case compares these for compatibility, gap analysis, and model groundtruthing. It also reviews sensors needed and harmonizes instrument settings. Sensor metadata and registries are compared, harmonized, and aligned. Data policies and access are also compared and assessed for gap analysis. Modelling algorithms are compared and tested against archived and real-time data. This case study will then be extended to other related tsunami data and model sources globally with similar geographic and seismic scenarios.

Design of a Fatigue Detection System for High-Speed Trains Based on Driver Vigilance Using a Wireless Wearable EEG.

PubMed

Zhang, Xiaoliang; Li, Jiali; Liu, Yugang; Zhang, Zutao; Wang, Zhuojun; Luo, Dianyuan; Zhou, Xiang; Zhu, Miankuan; Salman, Waleed; Hu, Guangdi; Wang, Chunbai

2017-03-01

The vigilance of the driver is important for railway safety, despite not being included in the safety management system (SMS) for high-speed train safety. In this paper, a novel fatigue detection system for high-speed train safety based on monitoring train driver vigilance using a wireless wearable electroencephalograph (EEG) is presented. This system is designed to detect whether the driver is drowsiness. The proposed system consists of three main parts: (1) a wireless wearable EEG collection; (2) train driver vigilance detection; and (3) early warning device for train driver. In the first part, an 8-channel wireless wearable brain-computer interface (BCI) device acquires the locomotive driver's brain EEG signal comfortably under high-speed train-driving conditions. The recorded data are transmitted to a personal computer (PC) via Bluetooth. In the second step, a support vector machine (SVM) classification algorithm is implemented to determine the vigilance level using the Fast Fourier transform (FFT) to extract the EEG power spectrum density (PSD). In addition, an early warning device begins to work if fatigue is detected. The simulation and test results demonstrate the feasibility of the proposed fatigue detection system for high-speed train safety.

An early warning and control system for urban, drinking water quality protection: China's experience.

PubMed

Hou, Dibo; Song, Xiaoxuan; Zhang, Guangxin; Zhang, Hongjian; Loaiciga, Hugo

2013-07-01

An event-driven, urban, drinking water quality early warning and control system (DEWS) is proposed to cope with China's urgent need for protecting its urban drinking water. The DEWS has a web service structure and provides users with water quality monitoring functions, water quality early warning functions, and water quality accident decision-making functions. The DEWS functionality is guided by the principles of control theory and risk assessment as applied to the feedback control of urban water supply systems. The DEWS has been deployed in several large Chinese cities and found to perform well insofar as water quality early warning and emergency decision-making is concerned. This paper describes a DEWS for urban water quality protection that has been developed in China.

Climate change implications and use of early warning systems for global dust storms

USGS Publications Warehouse

Harriman, Lindsey M.

2014-01-01

With increased changes in land cover and global climate, early detection and warning of dust storms in conjunction with effective and widespread information broadcasts will be essential to the prevention and mitigation of future risks and impacts. Human activities, seasonal variations and long-term climatic patterns influence dust storms. More research is needed to analyse these factors of dust mobilisation to create more certainty for the fate of vulnerable populations and ecosystems in the future. Early warning and communication systems, when in place and effectively implemented, can offer some relief to these vulnerable areas. As an issue that affects many regions of the world, there is a profound need to understand the potential changes and ultimately create better early warning systems for dust storms.

Studying the response of drivers against different collision warning systems: a review

NASA Astrophysics Data System (ADS)

Muzammel, M.; Yusoff, M. Zuki; Malik, A. Saeed; Mohamad Saad, M. Naufal; Meriaudeau, F.

2017-03-01

The number of vehicle accidents is rapidly increasing and causing significant economic losses in many countries. According to the World Health Organization, road accidents will become the fifth major cause of death by the year 2030. To minimize these accidents different types of collision warning systems have been proposed for motor vehicle drivers. These systems can early detect and warn the drivers about the potential danger, up to a certain accuracy. Many researchers study the effectiveness of these systems by using different methods, including Electroencephalography (EEG). From the literature review, it has been observed that, these systems increase the drivers' response and can help to minimize the accidents that may occur due to drivers unconsciousness. For these collision warning systems, tactile early warnings are found more effective as compared to the auditory and visual early warnings. This review also highlights the areas, where further research can be performed to fully analyze the collision warning system. For example, some contradictions are found among researchers, about these systems' performance for drivers within different age groups. Similarly, most of the EEG studies focus on the front collision warning systems and only give beep sound to alert the drivers. Therefore, EEG study can be performed for the rear end collision warning systems, against proper auditory warning messages which indicate the types of hazards. This EEG study will help to design more friendly collision warning system and may save many lives.

Preparing for floods: flood forecasting and early warning

NASA Astrophysics Data System (ADS)

Cloke, Hannah

2016-04-01

Flood forecasting and early warning has continued to stride ahead in strengthening the preparedness phases of disaster risk management, saving lives and property and reducing the overall impact of severe flood events. For example, continental and global scale flood forecasting systems such as the European Flood Awareness System and the Global Flood Awareness System provide early information about upcoming floods in real time to various decisionmakers. Studies have found that there are monetary benefits to implementing these early flood warning systems, and with the science also in place to provide evidence of benefit and hydrometeorological institutional outlooks warming to the use of probabilistic forecasts, the uptake over the last decade has been rapid and sustained. However, there are many further challenges that lie ahead to improve the science supporting flood early warning and to ensure that appropriate decisions are made to maximise flood preparedness.

Design of flood early warning system with wifi network based on smartphone

NASA Astrophysics Data System (ADS)

Supani, Ahyar; Andriani, Yuli; Taqwa, Ahmad

2017-11-01

Today, the development using internet of things enables activities surrounding us to be monitored, controlled, predicted and calculated remotely through connections to the internet network such as monitoring activities of long-distance flood warning with information technology. Applying an information technology in the field of flood early warning has been developed in the world, either connected to internet network or not. The internet network that has been done in this paper is the design of WiFi network to access data of rainfall, water level and flood status at any time with a smartphone coming from flood early warning system. The results obtained when test of data accessing with smartphone are in form of rainfall and water level graphs against time and flood status indicators consisting of 3 flood states: Standby 2, Standby 1 and Flood. It is concluded that data are from flood early warning system has been able to accessed and displayed on smartphone via WiFi network in any time and real time.

Sensors Provide Early Warning of Biological Threats

NASA Technical Reports Server (NTRS)

2009-01-01

Early Warning Inc. of Troy, New York, licensed powerful biosensor technology from Ames Research Center. Incorporating carbon nanotubes tipped with single strands of nucleic acid from waterborne pathogens, the sensor can detect even minute amounts of targeted, disease causing bacteria, viruses, and parasites. Early Warning features the NASA biosensor in its water analyzer, which can provide advance alert of potential biological hazards in water used for agriculture, food and beverages, showers, and at beaches and lakes -- within hours instead of the days required by conventional laboratory methods.

CISN ShakeAlert: Improving the Virtual Seismologist (VS) earthquake early warning framework to provide faster, more robust warning information

NASA Astrophysics Data System (ADS)

Meier, M.; Cua, G. B.; Wiemer, S.; Fischer, M.

2011-12-01

The Virtual Seismologist (VS) method is a Bayesian approach to regional network-based earthquake early warning (EEW) that uses observed phase arrivals, ground motion amplitudes and selected prior information to estimate earthquake magnitude, location and origin time, and predict the distribution of peak ground motion throughout a region using envelope attenuation relationships. Implementation of the VS algorithm in California is an on-going effort of the Swiss Seismological Service (SED) at ETH Zürich. VS is one of three EEW algorithms - the other two being ElarmS (Allen and Kanamori, 2003) and On-Site (Wu and Kanamori, 2005; Boese et al., 2008) - that form the basis of the California Integrated Seismic Network ShakeAlert system, a prototype end-to-end EEW system that could potentially be implemented in California. The current prototype version of VS in California requires picks at 4 stations to initiate an event declaration. On average, taking into account data latency, variable station distribution, and processing time, this initial estimate is available about 20 seconds after the earthquake origin time, corresponding to a blind zone of about 70 km around the epicenter which would receive no warning, but where it would be the most useful. To increase the available warning time, we want to produce EEW estimates faster (with less than 4 stations). However, working with less than 4 stations with our current approach would increase the number of false alerts, for which there is very little tolerance in a useful EEW system. We explore the use of back-azimuth estimations and the Voronoi-based concept of not-yet-arrived data for reducing false alerts of the earliest VS estimates. The concept of not-yet-arrived data was originally used to provide evolutionary location estimates in EEW (Horiuchi, 2005; Cua and Heaton, 2007; Satriano et al. 2008). However, it can also be applied in discriminating between earthquake and non-earthquake signals. For real earthquakes, the constraints on earthquake location from the not-yet-arrived data and the back-azimuth estimations are consistent with location constraints from the available picks. For non-earthquake signals, these different location constraints are in most cases inconsistent. We use archived event data from the Northern and Southern California Seismic Networks as well as archived continuous waveform data from where the current VS codes erroneously declared events to quantify how using a combination of pick-based and not-yet-arrived data constraints can reduce VS false alert rates while providing faster warning information. The consistency of the pick-based and not-yet-arrived data constraints are mapped into the VS likelihood parameter, which reflects the degree of believe that the signals come from a real earthquake. This approach contributes towards improving the robustness of the Virtual Seismologist Multiple Threshold Event Detection (VS-MTED), which allows for single-station event declarations, when signal amplitudes are large enough.

Early Warnings for Local Labor Markets

ERIC Educational Resources Information Center

Matland, Marc A.

1976-01-01

This articles summarizes the National Planning Association's (NPA) experience in its initial efforts to develop an early warning system to anticipate job openings generated in local communities by large Federal procurement contracts. (WL)

Efficient near-real-time monitoring of 3D surface displacements in complex landslide scenarios

NASA Astrophysics Data System (ADS)

Allasia, Paolo; Manconi, Andrea; Giordan, Daniele; Baldo, Marco; Lollino, Giorgio

2013-04-01

Ground deformation measurements play a key role in monitoring activities of landslides. A wide spectrum of instruments and methods is nowadays available, going from in-situ to remote sensing approaches. In emergency scenarios, monitoring is often based on automated instruments capable to achieve accurate measurements, possibly with a very high temporal resolution, in order to achieve the best information about the evolution of the landslide in near-real-time, aiming at early warning purposes. However, the available tools for a rapid and efficient exploitation, understanding and interpretation of the retrieved measurements is still a challenge. This issue is particularly relevant in contexts where monitoring is fundamental to support early warning systems aimed at ensuring safety to people and/or infrastructures. Furthermore, in many cases the results obtained might be of difficult reading and divulgation, especially when people of different backgrounds are involved (e.g. scientists, authorities, civil protection operators, decision makers, etc.). In this work, we extend the concept of automatic and near real time from the acquisition of measurements to the data processing and divulgation, in order to achieve an efficient monitoring of surface displacements in landslide scenarios. We developed an algorithm that allows to go automatically and in near-real-time from the acquisition of 3D displacements on a landslide area to the efficient divulgation of the monitoring results via WEB. This set of straightforward procedures is called ADVICE (ADVanced dIsplaCement monitoring system for Early warning), and has been already successfully applied in several emergency scenarios. The algorithm includes: (i) data acquisition and transfer protocols; (ii) data collection, filtering, and validation; (iii) data analysis and restitution through a set of dedicated software, such as ©3DA [1]; (iv) recognition of displacement/velocity threshold and early warning (v) short term prediction of the temporal evolution of the landslide, e.g. through the failure forecast method; (vi) publication of the results on a dedicated webpage. Here we show the results gained in the area of Montaguto (southern Italy, ca. 100 km northeast from Naples), where a large-scale earthflow reached the bottom of the valley and severely damaged the SP90 provincial road, as well as the national railroad [2]. We discuss how the use of ADVICE has speed-up and facilitated the understanding of the landslide evolution, the communication of the monitoring results to the partners, and consequently the decision-making process in a critical landslide scenario. [1] Manconi, A., P. Allasia, D. Giordan, M. Baldo, G. Lollino and A. Corazza, Near-real-time 3D surface deformation model obtained via RTS measurements. In Procedings of World Landslide Forum 2, October 3-9, 2011, Rome, Italy. [2] Giordan, D., P. Allasia, A. Manconi, M. Baldo, G. Lollino, M. Santangelo, M. Cardinali and F. Guzzetti, "Morphological evolution of a large earthflow: the Montaguto landslide southern Italy", Geomorphology, in press.

Continuous estimates on the earthquake early warning magnitude by use of the near-field acceleration records

NASA Astrophysics Data System (ADS)

Li, Jun; Jin, Xing; Wei, Yongxiang; Zhang, Hongcai

2013-10-01

In this article, the seismic records of Japan's Kik-net are selected to measure the acceleration, displacement, and effective peak acceleration of each seismic record within a certain time after P wave, then a continuous estimation is given on earthquake early warning magnitude through statistical analysis method, and Wenchuan earthquake record is utilized to check the method. The results show that the reliability of earthquake early warning magnitude continuously increases with the increase of the seismic information, the biggest residual happens if the acceleration is adopted to fit earthquake magnitude, which may be caused by rich high-frequency components and large dispersion of peak value in acceleration record, the influence caused by the high-frequency components can be effectively reduced if the effective peak acceleration and peak displacement is adopted, it is estimated that the dispersion of earthquake magnitude obviously reduces, but it is easy for peak displacement to be affected by long-period drifting. In various components, the residual enlargement phenomenon at vertical direction is almost unobvious, thus it is recommended in this article that the effective peak acceleration at vertical direction is preferred to estimate earthquake early warning magnitude. Through adopting Wenchuan strong earthquake record to check the method mentioned in this article, it is found that this method can be used to quickly, stably, and accurately estimate the early warning magnitude of this earthquake, which shows that this method is completely applicable for earthquake early warning.

Establishing an early warning alert and response network following the Solomon Islands tsunami in 2013.

PubMed

Bilve, Augustine; Nogareda, Francisco; Joshua, Cynthia; Ross, Lester; Betcha, Christopher; Durski, Kara; Fleischl, Juliet; Nilles, Eric

2014-11-01

On 6 February 2013, an 8.0 magnitude earthquake generated a tsunami that struck the Santa Cruz Islands, Solomon Islands, killing 10 people and displacing over 4700. A post-disaster assessment of the risk of epidemic disease transmission recommended the implementation of an early warning alert and response network (EWARN) to rapidly detect, assess and respond to potential outbreaks in the aftermath of the tsunami. Almost 40% of the Santa Cruz Islands' population were displaced by the disaster, and living in cramped temporary camps with poor or absent sanitation facilities and insufficient access to clean water. There was no early warning disease surveillance system. By 25 February, an EWARN was operational in five health facilities that served 90% of the displaced population. Eight priority diseases or syndromes were reported weekly; unexpected health events were reported immediately. Between 25 February and 19 May, 1177 target diseases or syndrome cases were reported. Seven alerts were investigated. No sustained transmission or epidemics were identified. Reporting compliance was 85%. The EWARN was then transitioned to the routine four-syndrome early warning disease surveillance system. It was necessary to conduct a detailed assessment to evaluate the risk and potential impact of serious infectious disease outbreaks, to assess whether and how enhanced early warning disease surveillance should be implemented. Local capacities and available resources should be considered in planning EWARN implementation. An EWARN can be an opportunity to establish or strengthen early warning disease surveillance capabilities.

A New Strategy for Analyzing Time-Series Data Using Dynamic Networks: Identifying Prospective Biomarkers of Hepatocellular Carcinoma.

PubMed

Huang, Xin; Zeng, Jun; Zhou, Lina; Hu, Chunxiu; Yin, Peiyuan; Lin, Xiaohui

2016-08-31

Time-series metabolomics studies can provide insight into the dynamics of disease development and facilitate the discovery of prospective biomarkers. To improve the performance of early risk identification, a new strategy for analyzing time-series data based on dynamic networks (ATSD-DN) in a systematic time dimension is proposed. In ATSD-DN, the non-overlapping ratio was applied to measure the changes in feature ratios during the process of disease development and to construct dynamic networks. Dynamic concentration analysis and network topological structure analysis were performed to extract early warning information. This strategy was applied to the study of time-series lipidomics data from a stepwise hepatocarcinogenesis rat model. A ratio of lyso-phosphatidylcholine (LPC) 18:1/free fatty acid (FFA) 20:5 was identified as the potential biomarker for hepatocellular carcinoma (HCC). It can be used to classify HCC and non-HCC rats, and the area under the curve values in the discovery and external validation sets were 0.980 and 0.972, respectively. This strategy was also compared with a weighted relative difference accumulation algorithm (wRDA), multivariate empirical Bayes statistics (MEBA) and support vector machine-recursive feature elimination (SVM-RFE). The better performance of ATSD-DN suggests its potential for a more complete presentation of time-series changes and effective extraction of early warning information.

A New Strategy for Analyzing Time-Series Data Using Dynamic Networks: Identifying Prospective Biomarkers of Hepatocellular Carcinoma

NASA Astrophysics Data System (ADS)

Huang, Xin; Zeng, Jun; Zhou, Lina; Hu, Chunxiu; Yin, Peiyuan; Lin, Xiaohui

2016-08-01

Time-series metabolomics studies can provide insight into the dynamics of disease development and facilitate the discovery of prospective biomarkers. To improve the performance of early risk identification, a new strategy for analyzing time-series data based on dynamic networks (ATSD-DN) in a systematic time dimension is proposed. In ATSD-DN, the non-overlapping ratio was applied to measure the changes in feature ratios during the process of disease development and to construct dynamic networks. Dynamic concentration analysis and network topological structure analysis were performed to extract early warning information. This strategy was applied to the study of time-series lipidomics data from a stepwise hepatocarcinogenesis rat model. A ratio of lyso-phosphatidylcholine (LPC) 18:1/free fatty acid (FFA) 20:5 was identified as the potential biomarker for hepatocellular carcinoma (HCC). It can be used to classify HCC and non-HCC rats, and the area under the curve values in the discovery and external validation sets were 0.980 and 0.972, respectively. This strategy was also compared with a weighted relative difference accumulation algorithm (wRDA), multivariate empirical Bayes statistics (MEBA) and support vector machine-recursive feature elimination (SVM-RFE). The better performance of ATSD-DN suggests its potential for a more complete presentation of time-series changes and effective extraction of early warning information.

Toward Global Drought Early Warning Capability - Expanding International Cooperation for the Development of a Framework for Monitoring and Forecasting

NASA Technical Reports Server (NTRS)

Pozzi, Will; Sheffield, Justin; Stefanski, Robert; Cripe, Douglas; Pulwarty, Roger; Vogt, Jurgen V.; Heim, Richard R., Jr.; Brewer, Michael J.; Svoboda, Mark; Westerhoff, Rogier;

Feasibility of Using Elastic Wave Velocity Monitoring for Early Warning of Rainfall-Induced Slope Failure.

PubMed

Chen, Yulong; Irfan, Muhammad; Uchimura, Taro; Zhang, Ke

2018-03-27

Rainfall-induced landslides are one of the most widespread slope instability phenomena posing a serious risk to public safety worldwide so that their temporal prediction is of great interest to establish effective warning systems. The objective of this study is to determine the effectiveness of elastic wave velocities in the surface layer of the slope in monitoring, prediction and early warning of landslide. The small-scale fixed and varied, and large-scale slope model tests were conducted. Analysis of the results has established that the elastic wave velocity continuously decreases in response of moisture content and deformation and there was a distinct surge in the decrease rate of wave velocity when failure was initiated. Based on the preliminary results of this analysis, the method using the change in elastic wave velocity proves superior for landslide early warning and suggests that a warning be issued at switch of wave velocity decrease rate.

Predicting Intracranial Pressure and Brain Tissue Oxygen Crises in Patients With Severe Traumatic Brain Injury.

PubMed

Myers, Risa B; Lazaridis, Christos; Jermaine, Christopher M; Robertson, Claudia S; Rusin, Craig G

2016-09-01

To develop computer algorithms that can recognize physiologic patterns in traumatic brain injury patients that occur in advance of intracranial pressure and partial brain tissue oxygenation crises. The automated early detection of crisis precursors can provide clinicians with time to intervene in order to prevent or mitigate secondary brain injury. A retrospective study was conducted from prospectively collected physiologic data. intracranial pressure, and partial brain tissue oxygenation crisis events were defined as intracranial pressure of greater than or equal to 20 mm Hg lasting at least 15 minutes and partial brain tissue oxygenation value of less than 10 mm Hg for at least 10 minutes, respectively. The physiologic data preceding each crisis event were used to identify precursors associated with crisis onset. Multivariate classification models were applied to recorded data in 30-minute epochs of time to predict crises between 15 and 360 minutes in the future. The neurosurgical unit of Ben Taub Hospital (Houston, TX). Our cohort consisted of 817 subjects with severe traumatic brain injury. Our algorithm can predict the onset of intracranial pressure crises with 30-minute advance warning with an area under the receiver operating characteristic curve of 0.86 using only intracranial pressure measurements and time since last crisis. An analogous algorithm can predict the start of partial brain tissue oxygenation crises with 30-minute advanced warning with an area under the receiver operating characteristic curve of 0.91. Our algorithms provide accurate and timely predictions of intracranial hypertension and tissue hypoxia crises in patients with severe traumatic brain injury. Almost all of the information needed to predict the onset of these events is contained within the signal of interest and the time since last crisis.

A Cardiac Early Warning System with Multi Channel SCG and ECG Monitoring for Mobile Health

PubMed Central

Sahoo, Prasan Kumar; Thakkar, Hiren Kumar; Lee, Ming-Yih

2017-01-01

Use of information and communication technology such as smart phone, smart watch, smart glass and portable health monitoring devices for healthcare services has made Mobile Health (mHealth) an emerging research area. Coronary Heart Disease (CHD) is considered as a leading cause of death world wide and an increasing number of people die prematurely due to CHD. Under such circumstances, there is a growing demand for a reliable cardiac monitoring system to catch the intermittent abnormalities and detect critical cardiac behaviors which lead to sudden death. Use of mobile devices to collect Electrocardiography (ECG), Seismocardiography (SCG) data and efficient analysis of those data can monitor a patient’s cardiac activities for early warning. This paper presents a novel cardiac data acquisition method and combined analysis of Electrocardiography (ECG) and multi channel Seismocardiography (SCG) data. An early warning system is implemented to monitor the cardiac activities of a person and accuracy assessment of the early warning system is conducted for the ECG data only. The assessment shows 88% accuracy and effectiveness of our proposed analysis, which implies the viability and applicability of the proposed early warning system. PMID:28353681

A Cardiac Early Warning System with Multi Channel SCG and ECG Monitoring for Mobile Health.

PubMed

Sahoo, Prasan Kumar; Thakkar, Hiren Kumar; Lee, Ming-Yih

2017-03-29

Use of information and communication technology such as smart phone, smart watch, smart glass and portable health monitoring devices for healthcare services has made Mobile Health (mHealth) an emerging research area. Coronary Heart Disease (CHD) is considered as a leading cause of death world wide and an increasing number of people die prematurely due to CHD. Under such circumstances, there is a growing demand for a reliable cardiac monitoring system to catch the intermittent abnormalities and detect critical cardiac behaviors which lead to sudden death. Use of mobile devices to collect Electrocardiography (ECG), Seismocardiography (SCG) data and efficient analysis of those data can monitor a patient's cardiac activities for early warning. This paper presents a novel cardiac data acquisition method and combined analysis of Electrocardiography (ECG) and multi channel Seismocardiography (SCG) data. An early warning system is implemented to monitor the cardiac activities of a person and accuracy assessment of the early warning system is conducted for the ECG data only. The assessment shows 88% accuracy and effectiveness of our proposed analysis, which implies the viability and applicability of the proposed early warning system.

Bridging Empirical and Physical Approaches for Landslide Monitoring and Early Warning

NASA Technical Reports Server (NTRS)

Kirschbaum, Dalia; Peters-Lidard, Christa; Adler, Robert; Kumar, Sujay; Harrison, Ken

2011-01-01

Rainfall-triggered landslides typically occur and are evaluated at local scales, using slope-stability models to calculate coincident changes in driving and resisting forces at the hillslope level in order to anticipate slope failures. Over larger areas, detailed high resolution landslide modeling is often infeasible due to difficulties in quantifying the complex interaction between rainfall infiltration and surface materials as well as the dearth of available in situ soil and rainfall estimates and accurate landslide validation data. This presentation will discuss how satellite precipitation and surface information can be applied within a landslide hazard assessment framework to improve landslide monitoring and early warning by considering two disparate approaches to landslide hazard assessment: an empirical landslide forecasting algorithm and a physical slope-stability model. The goal of this research is to advance near real-time landslide hazard assessment and early warning at larger spatial scales. This is done by employing high resolution surface and precipitation information within a probabilistic framework to provide more physically-based grounding to empirical landslide triggering thresholds. The empirical landslide forecasting tool, running in near real-time at http://trmm.nasa.gov, considers potential landslide activity at the global scale and relies on Tropical Rainfall Measuring Mission (TRMM) precipitation data and surface products to provide a near real-time picture of where landslides may be triggered. The physical approach considers how rainfall infiltration on a hillslope affects the in situ hydro-mechanical processes that may lead to slope failure. Evaluation of these empirical and physical approaches are performed within the Land Information System (LIS), a high performance land surface model processing and data assimilation system developed within the Hydrological Sciences Branch at NASA's Goddard Space Flight Center. LIS provides the capabilities to quantify uncertainty from model inputs and calculate probabilistic estimates for slope failures. Results indicate that remote sensing data can provide many of the spatiotemporal requirements for accurate landslide monitoring and early warning; however, higher resolution precipitation inputs will help to better identify small-scale precipitation forcings that contribute to significant landslide triggering. Future missions, such as the Global Precipitation Measurement (GPM) mission will provide more frequent and extensive estimates of precipitation at the global scale, which will serve as key inputs to significantly advance the accuracy of landslide hazard assessment, particularly over larger spatial scales.

RED Alert – Early warning or detection of global re-emerging infectious disease (RED)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Deshpande, Alina

This is the PDF of a presentation for a webinar given by Los Alamos National Laboratory (LANL) on the early warning or detection of global re-emerging infectious disease (RED). First, there is an overview of LANL biosurveillance tools. Then, information is given about RED Alert. Next, a demonstration is given of a component prototype. RED Alert is an analysis tool that can provide early warning or detection of the re-emergence of an infectious disease at the global level, but through a local lens.

The use of early warning scores to recognise and respond to patient deterioration in district nursing.

PubMed

Tucker, Guy; Lusher, Adele

2018-02-02

This discussion article focuses on the literature surrounding early warning scoring systems and their use in primary care, specifically within district nursing. Patient deterioration is a global concern, associated with high mortality rates and avoidable deaths. Early recognition and response by nursing and other health care staff has been attributed to early warning scoring systems (EWSS) and tools. However, the use of equivalent tools in the community appears to be lacking. This review concludes that there is no consensus over the use of EWSS in district nursing and culture of practice is varied, rather than standardised.

A Steady-State Visual Evoked Potential Brain-Computer Interface System Evaluation as an In-Vehicle Warning Device

NASA Astrophysics Data System (ADS)

Riyahi, Pouria

This thesis is part of current research at Center for Intelligence Systems Research (CISR) at The George Washington University for developing new in-vehicle warning systems via Brain-Computer Interfaces (BCIs). The purpose of conducting this research is to contribute to the current gap between BCI and in-vehicle safety studies. It is based on the premise that accurate and timely monitoring of human (driver) brain's signal to external stimuli could significantly aide in detection of driver's intentions and development of effective warning systems. The thesis starts with introducing the concept of BCI and its development history while it provides a literature review on the nature of brain signals. The current advancement and increasing demand for commercial and non-medical BCI products are described. In addition, the recent research attempts in transportation safety to study drivers' behavior or responses through brain signals are reviewed. The safety studies, which are focused on employing a reliable and practical BCI system as an in-vehicle assistive device, are also introduced. A major focus of this thesis research has been on the evaluation and development of the signal processing algorithms which can effectively filter and process brain signals when the human subject is subjected to Visual LED (Light Emitting Diodes) stimuli at different frequencies. The stimulated brain generates a voltage potential, referred to as Steady-State Visual Evoked Potential (SSVEP). Therefore, a newly modified analysis algorithm for detecting the brain visual signals is proposed. These algorithms are designed to reach a satisfactory accuracy rate without preliminary trainings, hence focusing on eliminating the need for lengthy training of human subjects. Another important concern is the ability of the algorithms to find correlation of brain signals with external visual stimuli in real-time. The developed analysis models are based on algorithms which are capable of generating results for real-time processing of BCI devices. All of these methods are evaluated through two sets of recorded brain signals which were recorded by g.TEC CO. as an external source and recorded brain signals during our car driving simulator experiments. The final discussion is about how the presence of an SSVEP based warning system could affect drivers' performances which is defined by their reaction distance and Time to Collision (TTC). Three different scenarios with and without warning LEDs were planned to measure the subjects' normal driving behavior and their performance while they use a warning system during their driving task. Finally, warning scenarios are divided into short and long warning periods without and with informing the subjects, respectively. The long warning period scenario attempts to determine the level of drivers' distraction or vigilance during driving. The good outcome of warning scenarios can bridge between vehicle safety studies and online BCI system design research. The preliminary results show some promise of the developed methods for in-vehicle safety systems. However, for any decisive conclusion that considers using a BCI system as a helpful in-vehicle assistive device requires far deeper scrutinizing.

Earthquake Early Warning: User Education and Designing Effective Messages

NASA Astrophysics Data System (ADS)

Burkett, E. R.; Sellnow, D. D.; Jones, L.; Sellnow, T. L.

2014-12-01

The U.S. Geological Survey (USGS) and partners are transitioning from test-user trials of a demonstration earthquake early warning system (ShakeAlert) to deciding and preparing how to implement the release of earthquake early warning information, alert messages, and products to the public and other stakeholders. An earthquake early warning system uses seismic station networks to rapidly gather information about an occurring earthquake and send notifications to user devices ahead of the arrival of potentially damaging ground shaking at their locations. Earthquake early warning alerts can thereby allow time for actions to protect lives and property before arrival of damaging shaking, if users are properly educated on how to use and react to such notifications. A collaboration team of risk communications researchers and earth scientists is researching the effectiveness of a chosen subset of potential earthquake early warning interface designs and messages, which could be displayed on a device such as a smartphone. Preliminary results indicate, for instance, that users prefer alerts that include 1) a map to relate their location to the earthquake and 2) instructions for what to do in response to the expected level of shaking. A number of important factors must be considered to design a message that will promote appropriate self-protective behavior. While users prefer to see a map, how much information can be processed in limited time? Are graphical representations of wavefronts helpful or confusing? The most important factor to promote a helpful response is the predicted earthquake intensity, or how strong the expected shaking will be at the user's location. Unlike Japanese users of early warning, few Californians are familiar with the earthquake intensity scale, so we are exploring how differentiating instructions between intensity levels (e.g., "Be aware" for lower shaking levels and "Drop, cover, hold on" at high levels) can be paired with self-directed supplemental information to increase the public's understanding of earthquake shaking and protective behaviors.

Early Warning Look Ahead Metrics: The Percent Milestone Backlog Metric

NASA Technical Reports Server (NTRS)

Shinn, Stephen A.; Anderson, Timothy P.

2017-01-01

All complex development projects experience delays and corresponding backlogs of their project control milestones during their acquisition lifecycles. NASA Goddard Space Flight Center (GSFC) Flight Projects Directorate (FPD) teamed with The Aerospace Corporation (Aerospace) to develop a collection of Early Warning Look Ahead metrics that would provide GSFC leadership with some independent indication of the programmatic health of GSFC flight projects. As part of the collection of Early Warning Look Ahead metrics, the Percent Milestone Backlog metric is particularly revealing, and has utility as a stand-alone execution performance monitoring tool. This paper describes the purpose, development methodology, and utility of the Percent Milestone Backlog metric. The other four Early Warning Look Ahead metrics are also briefly discussed. Finally, an example of the use of the Percent Milestone Backlog metric in providing actionable insight is described, along with examples of its potential use in other commodities.

Research on the Risk Early Warning Method of Material Supplier Performance in Power Industry

NASA Astrophysics Data System (ADS)

Chen, Peng; Zhang, Xi

2018-01-01

The early warning of supplier performance risk is still in the initial stage interiorly, and research on the early warning mechanism to identify, analyze and prevent the performance risk is few. In this paper, a new method aiming at marerial supplier performance risk in power industry is proposed, firstly, establishing a set of risk early warning indexes, Then use the ECM method to classify the indexes to form different risk grades. Then, improving Crock Ford risk quantization model by considering three indicators, including the stability of power system, economic losses and successful bid ratio to form the predictive risk grade, and ultimately using short board effect principle to form the ultimate risk grade to truly reflect the supplier performance risk. Finally, making empirical analysis on supplier performance and putting forward the counter measures and prevention strategies for different risks.

Early warning method of Glacial Lake Outburst Floods based on temperature and rainfall

NASA Astrophysics Data System (ADS)

Liu, Jingjing; Su, Pengcheng; Cheng, Zunlan

2017-04-01

Glacial lake outburst floods (GLOFs) are serious disasters in glacial areas. At present, glaciers are retreating while glacial lake area and the outburst risk increases due to the global warming. Therefore, the research of early warning method of GLOFs is important to prevent and reduce the disasters. This paper provides an early warning method using the temperature and rainfall as indices. The daily growth rate of positive antecedent accumulative temperature and the antecedent thirty days accumulative precipitation are calculated for 21 events of GLOF before 2010, based on data from the 21 meteorological stations nearby. The result shows that all the events are above the curve, TV = -0.0193RDC + 3.0018, which can be taken as the early warning threshold curve. This has been verified by the GLOF events in the Ranzeaco glacial lake on 2013-07-05.

Landslide early warning system prototype with GIS analysis indicates by soil movement and rainfall

NASA Astrophysics Data System (ADS)

Artha, Y.; Julian, E. S.

2018-01-01

The aim of this paper is developing and testing of landslide early warning system. The early warning system uses accelerometersas ground movement and tilt-sensing device and a water flow sensor. A microcentroller is used to process the input signal and activate the alarm. An LCD is used to display the acceleration in x,y and z axis. When the soil moved or shifted and rainfall reached 100 mm/day, the alarm rang and signal were sentto the monitoring center via a telemetry system.Data logging information and GIS spatial data can be monitored remotely as tables and graphics as well as in the form of geographical map with the help of web-GIS interface. The system were tested at Kampung Gerendong, Desa Putat Nutug, Kecamatan Ciseeng, Kabupaten Bogor. This area has 3.15 cumulative score, which mean vulnerable to landslide. The results show that the early warning system worked as planned.

HRAS: a webserver for early warning of human health risk brought by aflatoxin.

PubMed

Hu, Ruifeng; Zeng, Xu; Gao, Weiwei; Wang, Qian; Liu, Zhihua

2013-02-01

Most people are aware that outdoor air pollution can damage their health, but many do not know that indoor air pollution can also exhibit significant negative health effects. Fungi parasitizing in air conditioning and ventilation systems can be one of indoor air pollution sources. Aflatoxin produced by Aspergillus flavus (A. flavus) became a central focus of indoor air pollution, especially in farmer markets. Therefore we developed an early warning system, Health Risk Assessment System, to estimate the growth rate of A. flavus, predict the amount of aflatoxin and provide early warning information. Firstly, the growth of A. flavus and the production of aflatoxin under different conditions were widely obtained through a comprehensive literature review. Secondly, three mathematical models were established to predict the A. flavus colony growth rate, lag phase duration and aflatoxin content, as functions of temperature and water activity based on present studies. Finally, all the results were evaluated by the user-supplied data using PHP programming language. We utilized the web page to show the results and display warning information. The JpGraph library was used to create a dynamic line chart, refreshing the warning information dynamically in real-time. The HARS provides accurate information for early warning purposes to let us take timely steps to protect ourselves.

Earthquake Early Warning: New Strategies for Seismic Hardware

NASA Astrophysics Data System (ADS)

Allardice, S.; Hill, P.

2017-12-01

Implementing Earthquake Early Warning System (EEWS) triggering algorithms into seismic networks has been a hot topic of discussion for some years now. With digitizer technology now available, such as the Güralp Minimus, with on average 40-60ms delay time (latency) from earthquake origin to issuing an alert the next step is to provide network operators with a simple interface for on board parameter calculations from a seismic station. A voting mechanism is implemented on board which mitigates the risk of false positives being communicated. Each Minimus can be configured to with a `score' from various sources i.e. Z channel on seismometer, N/S E/W channels on accelerometer and MEMS inside Minimus. If the score exceeds the set threshold then an alert is sent to the `Master Minimus'. The Master Minimus within the network will also be configured as to when the alert should be issued i.e. at least 3 stations must have triggered. Industry standard algorithms focus around the calculation of Peak Ground Acceleration (PGA), Peak Ground Velocity (PGV), Peak Ground Displacement (PGD) and C. Calculating these single station parameters on-board in order to stream only the results could help network operators with possible issues, such as restricted bandwidth. Developments on the Minimus allow these parameters to be calculated and distributed through Common Alert Protocol (CAP). CAP is the XML based data format used for exchanging and describing public warnings and emergencies. Whenever the trigger conditions are met the Minimus can send a signed UDP packet to the configured CAP receiver which can then send the alert via SMS, e-mail or CAP forwarding. Increasing network redundancy is also a consideration when developing these features, therefore the forwarding CAP message can be sent to multiple destinations. This allows for a hierarchical approach by which the single station (or network) parameters can be streamed to another Minimus, or data centre, or both, so that there is no one single point of failure. Developments on the Guralp Minimus to calculate these on board parameters which are capable of streaming single station parameters, accompanied with the ultra-low latency is the next generation of EEWS and Güralps contribution to the community.

A hazard-independent approach for the standardised multi-channel dissemination of warning messages

NASA Astrophysics Data System (ADS)

Esbri Palomares, M. A.; Hammitzsch, M.; Lendholt, M.

2012-04-01

The tsunami disaster affecting the Indian Ocean region on Christmas 2004 demonstrated very clearly the shortcomings in tsunami detection, public warning processes as well as intergovernmental warning message exchange in the Indian Ocean region. In that regard, early warning systems require that the dissemination of early warning messages has to be executed in way that ensures that the message delivery is timely; the message content is understandable, usable and accurate. To that end, diverse and multiple dissemination channels must be used to increase the chance of the messages reaching all affected persons in a hazard scenario. In addition to this, usage of internationally accepted standards for the warning dissemination such as the Common Alerting Protocol (CAP) and Emergency Data Exchange Language (EDXL) Distribution Element specified by the Organization for the Advancement of Structured Information Standards (OASIS) increase the interoperability among different warning systems enabling thus the concept of system-of-systems proposed by GEOSS. The project Distant Early Warning System (DEWS), co-funded by the European Commission under the 6th Framework Programme, aims at strengthening the early warning capacities by building an innovative generation of interoperable tsunami early warning systems based on the above mentioned concepts following a Service-oriented Architecture (SOA) approach. The project focuses on the downstream part of the hazard information processing where customized, user-tailored warning messages and alerts flow from the warning centre to the responsible authorities and/or the public with their different needs and responsibilities. The information logistics services within DEWS generate tailored EDXL-DE/CAP warning messages for each user that must receive the message according to their preferences, e.g., settings for language, interested areas, dissemination channels, etc.. However, the significant difference in the implementation and capabilities of different dissemination channels such as SMS, email and television, have bearing on the information processing required for delivery and consumption of a DEWS EDXL-DE/CAP message over each dissemination channel. These messages may include additional information in the form of maps, graphs, documents, sensor observations, etc. Therefore, the generated messages are pre-processed by channel adaptors in the information dissemination services converting it into a format that is suitable for end-to-end delivery over the dissemination channels without any semantic distortion. The approach followed by DEWS for disseminating warnings not only relies on traditional communication ways used by the already established early warnings such as the delivery of faxes and phone calls but takes into consideration the use of other broadly used communication channels such as SMS, email, narrowcast and broadcast television, instant messaging, Voice over IP, and radio. It also takes advantage of social media channels like RSS feeds, Facebook, Twitter, etc., enabling a multiplier effect, like in the case of radio and television, and thus allowing to create mash-ups by aggregating other sources of information to the original message. Finally, status information is also important in order to assess and understand whether the process of disseminating the warning to the message consumers has been successfully completed or the process failed at some point of the dissemination chain. To that end, CAP-based messages generated within the information dissemination services provide the semantics for those fields that are of interest within the context of reporting the warning dissemination status in DEWS.

Information spread of emergency events: path searching on social networks.

PubMed

Dai, Weihui; Hu, Hongzhi; Wu, Tunan; Dai, Yonghui

2014-01-01

Emergency has attracted global attentions of government and the public, and it will easily trigger a series of serious social problems if it is not supervised effectively in the dissemination process. In the Internet world, people communicate with each other and form various virtual communities based on social networks, which lead to a complex and fast information spread pattern of emergency events. This paper collects Internet data based on data acquisition and topic detection technology, analyzes the process of information spread on social networks, describes the diffusions and impacts of that information from the perspective of random graph, and finally seeks the key paths through an improved IBF algorithm. Application cases have shown that this algorithm can search the shortest spread paths efficiently, which may help us to guide and control the information dissemination of emergency events on early warning.

The impact of Early Warning Score and Rapid Response Systems on nurses' competence: An integrative literature review and synthesis.

PubMed

Jensen, Jørghild Karlotte; Skår, Randi; Tveit, Bodil

2018-04-01

To describe, interpret and synthesise the current research findings on the impact of the Early Warning Score and Rapid Response Systems on nurses' competence in identifying and managing deteriorating patients in general hospital wards. As patient safety initiatives designed to ensure the early identification and management of deteriorating patients, the Early Warning Score and Rapid Response Systems have broad appeal. However, it is still unclear how these systems impact nurses' competence when these systems are used in general hospital wards. CINAHL, PubMed, Cochrane, EMBASE and Ovid MEDLINE databases were systematically searched for relevant articles. Articles were appraised, a thematic analysis was conducted, and similar and divergent perspectives on emergent themes and subthemes were extracted by a team of researchers. Thirty-six studies met the inclusion criteria. The analysis of findings showed how the Early Warning Score and Rapid Response Systems impacted three competence areas: (i) Nurses' competence in assessing and caring for patients related to the subthemes: (a) sensing clinical deterioration and (b) the development of skills and knowledge. (ii). Nurses' competence in referring patients, related to the subthemes: (a) deciding whether to summon help and (b) the language and communication lines in the referral process. (ii) Nurses' coping and mastery experiences. The impact of the Early Warning Score and Rapid Response Systems on nurses' competence in identifying and managing deteriorating patients is beneficial but also somewhat contradictory. A greater understanding of nurses' development of competence when using the Early Warning Score and Rapid Response Systems will facilitate the design of implementation strategies and the use of these systems to improve practice. © 2017 John Wiley & Sons Ltd.

Early Warning Systems: Re-Engaging Chronic Truants

ERIC Educational Resources Information Center

Chorneau, Tom

2012-01-01

School attendance can be an early indicator that something is going wrong with a student. Gathering, analyzing, and acting on attendance information is a first step toward school improvement. Meanwhile, the majority of the states are moving to build and enhance what are called "early warning systems," intended to flag at-risk students during their…

Integration of launch/impact discrimination algorithm with the UTAMS platform

NASA Astrophysics Data System (ADS)

Desai, Sachi; Morcos, Amir; Tenney, Stephen; Mays, Brian

2008-04-01

An acoustic array, integrated with an algorithm to discriminate potential Launch (LA) or Impact (IM) events, was augmented by employing the Launch Impact Discrimination (LID) algorithm for mortar events. We develop an added situational awareness capability to determine whether the localized event is a mortar launch or mortar impact at safe standoff distances. The algorithm utilizes a discrete wavelet transform to exploit higher harmonic components of various sub bands of the acoustic signature. Additional features are extracted via the frequency domain exploiting harmonic components generated by the nature of event, i.e. supersonic shrapnel components at impact. The further extrapolations of these features are employed with a neural network to provide a high level of confidence for discrimination and classification. The ability to discriminate between these events is of great interest on the battlefield. Providing more information and developing a common picture of situational awareness. Algorithms exploit the acoustic sensor array to provide detection and identification of IM/LA events at extended ranges. The integration of this algorithm with the acoustic sensor array for mortar detection provides an early warning detection system giving greater battlefield information for field commanders. This paper will describe the integration of the algorithm with a candidate sensor and resulting field tests.

Adapting the EDuMaP method to test the performance of the Norwegian early warning system for weather-induced landslides

NASA Astrophysics Data System (ADS)

Piciullo, Luca; Dahl, Mads-Peter; Devoli, Graziella; Colleuille, Hervé; Calvello, Michele

2017-06-01

The Norwegian national landslide early warning system (LEWS), operational since 2013, is managed by the Norwegian Water Resources and Energy Directorate and was designed for monitoring and forecasting the hydrometeorological conditions potentially triggering slope failures. Decision-making in the LEWS is based upon rainfall thresholds, hydrometeorological and real-time landslide observations as well as on landslide inventory and susceptibility maps. Daily alerts are issued throughout the country considering variable size warning zones. Warnings are issued once per day for the following 3 days and can be updated according to weather forecasts and information gathered by the monitoring network. The performance of the LEWS operational in Norway has been evaluated applying the EDuMaP method, which is based on the computation of a duration matrix relating number of landslides and warning levels issued in a warning zone. In the past, this method has been exclusively employed to analyse the performance of regional early warning models considering fixed warning zones. Herein, an original approach is proposed for the computation of the elements of the duration matrix in the case of early warning models issuing alerts on variable size areas. The approach has been used to evaluate the warnings issued in Western Norway, in the period 2013-2014, considering two datasets of landslides. The results indicate that the landslide datasets do not significantly influence the performance evaluation, although a slightly better performance is registered for the smallest dataset. Different performance results are observed as a function of the values adopted for one of the most important input parameters of EDuMaP, the landslide density criterion (i.e. setting the thresholds to differentiate among classes of landslide events). To investigate this issue, a parametric analysis has been conducted; the results of the analysis show significant differences among computed performances when absolute or relative landslide density criteria are considered.

Exploring the utility of real-time hydrologic data for landslide early warning

NASA Astrophysics Data System (ADS)

Mirus, B. B.; Smith, J. B.; Becker, R.; Baum, R. L.; Koss, E.

2017-12-01

Early warning systems can provide critical information for operations managers, emergency planners, and the public to help reduce fatalities, injuries, and economic losses due to landsliding. For shallow, rainfall-triggered landslides early warning systems typically use empirical rainfall thresholds, whereas the actual triggering mechanism involves the non-linear hydrological processes of infiltration, evapotranspiration, and hillslope drainage that are more difficult to quantify. Because hydrologic monitoring has demonstrated that shallow landslides are often preceded by a rise in soil moisture and pore-water pressures, some researchers have developed early warning criteria that attempt to account for these antecedent wetness conditions through relatively simplistic storage metrics or soil-water balance modeling. Here we explore the potential for directly incorporating antecedent wetness into landslide early warning criteria using recent landslide inventories and in-situ hydrologic monitoring near Seattle, WA, and Portland, OR. We use continuous, near-real-time telemetered soil moisture and pore-water pressure data measured within a few landslide-prone hillslopes in combination with measured and forecasted rainfall totals to inform easy-to-interpret landslide initiation thresholds. Objective evaluation using somewhat limited landslide inventories suggests that our new thresholds based on subsurface hydrologic monitoring and rainfall data compare favorably to the capabilities of existing rainfall-only thresholds for the Seattle area, whereas there are no established rainfall thresholds for the Portland area. This preliminary investigation provides a proof-of-concept for the utility of developing landslide early warning criteria in two different geologic settings using real-time subsurface hydrologic measurements from in-situ instrumentation.

Establishing an early warning alert and response network following the Solomon Islands tsunami in 2013

PubMed Central

Bilve, Augustine; Nogareda, Francisco; Joshua, Cynthia; Ross, Lester; Betcha, Christopher; Durski, Kara; Fleischl, Juliet

2014-01-01

Abstract Problem On 6 February 2013, an 8.0 magnitude earthquake generated a tsunami that struck the Santa Cruz Islands, Solomon Islands, killing 10 people and displacing over 4700. Approach A post-disaster assessment of the risk of epidemic disease transmission recommended the implementation of an early warning alert and response network (EWARN) to rapidly detect, assess and respond to potential outbreaks in the aftermath of the tsunami. Local setting Almost 40% of the Santa Cruz Islands’ population were displaced by the disaster, and living in cramped temporary camps with poor or absent sanitation facilities and insufficient access to clean water. There was no early warning disease surveillance system. Relevant changes By 25 February, an EWARN was operational in five health facilities that served 90% of the displaced population. Eight priority diseases or syndromes were reported weekly; unexpected health events were reported immediately. Between 25 February and 19 May, 1177 target diseases or syndrome cases were reported. Seven alerts were investigated. No sustained transmission or epidemics were identified. Reporting compliance was 85%. The EWARN was then transitioned to the routine four-syndrome early warning disease surveillance system. Lesson learnt It was necessary to conduct a detailed assessment to evaluate the risk and potential impact of serious infectious disease outbreaks, to assess whether and how enhanced early warning disease surveillance should be implemented. Local capacities and available resources should be considered in planning EWARN implementation. An EWARN can be an opportunity to establish or strengthen early warning disease surveillance capabilities. PMID:25378746

Effects of stressor characteristics on early warning signs of critical transitions and "critical coupling" in complex dynamical systems.

PubMed

Blume, Steffen O P; Sansavini, Giovanni

2017-12-01

Complex dynamical systems face abrupt transitions into unstable and catastrophic regimes. These critical transitions are triggered by gradual modifications in stressors, which push the dynamical system towards unstable regimes. Bifurcation analysis can characterize such critical thresholds, beyond which systems become unstable. Moreover, the stochasticity of the external stressors causes small-scale fluctuations in the system response. In some systems, the decomposition of these signal fluctuations into precursor signals can reveal early warning signs prior to the critical transition. Here, we present a dynamical analysis of a power system subjected to an increasing load level and small-scale stochastic load perturbations. We show that the auto- and cross-correlations of bus voltage magnitudes increase, leading up to a Hopf bifurcation point, and further grow until the system collapses. This evidences a gradual transition into a state of "critical coupling," which is complementary to the established concept of "critical slowing down." Furthermore, we analyze the effects of the type of load perturbation and load characteristics on early warning signs and find that gradient changes in the autocorrelation provide early warning signs of the imminent critical transition under white-noise but not for auto-correlated load perturbations. Furthermore, the cross-correlation between all voltage magnitude pairs generally increases prior to and beyond the Hopf bifurcation point, indicating "critical coupling," but cannot provide early warning indications. Finally, we show that the established early warning indicators are oblivious to limit-induced bifurcations and, in the case of the power system model considered here, only react to an approaching Hopf bifurcation.

The effect of adult Early Warning Systems education on nurses' knowledge, confidence and clinical performance: A systematic review.

PubMed

Saab, Mohamad M; McCarthy, Bridie; Andrews, Tom; Savage, Eileen; Drummond, Frances J; Walshe, Nuala; Forde, Mary; Breen, Dorothy; Henn, Patrick; Drennan, Jonathan; Hegarty, Josephine

2017-11-01

This review aims to determine the effect of adult Early Warning Systems education on nurses' knowledge, confidence and clinical performance. Early Warning Systems support timely identification of clinical deterioration and prevention of avoidable deaths. Several educational programmes have been designed to help nurses recognize and manage deteriorating patients. Little is known as to the effectiveness of these programmes. Systematic review. Academic Search Complete, CINAHL, MEDLINE, PsycINFO, PsycARTICLES, Psychology and Behavioral Science Collection, SocINDEX and the UK & Ireland Reference Centre, EMBASE, the Turning Research Into Practice database, the Cochrane Central Register of Controlled Trials (CENTRAL) and Grey Literature sources were searched between October and November 2015. This is a quantitative systematic review using Cochrane methods. Studies published between January 2011 - November 2015 in English were sought. The risk of bias, level of evidence and the quality of evidence per outcome were assessed. Eleven articles with 10 studies were included. Nine studies addressed clinical performance, four addressed knowledge and two addressed confidence. Knowledge, vital signs recording and Early Warning Score calculation were improved in the short term. Two interventions had no effect on nurses' response to clinical deterioration and use of communication tools. This review highlights the importance of measuring outcomes using standardized tools and valid and reliable instruments. Using longitudinal designs, researchers are encouraged to investigate the effect of Early Warning Systems educational programmes. These can include interactive e-learning, on-site interdisciplinary Early Warning Scoring systems training sessions and simulated scenarios. © 2017 John Wiley & Sons Ltd.

Early warning reporting categories analysis of recall and complaints data.

DOT National Transportation Integrated Search

2001-12-31

This analysis was performed to assist the National Highway Traffic Safety Administration (NHTSA) in identifying components and systems to be included in early warning reporting (EWR) categories that would be based upon historical safety-related recal...

Putting Man in the Machine: Exploiting Expertise to Enhance Multiobjective Design of Water Supply Monitoring Network

NASA Astrophysics Data System (ADS)

Bode, F.; Nowak, W.; Reed, P. M.; Reuschen, S.

2016-12-01

Drinking-water well catchments need effective early-warning monitoring networks. Groundwater water supply wells in complex urban environments are in close proximity to a myriad of potential industrial pollutant sources that could irreversibly damage their source aquifers. These urban environments pose fiscal and physical challenges to designing monitoring networks. Ideal early-warning monitoring networks would satisfy three objectives: to detect (1) all potential contaminations within the catchment (2) as early as possible before they reach the pumping wells, (3) while minimizing costs. Obviously, the ideal case is nonexistent, so we search for tradeoffs using multiobjective optimization. The challenge of this optimization problem is the high number of potential monitoring-well positions (the search space) and the non-linearity of the underlying groundwater flow-and-transport problem. This study evaluates (1) different ways to effectively restrict the search space in an efficient way, with and without expert knowledge, (2) different methods to represent the search space during the optimization and (3) the influence of incremental increases in uncertainty in the system. Conductivity, regional flow direction and potential source locations are explored as key uncertainties. We show the need and the benefit of our methods by comparing optimized monitoring networks for different uncertainty levels with networks that seek to effectively exploit expert knowledge. The study's main contributions are the different approaches restricting and representing the search space. The restriction algorithms are based on a point-wise comparison of decision elements of the search space. The representation of the search space can be either binary or continuous. For both cases, the search space must be adjusted properly. Our results show the benefits and drawbacks of binary versus continuous search space representations and the high potential of automated search space restriction algorithms for high-dimensional, highly non-linear optimization problems.

New insights into flood warning reception and emergency response by affected parties

NASA Astrophysics Data System (ADS)

Kreibich, Heidi; Müller, Meike; Schröter, Kai; Thieken, Annegret H.

2017-11-01

Flood damage can be mitigated if the parties at risk are reached by flood warnings and if they know how to react appropriately. To gain more knowledge about warning reception and emergency response of private households and companies, surveys were undertaken after the August 2002 and the June 2013 floods in Germany. Despite pronounced regional differences, the results show a clear overall picture: in 2002, early warnings did not work well; e.g. many households (27 %) and companies (45 %) stated that they had not received any flood warnings. Additionally, the preparedness of private households and companies was low in 2002, mainly due to a lack of flood experience. After the 2002 flood, many initiatives were launched and investments undertaken to improve flood risk management, including early warnings and an emergency response in Germany. In 2013, only a small share of the affected households (5 %) and companies (3 %) were not reached by any warnings. Additionally, private households and companies were better prepared. For instance, the share of companies which have an emergency plan in place has increased from 10 % in 2002 to 34 % in 2013. However, there is still room for improvement, which needs to be triggered mainly by effective risk and emergency communication. The challenge is to continuously maintain and advance an integrated early warning and emergency response system even without the occurrence of extreme floods.

Availability and Reliability of Disaster Early Warning Systems and the IT Infrastructure Library

NASA Astrophysics Data System (ADS)

Wächter, J.; Loewe, P.

2012-12-01

The Boxing Day Tsunami of 2004 caused an information catastrophy. Crucial early warning information could not be delivered to the communities under imminent threat, resulting in over 240,000 casualties in 14 countries. This tragedy sparked the development of a new generation of integrated modular Tsunami Early Warning Systems (TEWS). While significant advances were accomplished in the past years, recent events, like the Chile 2010 and the Tohoku 2011 tsunami demonstrate that the key technical challenge for Tsunami Early Warning research on the supranational scale still lies in the timely issuing of status information and reliable early warning messages. A key challenge stems from the main objective of the IOC Tsunami Programme, the integration of national TEWS towards ocean-wide networks: Each of the increasing number of integrated Tsunami Early Warning Centres has to cope with the continuing evolution of sensors, hardware and software while having to maintain reliable inter-center information exchange services. To avoid future information catastrophes, the performance of all components, ranging from sensors to Warning Centers, has to be regularly validated against defined criteria. This task is complicated by the fact that in term of ICT system life cycles tsunami are very rare event resulting in very difficult framing conditions to safeguard the availability and reliability of TWS. Since 2004, GFZ German Research Centre for Geosciences (GFZ) has built up expertise in the field of TEWS. Within GFZ, the Centre for GeoInformation Technology (CEGIT) has focused its work on the geoinformatics aspects of TEWS in two projects already: The German Indonesian Tsunami Early Warning System (GITEWS) funded by the German Federal Ministry of Education and Research (BMBF) and the Distant Early Warning System (DEWS), a European project funded under the sixth Framework Programme (FP6). These developments are continued in the TRIDEC project (Collaborative, Complex, and Critical Decision Processes in Evolving Crises) funded under the European Union's seventh Framework Programme (FP7). This ongoing project focuses on real-time intelligent information management in Earth management and its long-term application. All technical development in TRIDEC is based on mature system architecture models and industry standards. The use of standards applies also to the operation of individual TRIDEC reference installations and their interlinking into an integrated service infrastructure for supranational warning services: A set of best practices for IT service management is used to align the TEWS software services with the requirements by the Early Warning Centre management by defining Service Level Agreements (SLA) and ensuring appliance. For this, the concept of service lifecycles is adapted for the TEWS domain, which is laid out in the IT Infrastructure Library (ITIL) by the United Kingdom's Office of Government Commerce (OGC). The cyclic procedures, tasks and checklists described by ITIL are used to establish a baseline to plan, implement, and maintain TEWS service components in the long run. This allows to ensure compliance with given international TEWS standards and to measure improvement of the provided services against a gold-standard.

eqMAXEL: A new automatic earthquake location algorithm implementation for Earthworm

NASA Astrophysics Data System (ADS)

Lisowski, S.; Friberg, P. A.; Sheen, D. H.

2017-12-01

A common problem with automated earthquake location systems for a local to regional scale seismic network is false triggering and false locations inside the network caused by larger regional to teleseismic distance earthquakes. This false location issue also presents a problem for earthquake early warning systems where societal impacts of false alarms can be very expensive. Towards solving this issue, Sheen et al. (2016) implemented a robust maximum-likelihood earthquake location algorithm known as MAXEL. It was shown with both synthetics and real-data for a small number of arrivals, that large regional events were easily identifiable through metrics in the MAXEL algorithm. In the summer of 2017, we collaboratively implemented the MAXEL algorithm into a fully functional Earthworm module and tested it in regions of the USA where false detections and alarming are observed. We show robust improvement in the ability of the Earthworm system to filter out regional and teleseismic events that would have falsely located inside the network using the traditional Earthworm hypoinverse solution. We also explore using different grid sizes in the implementation of the MAXEL algorithm, which was originally designed with South Korea as the target network size.

Nonstationary EO/IR Clutter Suppression and Dim Object Tracking

NASA Astrophysics Data System (ADS)

Tartakovsky, A.; Brown, A.; Brown, J.

2010-09-01

We develop and evaluate the performance of advanced algorithms which provide significantly improved capabilities for automated detection and tracking of ballistic and flying dim objects in the presence of highly structured intense clutter. Applications include ballistic missile early warning, midcourse tracking, trajectory prediction, and resident space object detection and tracking. The set of algorithms include, in particular, adaptive spatiotemporal clutter estimation-suppression and nonlinear filtering-based multiple-object track-before-detect. These algorithms are suitable for integration into geostationary, highly elliptical, or low earth orbit scanning or staring sensor suites, and are based on data-driven processing that adapts to real-world clutter backgrounds, including celestial, earth limb, or terrestrial clutter. In many scenarios of interest, e.g., for highly elliptic and, especially, low earth orbits, the resulting clutter is highly nonstationary, providing a significant challenge for clutter suppression to or below sensor noise levels, which is essential for dim object detection and tracking. We demonstrate the success of the developed algorithms using semi-synthetic and real data. In particular, our algorithms are shown to be capable of detecting and tracking point objects with signal-to-clutter levels down to 1/1000 and signal-to-noise levels down to 1/4.

REWSET: A prototype seismic and tsunami early warning system in Rhodes island, Greece

NASA Astrophysics Data System (ADS)

Papadopoulos, Gerasimos; Argyris, Ilias; Aggelou, Savvas; Karastathis, Vasilis

2014-05-01

Tsunami warning in near-field conditions is a critical issue in the Mediterranean Sea since the most important tsunami sources are situated within tsunami wave travel times starting from about five minutes. The project NEARTOWARN (2012-2013) supported by the EU-DG ECHO contributed substantially to the development of new tools for the near-field tsunami early warning in the Mediterranean. One of the main achievements is the development of a local warning system in the test-site of Rhodes island (Rhodes Early Warning System for Earthquakes and Tsunamis - REWSET). The system is composed by three main subsystems: (1) a network of eight seismic early warning devices installed in four different localities of the island, one in the civil protection, another in the Fire Brigade and another two in municipality buildings; (2) two radar-type (ultrasonic) tide-gauges installed in the eastern coastal zine of the island which was selected since research on the historical earthquake and tsunami activity has indicated that the most important, near-field tsunami sources are situated offshore to the east of Rhodes; (3) a crisis Geographic Management System (GMS), which is a web-based and GIS-based application incorporating a variety of thematic maps and other information types. The seismic early warning devices activate by strong (magnitude around 6 or more) earthquakes occurring at distances up to about 100 km from Rhodes, thus providing immediate mobilization of the civil protection. The tide-gauges transmit sea level data, while during the crisis the GMS supports decisions to be made by civil protection. In the near future it is planned the REWSET system to be integrated with national and international systems. REWSET is a prototype which certainly could be developed in other coastal areas of the Mediterranean and beyond.

Seismic Monitoring of Permafrost During Controlled Thaw: An Active-Source Experiment Using a Surface Orbital Vibrator and Fiber-Optic DAS Arrays

NASA Astrophysics Data System (ADS)

Dou, S.; Wood, T.; Lindsey, N.; Ajo Franklin, J. B.; Freifeld, B. M.; Gelvin, A.; Morales, A.; Saari, S.; Ekblaw, I.; Wagner, A. M.; Daley, T. M.; Robertson, M.; Martin, E. R.; Ulrich, C.; Bjella, K.

2016-12-01

Thawing of permafrost can cause ground deformations that threaten the integrity of civil infrastructure. It is essential to develop early warning systems that can identify critically warmed permafrost and issue warnings for hazard prevention and control. Seismic methods can play a pivotal role in such systems for at least two reasons: First, seismic velocities are indicative of mechanical strength of the subsurface and thus are directly relevant to engineering properties; Second, seismic velocities in permafrost systems are sensitive to pre-thaw warming, which makes it possible to issue early warnings before the occurrence of hazardous subsidence events. However, several questions remain: What are the seismic signatures that can be effectively used for early warning of permafrost thaw? Can seismic methods provide enough warning times for hazard prevention and control? In this study, we investigate the feasibility of using permanently installed seismic networks for early warnings of permafrost thaw. We conducted continuous active-source seismic monitoring of permafrost that was under controlled heating at CRREL's Fairbanks permafrost experiment station. We used a permanently installed surface orbital vibrator (SOV) as source and surface-trenched DAS arrays as receivers. The SOV is characterized by its excellent repeatability, automated operation, high energy level, and the rich frequency content (10-100 Hz) of the generated wavefields. The fiber-optic DAS arrays allow continuous recording of seismic data with dense spatial sampling (1-meter channel spacing), low cost, and low maintenance. This combination of SOV-DAS provides unique seismic datasets for observing time-lapse changes of warming permafrost at the field scale, hence providing an observational basis for design and development of early warning systems for permafrost thaw.

Evaluation of Real-Time and Off-Line Performance of the Virtual Seismologist Earthquake Early Warning Algorithm in Switzerland

NASA Astrophysics Data System (ADS)

Behr, Yannik; Clinton, John; Cua, Georgia; Cauzzi, Carlo; Heimers, Stefan; Kästli, Philipp; Becker, Jan; Heaton, Thomas

2013-04-01

The Virtual Seismologist (VS) method is a Bayesian approach to regional network-based earthquake early warning (EEW) originally formulated by Cua and Heaton (2007). Implementation of VS into real-time EEW codes has been an on-going effort of the Swiss Seismological Service at ETH Zürich since 2006, with support from ETH Zürich, various European projects, and the United States Geological Survey (USGS). VS is one of three EEW algorithms that form the basis of the California Integrated Seismic Network (CISN) ShakeAlert system, a USGS-funded prototype end-to-end EEW system that could potentially be implemented in California. In Europe, VS is currently operating as a real-time test system in Switzerland. As part of the on-going EU project REAKT (Strategies and Tools for Real-Time Earthquake Risk Reduction), VS installations in southern Italy, western Greece, Istanbul, Romania, and Iceland are planned or underway. In Switzerland, VS has been running in real-time on stations monitored by the Swiss Seismological Service (including stations from Austria, France, Germany, and Italy) since 2010. While originally based on the Earthworm system it has recently been ported to the SeisComp3 system. Besides taking advantage of SeisComp3's picking and phase association capabilities it greatly simplifies the potential installation of VS at networks in particular those already running SeisComp3. We present the architecture of the new SeisComp3 based version and compare its results from off-line tests with the real-time performance of VS in Switzerland over the past two years. We further show that the empirical relationships used by VS to estimate magnitudes and ground motion, originally derived from southern California data, perform well in Switzerland.

Total Lightning Characteristics with Respect to Radar-Derived Mesocyclone Strength

NASA Technical Reports Server (NTRS)

Stough, Sarah M.; Carey, Lawrence D.; Schultz, Christopher J.

2015-01-01

Recent work investigating the microphysical and kinematic relationship between a storm's updraft, its total lightning production, and manifestations of severe weather has resulted in development of tools for improved nowcasting of storm intensity. The total lightning jump algorithm, which identifies rapid increases in total lightning flash rate that often precede severe events, has shown particular potential to benefit warning operations. Maximizing this capability of total lightning and its operational implementation via the lightning jump may best be done through its fusion with radar and radar-derived intensity metrics. Identification of a mesocyclone, or quasi-steady rotating updraft, in Doppler velocity is the predominant radar-inferred early indicator of severe potential in a convective storm. Fused lightning-radar tools that capitalize on the most robust intensity indicators would allow enhanced situational awareness for increased warning confidence. A foundational step toward such tools comes from a better understanding of the updraft-centric relationship between intensification of total lightning production and mesocyclone development and strength. The work presented here utilizes a sample of supercell case studies representing a spectrum of severity. These storms are analyzed with respect to total lightning flash rate and the lightning jump alongside mesocyclone strength derived objectively from the National Severe Storms Laboratory (NSSL) Mesocyclone Detection Algorithm (MDA) and maximum azimuthal shear through a layer. Early results indicate that temporal similarities exist in the trends between total lightning flash rate and low- to mid-level rotation in supercells. Other characteristics such as polarimetric signatures of rotation, flash size, and cloud-to-ground flash ratio are explored for added insight into the significance of these trends with respect to the updraft and related processes of severe weather production.

Earthquake Early Warning: Real-time Testing of an On-site Method Using Waveform Data from the Southern California Seismic Network

NASA Astrophysics Data System (ADS)

Solanki, K.; Hauksson, E.; Kanamori, H.; Wu, Y.; Heaton, T.; Boese, M.

2007-12-01

We have implemented an on-site early warning algorithm using the infrastructure of the Caltech/USGS Southern California Seismic Network (SCSN). We are evaluating the real-time performance of the software system and the algorithm for rapid assessment of earthquakes. In addition, we are interested in understanding what parts of the SCSN need to be improved to make early warning practical. Our EEW processing system is composed of many independent programs that process waveforms in real-time. The codes were generated by using a software framework. The Pd (maximum displacement amplitude of P wave during the first 3sec) and Tau-c (a period parameter during the first 3 sec) values determined during the EEW processing are being forwarded to the California Integrated Seismic Network (CISN) web page for independent evaluation of the results. The on-site algorithm measures the amplitude of the P-wave (Pd) and the frequency content of the P-wave during the first three seconds (Tau-c). The Pd and the Tau-c values make it possible to discriminate between a variety of events such as large distant events, nearby small events, and potentially damaging nearby events. The Pd can be used to infer the expected maximum ground shaking. The method relies on data from a single station although it will become more reliable if readings from several stations are associated. To eliminate false triggers from stations with high background noise level, we have created per station Pd threshold configuration for the Pd/Tau-c algorithm. To determine appropriate values for the Pd threshold we calculate Pd thresholds for stations based on the information from the EEW logs. We have operated our EEW test system for about a year and recorded numerous earthquakes in the magnitude range from M3 to M5. Two recent examples are a M4.5 earthquake near Chatsworth and a M4.7 earthquake near Elsinore. In both cases, the Pd and Tau-c parameters were determined successfully within 10 to 20 sec of the arrival of the P-wave at the station. The Tau-c values predicted the magnitude within 0.1 and the predicted average peak-ground-motion was 0.7 cm/s and 0.6 cm/s. The delays in the system are caused mostly by the packetizing delay because our software system is based on processing miniseed packets. Most recently we have begun reducing the data latency using new qmaserv2 software for the Q330 Quanterra datalogger. We implemented qmaserv2 based multicast receiver software to receive the native 1 sec packets from the dataloggers. The receiver reads multicast packets from the network and writes them into shared memory area. This new software will fully take advantage of the capabilities of the Q330 datalogger and significantly reduce data latency for EEW system. We have also implemented a new EEW sub-system that compliments the currently running EEW system by associating Pd and Tau-c values from multiple stations. So far, we have implemented a new trigger generation algorithm for real-time processing for the sub-system, and are able to routinely locate events and determine magnitudes using the Pd and Tau-c values.

77 FR 55605 - Early Warning Reporting, Foreign Defect Reporting, and Motor Vehicle and Equipment Recall...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-09-10

... requires quarterly reporting of early warning information: Production information; information on incidents... manufacturers, and other equipment manufacturers) and the annual production of the entity. The EWR information... vehicle type as part of [[Page 55608

Coral Reef Early Warning System (CREWS) RPC Experiment

NASA Technical Reports Server (NTRS)

Estep, Leland; Spruce, Joseph P.; Hall, Callie

2007-01-01

This viewgraph document reviews the background, objectives, methodology, validation, and present status of the Coral Reef Early Warning System (CREWS) Rapid Prototyping Capability (RPC) experiment. The potential NASA contribution to CREWS Decision Support Tool (DST) centers on remotely sensed imagery products.

Improving Correlation Algorithms to Detect and Characterize Smaller Magnitude Induced Seismicity Swarms

NASA Astrophysics Data System (ADS)

Skoumal, R.; Brudzinski, M.; Currie, B.

2015-12-01

Induced seismic sequences often occur as swarms that can include thousands of small (< M 2) earthquakes. While the identification of this microseismicity would invariably aid in the characterization and modeling of induced sequences, traditional earthquake detection techniques often provide incomplete catalogs, even when local networks are deployed. Because induced sequences often include scores of micro-earthquakes that prelude larger magnitude events, the identification of these small magnitude events would be crucial for the early identification of induced sequences. By taking advantage of the repeating, swarm-like nature of induced seismicity, a more robust catalog can be created using complementary correlation algorithms in near real-time without the reliance on traditional earthquake detection and association routines. Since traditional earthquake catalog methodologies using regional networks have a relatively high detection threshold (M 2+), we have sought to develop correlation routines that can detect smaller magnitude sequences. While short-term/long-term amplitude average detection algorithms requires significant signal-to-noise ratio at multiple stations for confident identification, a correlation detector is capable of identifying earthquakes with high confidence using just a single station. The result is an embarrassingly parallel task that can be employed for a network to be used as an early warning system for potentially induced seismicity while also better characterizing tectonic sequences beyond what traditional methods allow.

Early warning systems for the management of chronic heart failure: a systematic literature review of cost-effectiveness models.

PubMed

Albuquerque De Almeida, Fernando; Al, Maiwenn; Koymans, Ron; Caliskan, Kadir; Kerstens, Ankie; Severens, Johan L

2018-04-01

Describing the general and methodological characteristics of decision-analytical models used in the economic evaluation of early warning systems for the management of chronic heart failure patients and performing a quality assessment of their methodological characteristics is expected to provide concise and useful insight to inform the future development of decision-analytical models in the field of heart failure management. Areas covered: The literature on decision-analytical models for the economic evaluation of early warning systems for the management of chronic heart failure patients was systematically reviewed. Nine electronic databases were searched through the combination of synonyms for heart failure and sensitive filters for cost-effectiveness and early warning systems. Expert commentary: The retrieved models show some variability with regards to their general study characteristics. Overall, they display satisfactory methodological quality, even though some points could be improved, namely on the consideration and discussion of any competing theories regarding model structure and disease progression, identification of key parameters and the use of expert opinion, and uncertainty analyses. A comprehensive definition of early warning systems and further research under this label should be pursued. To improve the transparency of economic evaluation publications, authors should make available detailed technical information regarding the published models.

Community-based early warning systems for flood risk mitigation in Nepal

NASA Astrophysics Data System (ADS)

Smith, Paul J.; Brown, Sarah; Dugar, Sumit

2017-03-01

This paper focuses on the use of community-based early warning systems for flood resilience in Nepal. The first part of the work outlines the evolution and current status of these community-based systems, highlighting the limited lead times currently available for early warning. The second part of the paper focuses on the development of a robust operational flood forecasting methodology for use by the Nepal Department of Hydrology and Meteorology (DHM) to enhance early warning lead times. The methodology uses data-based physically interpretable time series models and data assimilation to generate probabilistic forecasts, which are presented in a simple visual tool. The approach is designed to work in situations of limited data availability with an emphasis on sustainability and appropriate technology. The successful application of the forecast methodology to the flood-prone Karnali River basin in western Nepal is outlined, increasing lead times from 2-3 to 7-8 h. The challenges faced in communicating probabilistic forecasts to the last mile of the existing community-based early warning systems across Nepal is discussed. The paper concludes with an assessment of the applicability of this approach in basins and countries beyond Karnali and Nepal and an overview of key lessons learnt from this initiative.

Feasibility of Using Elastic Wave Velocity Monitoring for Early Warning of Rainfall-Induced Slope Failure

PubMed Central

Chen, Yulong; Irfan, Muhammad; Uchimura, Taro; Zhang, Ke

2018-01-01

Rainfall-induced landslides are one of the most widespread slope instability phenomena posing a serious risk to public safety worldwide so that their temporal prediction is of great interest to establish effective warning systems. The objective of this study is to determine the effectiveness of elastic wave velocities in the surface layer of the slope in monitoring, prediction and early warning of landslide. The small-scale fixed and varied, and large-scale slope model tests were conducted. Analysis of the results has established that the elastic wave velocity continuously decreases in response of moisture content and deformation and there was a distinct surge in the decrease rate of wave velocity when failure was initiated. Based on the preliminary results of this analysis, the method using the change in elastic wave velocity proves superior for landslide early warning and suggests that a warning be issued at switch of wave velocity decrease rate. PMID:29584699

Detection of severe respiratory disease epidemic outbreaks by CUSUM-based overcrowd-severe-respiratory-disease-index model.

PubMed

Polanco, Carlos; Castañón-González, Jorge Alberto; Macías, Alejandro E; Samaniego, José Lino; Buhse, Thomas; Villanueva-Martínez, Sebastián

2013-01-01

A severe respiratory disease epidemic outbreak correlates with a high demand of specific supplies and specialized personnel to hold it back in a wide region or set of regions; these supplies would be beds, storage areas, hemodynamic monitors, and mechanical ventilators, as well as physicians, respiratory technicians, and specialized nurses. We describe an online cumulative sum based model named Overcrowd-Severe-Respiratory-Disease-Index based on the Modified Overcrowd Index that simultaneously monitors and informs the demand of those supplies and personnel in a healthcare network generating early warnings of severe respiratory disease epidemic outbreaks through the interpretation of such variables. A post hoc historical archive is generated, helping physicians in charge to improve the transit and future allocation of supplies in the entire hospital network during the outbreak. The model was thoroughly verified in a virtual scenario, generating multiple epidemic outbreaks in a 6-year span for a 13-hospital network. When it was superimposed over the H1N1 influenza outbreak census (2008-2010) taken by the National Institute of Medical Sciences and Nutrition Salvador Zubiran in Mexico City, it showed that it is an effective algorithm to notify early warnings of severe respiratory disease epidemic outbreaks with a minimal rate of false alerts.

Applying the zero-inflated Poisson model with random effects to detect abnormal rises in school absenteeism indicating infectious diseases outbreak.

PubMed

Song, X X; Zhao, Q; Tao, T; Zhou, C M; Diwan, V K; Xu, B

2018-05-30

Records of absenteeism from primary schools are valuable data for infectious diseases surveillance. However, the analysis of the absenteeism is complicated by the data features of clustering at zero, non-independence and overdispersion. This study aimed to generate an appropriate model to handle the absenteeism data collected in a European Commission granted project for infectious disease surveillance in rural China and to evaluate the validity and timeliness of the resulting model for early warnings of infectious disease outbreak. Four steps were taken: (1) building a 'well-fitting' model by the zero-inflated Poisson model with random effects (ZIP-RE) using the absenteeism data from the first implementation year; (2) applying the resulting model to predict the 'expected' number of absenteeism events in the second implementation year; (3) computing the differences between the observations and the expected values (O-E values) to generate an alternative series of data; (4) evaluating the early warning validity and timeliness of the observational data and model-based O-E values via the EARS-3C algorithms with regard to the detection of real cluster events. The results indicate that ZIP-RE and its corresponding O-E values could improve the detection of aberrations, reduce the false-positive signals and are applicable to the zero-inflated data.

Design of a Fatigue Detection System for High-Speed Trains Based on Driver Vigilance Using a Wireless Wearable EEG

PubMed Central

Zhang, Xiaoliang; Li, Jiali; Liu, Yugang; Zhang, Zutao; Wang, Zhuojun; Luo, Dianyuan; Zhou, Xiang; Zhu, Miankuan; Salman, Waleed; Hu, Guangdi; Wang, Chunbai

2017-01-01

The vigilance of the driver is important for railway safety, despite not being included in the safety management system (SMS) for high-speed train safety. In this paper, a novel fatigue detection system for high-speed train safety based on monitoring train driver vigilance using a wireless wearable electroencephalograph (EEG) is presented. This system is designed to detect whether the driver is drowsiness. The proposed system consists of three main parts: (1) a wireless wearable EEG collection; (2) train driver vigilance detection; and (3) early warning device for train driver. In the first part, an 8-channel wireless wearable brain-computer interface (BCI) device acquires the locomotive driver’s brain EEG signal comfortably under high-speed train-driving conditions. The recorded data are transmitted to a personal computer (PC) via Bluetooth. In the second step, a support vector machine (SVM) classification algorithm is implemented to determine the vigilance level using the Fast Fourier transform (FFT) to extract the EEG power spectrum density (PSD). In addition, an early warning device begins to work if fatigue is detected. The simulation and test results demonstrate the feasibility of the proposed fatigue detection system for high-speed train safety. PMID:28257073

Detection of Severe Respiratory Disease Epidemic Outbreaks by CUSUM-Based Overcrowd-Severe-Respiratory-Disease-Index Model

PubMed Central

Castañón-González, Jorge Alberto; Macías, Alejandro E.; Samaniego, José Lino; Buhse, Thomas; Villanueva-Martínez, Sebastián

2013-01-01

A severe respiratory disease epidemic outbreak correlates with a high demand of specific supplies and specialized personnel to hold it back in a wide region or set of regions; these supplies would be beds, storage areas, hemodynamic monitors, and mechanical ventilators, as well as physicians, respiratory technicians, and specialized nurses. We describe an online cumulative sum based model named Overcrowd-Severe-Respiratory-Disease-Index based on the Modified Overcrowd Index that simultaneously monitors and informs the demand of those supplies and personnel in a healthcare network generating early warnings of severe respiratory disease epidemic outbreaks through the interpretation of such variables. A post hoc historical archive is generated, helping physicians in charge to improve the transit and future allocation of supplies in the entire hospital network during the outbreak. The model was thoroughly verified in a virtual scenario, generating multiple epidemic outbreaks in a 6-year span for a 13-hospital network. When it was superimposed over the H1N1 influenza outbreak census (2008–2010) taken by the National Institute of Medical Sciences and Nutrition Salvador Zubiran in Mexico City, it showed that it is an effective algorithm to notify early warnings of severe respiratory disease epidemic outbreaks with a minimal rate of false alerts. PMID:24069063

Future of Earthquake Early Warning: Quantifying Uncertainty and Making Fast Automated Decisions for Applications

NASA Astrophysics Data System (ADS)

Wu, Stephen

Earthquake early warning (EEW) systems have been rapidly developing over the past decade. Japan Meteorological Agency (JMA) has an EEW system that was operating during the 2011 M9 Tohoku earthquake in Japan, and this increased the awareness of EEW systems around the world. While longer-time earthquake prediction still faces many challenges to be practical, the availability of shorter-time EEW opens up a new door for earthquake loss mitigation. After an earthquake fault begins rupturing, an EEW system utilizes the first few seconds of recorded seismic waveform data to quickly predict the hypocenter location, magnitude, origin time and the expected shaking intensity level around the region. This early warning information is broadcast to different sites before the strong shaking arrives. The warning lead time of such a system is short, typically a few seconds to a minute or so, and the information is uncertain. These factors limit human intervention to activate mitigation actions and this must be addressed for engineering applications of EEW. This study applies a Bayesian probabilistic approach along with machine learning techniques and decision theories from economics to improve different aspects of EEW operation, including extending it to engineering applications. Existing EEW systems are often based on a deterministic approach. Often, they assume that only a single event occurs within a short period of time, which led to many false alarms after the Tohoku earthquake in Japan. This study develops a probability-based EEW algorithm based on an existing deterministic model to extend the EEW system to the case of concurrent events, which are often observed during the aftershock sequence after a large earthquake. To overcome the challenge of uncertain information and short lead time of EEW, this study also develops an earthquake probability-based automated decision-making (ePAD) framework to make robust decision for EEW mitigation applications. A cost-benefit model that can capture the uncertainties in EEW information and the decision process is used. This approach is called the Performance-Based Earthquake Early Warning, which is based on the PEER Performance-Based Earthquake Engineering method. Use of surrogate models is suggested to improve computational efficiency. Also, new models are proposed to add the influence of lead time into the cost-benefit analysis. For example, a value of information model is used to quantify the potential value of delaying the activation of a mitigation action for a possible reduction of the uncertainty of EEW information in the next update. Two practical examples, evacuation alert and elevator control, are studied to illustrate the ePAD framework. Potential advanced EEW applications, such as the case of multiple-action decisions and the synergy of EEW and structural health monitoring systems, are also discussed.

Linking Research to Practice: FEWS NET and Its Use of Satellite Remote Sensing Data

NASA Technical Reports Server (NTRS)

Brown, Molly E.; Brickley, Elizabeth B.

2011-01-01

The purpose of the Famine Early Warning Systems Network (FEWS NET) is to collaborate with international, regional and national partners to provide timely and rigorous early warning and vulnerability information on emerging and evolving food security issues

Volvo and Infiniti drivers' experiences with select crash avoidance technologies.

PubMed

Braitman, Keli A; McCartt, Anne T; Zuby, David S; Singer, Jeremiah

2010-06-01

Vehicle-based crash avoidance systems can potentially reduce crashes, but success depends on driver acceptance and understanding. This study gauged driver use, experience, and acceptance among early adopters of select technologies. Telephone interviews were conducted in early 2009 with 380 owners of Volvo vehicles equipped with forward collision warning with autobrake, lane departure warning, side-view assist, and/or active bi-xenon headlights and 485 owners of Infiniti vehicles with lane departure warning/prevention. Most owners kept systems turned on most of the time, especially forward collision warning with autobrake and side-view assist. The exception was lane departure prevention; many owners were unaware they had it, and the system must be activated each time the vehicle is started. Most owners reported being safer with the technologies and would want them again on their next vehicles. Perceived false or unnecessary warnings were fairly common, particularly with side-view assist. Some systems were annoying, especially lane departure warning. Many owners reported safer driving behaviors such as greater use of turn signals (lane departure warning), increased following distance (forward collision warning), and checking side mirrors more frequently (side-view assist), but some reported driving faster at night (active headlights). Despite some unnecessary or annoying warnings, most Volvo and Infiniti owners use crash avoidance systems most of the time. Among early adopters, the first requirement of effective warning systems (that owners use the technology) seems largely met. Systems requiring activation by drivers for each trip are used less often. Owner experience with the latest technologies from other automobile manufacturers should be studied, as well as for vehicles on which technologies are standard (versus optional) equipment. The effectiveness of technologies in preventing and mitigating crashes and injuries, and user acceptance of interfaces, should be examined as more vehicles with advanced technologies penetrate the fleet.

[Reliability and validity of warning signs checklist for screening psychological, behavioral and developmental problems of children].

PubMed

Huang, X N; Zhang, Y; Feng, W W; Wang, H S; Cao, B; Zhang, B; Yang, Y F; Wang, H M; Zheng, Y; Jin, X M; Jia, M X; Zou, X B; Zhao, C X; Robert, J; Jing, Jin

2017-06-02

Objective: To evaluate the reliability and validity of warning signs checklist developed by the National Health and Family Planning Commission of the People's Republic of China (NHFPC), so as to determine the screening effectiveness of warning signs on developmental problems of early childhood. Method: Stratified random sampling method was used to assess the reliability and validity of checklist of warning sign and 2 110 children 0 to 6 years of age(1 513 low-risk subjects and 597 high-risk subjects) were recruited from 11 provinces of China. The reliability evaluation for the warning signs included the test-retest reliability and interrater reliability. With the use of Age and Stage Questionnaire (ASQ) and Gesell Development Diagnosis Scale (GESELL) as the criterion scales, criterion validity was assessed by determining the correlation and consistency between the screening results of warning signs and the criterion scales. Result: In terms of the warning signs, the screening positive rates at different ages ranged from 10.8%(21/141) to 26.2%(51/137). The median (interquartile) testing time for each subject was 1(0.6) minute. Both the test-retest reliability and interrater reliability of warning signs reached 0.7 or above, indicating that the stability was good. In terms of validity assessment, there was remarkable consistency between ASQ and warning signs, with the Kappa value of 0.63. With the use of GESELL as criterion, it was determined that the sensitivity of warning signs in children with suspected developmental delay was 82.2%, and the specificity was 77.7%. The overall Youden index was 0.6. Conclusion: The reliability and validity of warning signs checklist for screening early childhood developmental problems have met the basic requirements of psychological screening scales, with the characteristics of short testing time and easy operation. Thus, this warning signs checklist can be used for screening psychological and behavioral problems of early childhood, especially in community settings.

Optical Embedded Dust Sensor for Engine Protection and Early Warning on M1 Abrams/Ground Combat Vehicles

DTIC Science & Technology

2012-04-11

warning of seal leakage or deterioration of air filters, thereby reducing engine damage and improving vehicle operational readiness. To be effective , the...for a comprehensive early warning and health management solution. To address the need for an effective dust detector for the AGT1500 engine and M1...an optical dust sensor for real-time continuous monitoring, and its effectiveness in quantitatively measuring dust penetration in the AGT1500 engine

[System construction of early warning for ecological security at cultural and natural heritage mixed sites and its application: a case study of Wuyishan Scenery District].

PubMed

You, Wei-Bin; He, Dong-Jin; Qin, De-Hua; Ji, Zhi-Rong; Wu, Li-Yun; Yu, Jian-An; Chen, Bing-Rong; Tan, Yong

2014-05-01

This paper proposed a new concept of ecological security for protection by a comprehensive analysis of the contents and standards of world heritage sites. A frame concept model named "Pressure-State-Control" for early warning of ecological security at world heritage mixed sites was constructed and evaluation indicators of this frame were also selected. Wuyishan Scenery District was chosen for a case study, which has been severely disturbed by natural and artificial factors. Based on the frame model of "Pressure-State-Control" and by employing extension analysis, the matter-element model was established to assess the ecological security status of this cultural and natural world heritage mixed site. The results showed that the accuracy of ecological security early warning reached 84%. Early warning rank was I level (no alert status) in 1997 and 2009, but that in 2009 had a higher possibility to convert into II level. Likewise, the early-warning indices of sensitive ranks were different between 1997 and 2009. Population density, population growth rate, area index for tea garden, cultivated land owned per capita, level of drought, and investment for ecological and environmental construction were the main limiting factors to hinder the development of ecological security from 2009 to future. In general, the status of Wuyishan Scenery District ecological security was relatively good and considered as no alert level, while risk conditions also existed in terms of a few early-warning indicators. We still need to pay more attention to serious alert indicators and adopt effective prevention and control measures to maintain a good ecological security status of this heritage site.

Refuting the ticagrelor-aspirin black box warning: and proposing a ticagrelor early-PCI black box warning.

PubMed

DiNicolantonio, James J; Serebruany, Victor L; Tomek, Ales

2013-10-03

Ticagrelor, a novel reversible antiplatelet agent, has a black box warning to avoid maintenance doses of aspirin>100mg. However, a significant ticagrelor-early percutaneous coronary intervention (PCI) interaction exists. To discuss the inappropriateness of the black box warning for aspirin doses>100mg with ticagrelor and the appropriateness (and need) for a black box warning for ticagrelor patients needing early (within 24 hours of randomization) PCI. The FDA Complete Response Review for ticagrelor indicates that aspirin doses ≥ 300 mg/daily was not a significant interaction. In the ticagrelor-aspirin ≥ 300 mg cohort, all-cause mortality (through study end) and cardiovascular (CV) mortality (through study end) were not significantly increased (HR=1.27; 95% CI, 0.84-1.93, p=0.262 and HR=1.39; 95% CI:0.87-2.2, p=0.170), respectively. However, in patients treated with early (within 24 hours) PCI, ticagrelor significantly increased all-cause mortality (30 day: HR=1.89; 95% CI: 1.26-2.81, p=0.002, and through study end, HR=1.41; 95% CI,1.08-1.84, p=0.012) and increased CV mortality (30 day: HR=1.31; 95% CI: 0.97-1.77, p=0.075, and through study end, HR=1.35; 95% CI, 0.995-1.82, p=0.054) compared to clopidogrel. Early-PCI was more prevalent in the US versus outside-US regions (61% versus 49%). The black box warning for the use of maintenance aspirin doses over 100mg/daily with ticagrelor is inappropriate and ignores the more important, credible, and highly significant ticagrelor-early PCI adverse interaction in PLATO. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Small-scale (flash) flood early warning in the light of operational requirements: opportunities and limits with regard to user demands, driving data, and hydrologic modeling techniques

NASA Astrophysics Data System (ADS)

Philipp, Andy; Kerl, Florian; Büttner, Uwe; Metzkes, Christine; Singer, Thomas; Wagner, Michael; Schütze, Niels

2016-05-01

In recent years, the Free State of Saxony (Eastern Germany) was repeatedly hit by both extensive riverine flooding, as well as flash flood events, emerging foremost from convective heavy rainfall. Especially after a couple of small-scale, yet disastrous events in 2010, preconditions, drivers, and methods for deriving flash flood related early warning products are investigated. This is to clarify the feasibility and the limits of envisaged early warning procedures for small catchments, hit by flashy heavy rain events. Early warning about potentially flash flood prone situations (i.e., with a suitable lead time with regard to required reaction-time needs of the stakeholders involved in flood risk management) needs to take into account not only hydrological, but also meteorological, as well as communication issues. Therefore, we propose a threefold methodology to identify potential benefits and limitations in a real-world warning/reaction context. First, the user demands (with respect to desired/required warning products, preparation times, etc.) are investigated. Second, focusing on small catchments of some hundred square kilometers, two quantitative precipitation forecasts are verified. Third, considering the user needs, as well as the input parameter uncertainty (i.e., foremost emerging from an uncertain QPF), a feasible, yet robust hydrological modeling approach is proposed on the basis of pilot studies, employing deterministic, data-driven, and simple scoring methods.

The reconnaissance and early-warning optical system design for dual field of space-based "solar blind ultraviolet"

NASA Astrophysics Data System (ADS)

Wang, Wen-cong; Jin, Dong-dong; Shao, Fei; Hu, Hui-jun; Shi, Yu-feng; Song, Juan; Zhang, Yu-tu; Yong, Liu

2016-07-01

With the development of modern technology, especially the development of information technology at high speed, the ultraviolet early warning system plays an increasingly important role. In the modern warfare, how to detect the threats earlier, prevent and reduce the attack of precision-guided missile has become a new challenge. Because the ultraviolet warning technology has high environmental adaptability, the low false alarm rate, small volume and other advantages, in the military field applications it has been developed rapidly. According to current application demands for solar blind ultraviolet detection and warning, this paper proposes a reconnaissance and early-warning optical system, which covers solar blind ultraviolet (250nm-280nm) and dual field. This structure takes advantage of a narrow field of view and long focal length optical system to achieve the target object detection, uses wide-field and short focal length optical system to achieve early warning of the target object. It makes use of an ultraviolet beam-splitter to achieve the separation of two optical systems. According to the detector and the corresponding application needs of two visual field of the optical system, the calculation and optical system design were completed. After the design, the MTF of the two optical system is more than 0.8@39lp/mm. A single pixel energy concentration is greater than 80%.

Vision-based measurement for rotational speed by improving Lucas-Kanade template tracking algorithm.

PubMed

Guo, Jie; Zhu, Chang'an; Lu, Siliang; Zhang, Dashan; Zhang, Chunyu

2016-09-01

Rotational angle and speed are important parameters for condition monitoring and fault diagnosis of rotating machineries, and their measurement is useful in precision machining and early warning of faults. In this study, a novel vision-based measurement algorithm is proposed to complete this task. A high-speed camera is first used to capture the video of the rotational object. To extract the rotational angle, the template-based Lucas-Kanade algorithm is introduced to complete motion tracking by aligning the template image in the video sequence. Given the special case of nonplanar surface of the cylinder object, a nonlinear transformation is designed for modeling the rotation tracking. In spite of the unconventional and complex form, the transformation can realize angle extraction concisely with only one parameter. A simulation is then conducted to verify the tracking effect, and a practical tracking strategy is further proposed to track consecutively the video sequence. Based on the proposed algorithm, instantaneous rotational speed (IRS) can be measured accurately and efficiently. Finally, the effectiveness of the proposed algorithm is verified on a brushless direct current motor test rig through the comparison with results obtained by the microphone. Experimental results demonstrate that the proposed algorithm can extract accurately rotational angles and can measure IRS with the advantage of noncontact and effectiveness.

7 Warning Signs of Alzheimer's | Alzheimer's disease | NIH MedlinePlus the Magazine

MedlinePlus

... please turn Javascript on. Feature: Alzheimer's Disease 7 Warning Signs of Alzheimer's Past Issues / Fall 2010 Table ... is to alert the public to the early warning signs of Alzheimer's disease. If someone has several ...

Educator Evaluation and the Use of the Early Warning Indicator System (EWIS). Updated September 9, 2013

ERIC Educational Resources Information Center

Massachusetts Department of Elementary and Secondary Education, 2013

2013-01-01

The Massachusetts Department of Elementary and Secondary Education first released the Early Warning Indicator System (EWIS) data for grades 1-12 in the 2012-13 school year. The Department created the EWIS in direct response to educators' requests for early indicator data across multiple grade levels. The EWIS is a "tool to systematically…

Theory and Application of Early Warning Systems for High School and Beyond

ERIC Educational Resources Information Center

Carl, Bradley; Richardson, Jed T.; Cheng, Emily; Kim, HeeJin; Meyer, Robert H.

2013-01-01

This article describes the development of early warning indicators for high school and beyond in the Milwaukee Public Schools (MPS) by the Value-Added Research Center (VARC) at the University of Wisconsin-Madison, working in conjunction with staff from the Division of Research and Evaluation at MPS. Our work in MPS builds on prior early warning…

ON-LINE TOXICITY MONITORS AND WATERSHED EARLY WARNING SYSTEMS

EPA Science Inventory

A Water Quality Early Warning System using On-line Toxicity Monitors (OTMs) has been deployed in the East Fork of the Little Miami River, Clermont County, OH. Living organisms have long been used to determine the toxicity of environmental samples. With advancements in electronic ...

IG Statement: Arthur A. Elkins, Jr., on OIG report Early Warning Report: Main EPA Headquarters Warehouse in Landover, Maryland, Requires Immediate EPA Attention

EPA Pesticide Factsheets

Statement of Inspector General Arthur A. Elkins, Jr., on the Office of Inspector General (OIG) report Early Warning Report: Main EPA Headquarters Warehouse in Landover, Maryland, Requires Immediate EPA Attention.

Including trait-based early warning signals helps predict population collapse

PubMed Central

Clements, Christopher F.; Ozgul, Arpat

2016-01-01

Foreseeing population collapse is an on-going target in ecology, and this has led to the development of early warning signals based on expected changes in leading indicators before a bifurcation. Such signals have been sought for in abundance time-series data on a population of interest, with varying degrees of success. Here we move beyond these established methods by including parallel time-series data of abundance and fitness-related trait dynamics. Using data from a microcosm experiment, we show that including information on the dynamics of phenotypic traits such as body size into composite early warning indices can produce more accurate inferences of whether a population is approaching a critical transition than using abundance time-series alone. By including fitness-related trait information alongside traditional abundance-based early warning signals in a single metric of risk, our generalizable approach provides a powerful new way to assess what populations may be on the verge of collapse. PMID:27009968

A vantage from space can detect earlier drought onset: an approach using relative humidity.

PubMed

Farahmand, Alireza; AghaKouchak, Amir; Teixeira, Joao

2015-02-25

Each year, droughts cause significant economic and agricultural losses across the world. The early warning and onset detection of drought is of particular importance for effective agriculture and water resource management. Previous studies show that the Standard Precipitation Index (SPI), a measure of precipitation deficit, detects drought onset earlier than other indicators. Here we show that satellite-based near surface air relative humidity data can further improve drought onset detection and early warning. This paper introduces the Standardized Relative Humidity Index (SRHI) based on the NASA Atmospheric Infrared Sounder (AIRS) observations. The results indicate that the SRHI typically detects the drought onset earlier than the SPI. While the AIRS mission was not originally designed for drought monitoring, we show that its relative humidity data offers a new and unique avenue for drought monitoring and early warning. We conclude that the early warning aspects of SRHI may have merit for integration into current drought monitoring systems.

[Early warning on measles through the neural networks].

PubMed

Yu, Bin; Ding, Chun; Wei, Shan-bo; Chen, Bang-hua; Liu, Pu-lin; Luo, Tong-yong; Wang, Jia-gang; Pan, Zhi-wei; Lu, Jun-an

2011-01-01

To discuss the effects on early warning of measles, using the neural networks. Based on the available data through monthly and weekly reports on measles from January 1986 to August 2006 in Wuhan city. The modal was developed using the neural networks to predict and analyze the prevalence and incidence of measles. When the dynamic time series modal was established with back propagation (BP) networks consisting of two layers, if p was assigned as 9, the convergence speed was acceptable and the correlation coefficient was equal to 0.85. It was more acceptable for monthly forecasting the specific value, but better for weekly forecasting the classification under probabilistic neural networks (PNN). When data was big enough to serve the purpose, it seemed more feasible for early warning using the two-layer BP networks. However, when data was not enough, then PNN could be used for the purpose of prediction. This method seemed feasible to be used in the system for early warning.

A Vantage from Space Can Detect Earlier Drought Onset: An Approach Using Relative Humidity

PubMed Central

Farahmand, Alireza; AghaKouchak, Amir; Teixeira, Joao

2015-01-01

Each year, droughts cause significant economic and agricultural losses across the world. The early warning and onset detection of drought is of particular importance for effective agriculture and water resource management. Previous studies show that the Standard Precipitation Index (SPI), a measure of precipitation deficit, detects drought onset earlier than other indicators. Here we show that satellite-based near surface air relative humidity data can further improve drought onset detection and early warning. This paper introduces the Standardized Relative Humidity Index (SRHI) based on the NASA Atmospheric Infrared Sounder (AIRS) observations. The results indicate that the SRHI typically detects the drought onset earlier than the SPI. While the AIRS mission was not originally designed for drought monitoring, we show that its relative humidity data offers a new and unique avenue for drought monitoring and early warning. We conclude that the early warning aspects of SRHI may have merit for integration into current drought monitoring systems. PMID:25711500

Early warning indicators for monitoring the process failure of anaerobic digestion system of food waste.

PubMed

Li, Lei; He, Qingming; Wei, Yunmei; He, Qin; Peng, Xuya

2014-11-01

To determine reliable state parameters which could be used as early warning indicators of process failure due to the acidification of anaerobic digestion of food waste, three mesophilic anaerobic digesters of food waste with different operation conditions were investigated. Such parameters as gas production, methane content, pH, concentrations of volatile fatty acid (VFA), alkalinity and their combined indicators were evaluated. Results revealed that operation conditions significantly affect the responses of parameters and thus the optimal early warning indicators of each reactor differ from each other. None of the single indicators was universally valid for all the systems. The universally valid indicators should combine several parameters to supply complementary information. A combination of total VFA, the ratio of VFA to total alkalinity (VFA/TA) and the ratio of bicarbonate alkalinity to total alkalinity (BA/TA) can reflect the metabolism of the digesting system and realize rapid and effective early warning. Copyright © 2014 Elsevier Ltd. All rights reserved.

Early warnings of the potential for malaria transmission in Rural Africa using the Hydrology, Entomology and Malaria Transmission Simulator (HYDREMATS)

NASA Astrophysics Data System (ADS)

Yamana, T. K.; Eltahir, E. A.

2010-12-01

Early warnings of malaria transmission allow health officials to better prepare for future epidemics. Monitoring rainfall is recognized as an important part of malaria early warning systems, as outlined by the Roll Back Malaria Initiative. The Hydrology, Entomology and Malaria Simulator (HYDREMATS) is a mechanistic model that relates rainfall to malaria transmission, and could be used to provide early warnings of malaria epidemics. HYDREMATS is used to make predictions of mosquito populations and vectorial capacity for 2005, 2006, and 2007 in Banizoumbou village in western Niger. HYDREMATS is forced by observed rainfall, followed by a rainfall prediction based on the seasonal mean rainfall for a period two or four weeks into the future. Predictions made using this method provided reasonable estimates of mosquito populations and vectorial capacity, two to four weeks in advance. The predictions were significantly improved compared to those made when HYDREMATS was forced with seasonal mean rainfall alone.

Suitability of Open-Ocean Instrumentation for Use in Near-Field Tsunami Early Warning Along Seismically Active Subduction Zones

NASA Astrophysics Data System (ADS)

Williamson, Amy L.; Newman, Andrew V.

2018-05-01

Over the past decade, the number of open-ocean gauges capable of parsing information about a passing tsunami has steadily increased, particularly through national cable networks and international buoyed efforts such as the Deep-ocean Assessment and Reporting of Tsunami (DART). This information is analyzed to disseminate tsunami warnings to affected regions. However, most current warnings that incorporate tsunami are directed at mid- and far-field localities. In this study, we analyze the region surrounding four seismically active subduction zones, Cascadia, Japan, Chile, and Java, for their potential to facilitate local tsunami early warning using such systems. We assess which locations currently have instrumentation in the right locations for direct tsunami observations with enough time to provide useful warning to the nearest affected coastline—and which are poorly suited for such systems. Our primary findings are that while some regions are ill-suited for this type of early warning, such as the coastlines of Chile, other localities, like Java, Indonesia, could incorporate direct tsunami observations into their hazard forecasts with enough lead time to be effective for coastal community emergency response. We take into account the effect of tsunami propagation with regard to shallow bathymetry on the fore-arc as well as the effect of earthquake source placement. While it is impossible to account for every type of off-shore tsunamigenic event in these locales, this study aims to characterize a typical large tsunamigenic event occurring in the shallow part of the megathrust as a guide in what is feasible with early tsunami warning.

Assessing the Applicability of Earthquake Early Warning in Nicaragua.

NASA Astrophysics Data System (ADS)

Massin, F.; Clinton, J. F.; Behr, Y.; Strauch, W.; Cauzzi, C.; Boese, M.; Talavera, E.; Tenorio, V.; Ramirez, J.

2016-12-01

Nicaragua, like much of Central America, suffers from frequent damaging earthquakes (6 M7+ earthquakes occurred in the last 100 years). Thrust events occur at the Middle America Trench where the Cocos plate subducts by 72-81 mm/yr eastward beneath the Caribbean plate. Shallow crustal events occur on-shore, with potential extensive damage as demonstrated in 1972 by a M6.2 earthquake, 5 km beneath Managua. This seismotectonic setting is challenging for Earthquake Early Warning (EEW) because the target events derive from both the offshore seismicity, with potentially large lead times but uncertain locations, and shallow seismicity in close proximity to densely urbanized areas, where an early warning would be short if available at all. Nevertheless, EEW could reduce Nicaragua's earthquake exposure. The Swiss Development and Cooperation Fund and the Nicaraguan Government have funded a collaboration between the Swiss Seismological Service (SED) at ETH Zurich and the Nicaraguan Geosciences Institute (INETER) in Managua to investigate and build a prototype EEW system for Nicaragua and the wider region. In this contribution, we present the potential of EEW to effectively alert Nicaragua and the neighbouring regions. We model alert time delays using all available seismic stations (existing and planned) in the region, as well as communication and processing delays (observed and optimal) to estimate current and potential performances of EEW alerts. Theoretical results are verified with the output from the Virtual Seismologist in SeisComP3 (VS(SC3)). VS(SC3) is implemented in the INETER SeisComP3 system for real-time operation and as an offline instance, that simulates real-time operation, to record processing delays of playback events. We compare our results with similar studies for Europe, California and New Zealand. We further highlight current capabilities and challenges for providing EEW alerts in Nicaragua. We also discuss how combining different algorithms, like e.g. VS and FinDer, can lead to a robust approach to EEW.

Food Security, Decision Making and the Use of Remote Sensing in Famine Early Warning Systems

NASA Technical Reports Server (NTRS)

Brown, Molly E.

2008-01-01

Famine early warning systems use remote sensing in combination with socio-economic and household food economy analysis to provide timely and rigorous information on emerging food security crises. The Famine Early Warning Systems Network (FEWS NET) is the US Agency for International Development's decision support system in 20 African countries, as well as in Guatemala, Haiti and Afghanistan. FEWS NET provides early and actionable policy guidance for the US Government and its humanitarian aid partners. As we move into an era of climate change where weather hazards will become more frequent and severe, understanding how to provide quantitative and actionable scientific information for policy makers using biophysical data is critical for an appropriate and effective response.

a Process-Based Drought Early Warning Indicator for Supporting State Drought Mitigation Decision

NASA Astrophysics Data System (ADS)

Fu, R.; Fernando, D. N.; Pu, B.

2014-12-01

Drought prone states such as Texas requires creditable and actionable drought early warning ranging from seasonal to multi-decadal scales. Such information cannot be simply extracted from the available climate prediction and projections because of their large uncertainties at regional scales and unclear connections to the needs of the decision makers. In particular, current dynamic seasonal predictions and climate projections, such as those produced by the NOAA national multi-models ensemble experiment (NMME) and the IPCC AR5 (CMIP5) models, are much more reliable for winter and spring than for the summer season for the US Southern Plains. They also show little connection between the droughts in winter/spring and those in summer, in contrast to the observed dry memory from spring to summer over that region. To mitigate the weakness of dynamic prediction/projections, we have identified three key processes behind the spring-to-summer dry memory through observational studies. Based on these key processes and related fields, we have developed a multivariate principle component statistical model to provide a probabilistic summer drought early warning indicator, using the observed or predicted climate conditions in winter and spring on seasonal scale and climate projection for the mid-21stcentury. The summer drought early warning indicator is constructed in a similar way to the NOAA probabilistic predictions that are familiar to water resource managers. The indicator skill is assessed using the standard NOAA climate prediction assessment tools, i.e., the two alternative forced choice (2AFC) and the Receiver Operating Characteristic (ROC). Comparison with long-term observations suggest that this summer drought early warning indicator is able to capture nearly all the strong summer droughts and outperform the dynamic prediction in this regard over the US Southern Plains. This early warning indicator has been used by the state water agency in May 2014 in briefing the state drought preparedness council and will be provided to stake holders through the website of the Texas state water planning agency. We will also present the results of our ongoing work on using NASA satellite based soil moisture and vegetation stress measurements to further improve the reliability of the summer drought early warning indicator.

Sepsis in Obstetrics: Clinical Features and Early Warning Tools.

PubMed

Parfitt, Sheryl E; Bogat, Mary L; Hering, Sandra L; Ottley, Charlotte; Roth, Cheryl

Morbidity and mortality associated with sepsis has gained widespread attention on a local, state, and national level, yet, it remains a complicated disorder that can be difficult to identify in a timely manner. Sepsis in obstetric patients further complicates the diagnosis as alterations in physiology related to pregnancy can mask sepsis indicators normally seen in the general population. If early signs of sepsis go unrecognized, septic shock can develop, leading to organ dysfunction and potential death. Maternal early warning tools have been designed to assist clinicians in recognizing early indications of illness. Through use of clinical pathway-specific tools, disease processes may be detected early, subsequently benefitting patients with aggressive treatment management and intervention.This article is the second in a series of three that discuss the importance of sepsis and septic shock in pregnancy. Risk factors, causes of sepsis, signs and symptoms, and maternal early warning tools are discussed.

Small satellite technologies and applications II; Proceedings of the Meeting, Orlando, FL, Apr. 21, 22, 1992

NASA Astrophysics Data System (ADS)

Horais, Brian J.

The present conference on small satellite (SS) systems and their supporting technologies discusses the Medsat SS for malaria early warning and control, results of the Uosat earth-imaging system, commercial applications for MSSs, an SS family for LEO communications, videosignal signature-synthesis for fast narrow-bandwidth transmission, and NiH battery applications in SSs. Also discussed are the 'PegaStar' spacecraft concept for remote sensing, dual-cone scanning earth sensor processing algorithms, SS radiation-budget instrumentation, SDI's relevance to SSs, spacecraft fabrication and test integration, and cryocooler producibility. (For individual items see A93-28077 to A93-28100)

Research on intelligent scenic security early warning platform based on high resolution image: real scene linkage and real-time LBS

NASA Astrophysics Data System (ADS)

Li, Baishou; Huang, Yu; Lan, Guangquan; Li, Tingting; Lu, Ting; Yao, Mingxing; Luo, Yuandan; Li, Boxiang; Qian, Yongyou; Gao, Yujiu

2015-12-01

This paper design and implement security monitor system within a scenic spot for tourists, the scenic spot staff can be automatic real time for visitors to perception and monitoring, and visitors can also know about themselves location in the scenic, real-time and obtain the 3D imaging conditions of scenic area. Through early warning can realize "parent-child relation", preventing the old man and child lost and wandering. Research results to the further development of virtual reality to provide effective security early warning platform of the theoretical basis and practical reference.

Information Spread of Emergency Events: Path Searching on Social Networks

PubMed Central

Hu, Hongzhi; Wu, Tunan

2014-01-01

Emergency has attracted global attentions of government and the public, and it will easily trigger a series of serious social problems if it is not supervised effectively in the dissemination process. In the Internet world, people communicate with each other and form various virtual communities based on social networks, which lead to a complex and fast information spread pattern of emergency events. This paper collects Internet data based on data acquisition and topic detection technology, analyzes the process of information spread on social networks, describes the diffusions and impacts of that information from the perspective of random graph, and finally seeks the key paths through an improved IBF algorithm. Application cases have shown that this algorithm can search the shortest spread paths efficiently, which may help us to guide and control the information dissemination of emergency events on early warning. PMID:24600323

Enhanced ICP for the Registration of Large-Scale 3D Environment Models: An Experimental Study

PubMed Central

Han, Jianda; Yin, Peng; He, Yuqing; Gu, Feng

2016-01-01

One of the main applications of mobile robots is the large-scale perception of the outdoor environment. One of the main challenges of this application is fusing environmental data obtained by multiple robots, especially heterogeneous robots. This paper proposes an enhanced iterative closest point (ICP) method for the fast and accurate registration of 3D environmental models. First, a hierarchical searching scheme is combined with the octree-based ICP algorithm. Second, an early-warning mechanism is used to perceive the local minimum problem. Third, a heuristic escape scheme based on sampled potential transformation vectors is used to avoid local minima and achieve optimal registration. Experiments involving one unmanned aerial vehicle and one unmanned surface vehicle were conducted to verify the proposed technique. The experimental results were compared with those of normal ICP registration algorithms to demonstrate the superior performance of the proposed method. PMID:26891298

Dengue Contingency Planning: From Research to Policy and Practice.

PubMed

Runge-Ranzinger, Silvia; Kroeger, Axel; Olliaro, Piero; McCall, Philip J; Sánchez Tejeda, Gustavo; Lloyd, Linda S; Hakim, Lokman; Bowman, Leigh R; Horstick, Olaf; Coelho, Giovanini

2016-09-01

Dengue is an increasingly incident disease across many parts of the world. In response, an evidence-based handbook to translate research into policy and practice was developed. This handbook facilitates contingency planning as well as the development and use of early warning and response systems for dengue fever epidemics, by identifying decision-making processes that contribute to the success or failure of dengue surveillance, as well as triggers that initiate effective responses to incipient outbreaks. Available evidence was evaluated using a step-wise process that included systematic literature reviews, policymaker and stakeholder interviews, a study to assess dengue contingency planning and outbreak management in 10 countries, and a retrospective logistic regression analysis to identify alarm signals for an outbreak warning system using datasets from five dengue endemic countries. Best practices for managing a dengue outbreak are provided for key elements of a dengue contingency plan including timely contingency planning, the importance of a detailed, context-specific dengue contingency plan that clearly distinguishes between routine and outbreak interventions, surveillance systems for outbreak preparedness, outbreak definitions, alert algorithms, managerial capacity, vector control capacity, and clinical management of large caseloads. Additionally, a computer-assisted early warning system, which enables countries to identify and respond to context-specific variables that predict forthcoming dengue outbreaks, has been developed. Most countries do not have comprehensive, detailed contingency plans for dengue outbreaks. Countries tend to rely on intensified vector control as their outbreak response, with minimal focus on integrated management of clinical care, epidemiological, laboratory and vector surveillance, and risk communication. The Technical Handbook for Surveillance, Dengue Outbreak Prediction/ Detection and Outbreak Response seeks to provide countries with evidence-based best practices to justify the declaration of an outbreak and the mobilization of the resources required to implement an effective dengue contingency plan.

An on-board pedestrian detection and warning system with features of side pedestrian

NASA Astrophysics Data System (ADS)

Cheng, Ruzhong; Zhao, Yong; Wong, ChupChung; Chan, KwokPo; Xu, Jiayao; Wang, Xin'an

2012-01-01

Automotive Active Safety(AAS) is the main branch of intelligence automobile study and pedestrian detection is the key problem of AAS, because it is related with the casualties of most vehicle accidents. For on-board pedestrian detection algorithms, the main problem is to balance efficiency and accuracy to make the on-board system available in real scenes, so an on-board pedestrian detection and warning system with the algorithm considered the features of side pedestrian is proposed. The system includes two modules, pedestrian detecting and warning module. Haar feature and a cascade of stage classifiers trained by Adaboost are first applied, and then HOG feature and SVM classifier are used to refine false positives. To make these time-consuming algorithms available in real-time use, a divide-window method together with operator context scanning(OCS) method are applied to increase efficiency. To merge the velocity information of the automotive, the distance of the detected pedestrian is also obtained, so the system could judge if there is a potential danger for the pedestrian in the front. With a new dataset captured in urban environment with side pedestrians on zebra, the embedded system and its algorithm perform an on-board available result on side pedestrian detection.

Early warning signals of Atlantic Meridional Overturning Circulation collapse in a fully coupled climate model

NASA Astrophysics Data System (ADS)

Boulton, Chris A.; Allison, Lesley C.; Lenton, Timothy M.

2014-12-01

The Atlantic Meridional Overturning Circulation (AMOC) exhibits two stable states in models of varying complexity. Shifts between alternative AMOC states are thought to have played a role in past abrupt climate changes, but the proximity of the climate system to a threshold for future AMOC collapse is unknown. Generic early warning signals of critical slowing down before AMOC collapse have been found in climate models of low and intermediate complexity. Here we show that early warning signals of AMOC collapse are present in a fully coupled atmosphere-ocean general circulation model, subject to a freshwater hosing experiment. The statistical significance of signals of increasing lag-1 autocorrelation and variance vary with latitude. They give up to 250 years warning before AMOC collapse, after ~550 years of monitoring. Future work is needed to clarify suggested dynamical mechanisms driving critical slowing down as the AMOC collapse is approached.

Early warning signals of Atlantic Meridional Overturning Circulation collapse in a fully coupled climate model

PubMed Central

Boulton, Chris A.; Allison, Lesley C.; Lenton, Timothy M.

2014-01-01

The Atlantic Meridional Overturning Circulation (AMOC) exhibits two stable states in models of varying complexity. Shifts between alternative AMOC states are thought to have played a role in past abrupt climate changes, but the proximity of the climate system to a threshold for future AMOC collapse is unknown. Generic early warning signals of critical slowing down before AMOC collapse have been found in climate models of low and intermediate complexity. Here we show that early warning signals of AMOC collapse are present in a fully coupled atmosphere-ocean general circulation model, subject to a freshwater hosing experiment. The statistical significance of signals of increasing lag-1 autocorrelation and variance vary with latitude. They give up to 250 years warning before AMOC collapse, after ~550 years of monitoring. Future work is needed to clarify suggested dynamical mechanisms driving critical slowing down as the AMOC collapse is approached. PMID:25482065

Early warning signals of Atlantic Meridional Overturning Circulation collapse in a fully coupled climate model.

PubMed

Boulton, Chris A; Allison, Lesley C; Lenton, Timothy M

2014-12-08

The Atlantic Meridional Overturning Circulation (AMOC) exhibits two stable states in models of varying complexity. Shifts between alternative AMOC states are thought to have played a role in past abrupt climate changes, but the proximity of the climate system to a threshold for future AMOC collapse is unknown. Generic early warning signals of critical slowing down before AMOC collapse have been found in climate models of low and intermediate complexity. Here we show that early warning signals of AMOC collapse are present in a fully coupled atmosphere-ocean general circulation model, subject to a freshwater hosing experiment. The statistical significance of signals of increasing lag-1 autocorrelation and variance vary with latitude. They give up to 250 years warning before AMOC collapse, after ~550 years of monitoring. Future work is needed to clarify suggested dynamical mechanisms driving critical slowing down as the AMOC collapse is approached.

Implementing an Inpatient Social Early Warning System for Child Maltreatment

ERIC Educational Resources Information Center

Atabaki, Armita; Heddaeus, Daniela; Metzner, Franka; Schulz, Holger; Siefert, Sonke; Pawils, Silke

2013-01-01

Objectives: The current article describes the process evaluation of a social early warning system (SEWS) for the prevention of child maltreatment in the federal state of Hamburg. This prevention initiative targets expectant mothers and their partners including an initial screening of risk factors for child maltreatment, a subsequent structured…

PROPOSED WATER QUALITY SURVEILLANCE NETWORK USING PHYSICAL, CHEMICAL AND BIOLOGICAL EARLY WARNING SYSTEMS (CBEWS)

EPA Science Inventory

The Homeland Protection Act of 2002 specifically calls for the investigation and use of Early Warning Systems (EWS) for water security reasons. The EWS is a screening tool for detecting changes in source water and distribution system water quality. A suite of time-relevant biol...

PROPOSED WATER QUALITY SURVEILLANCE NETWORK USING PHYSICAL, CHEMICAL AND BIOLOGICAL EARLY WARNING SYSTEMS (BEWS)

EPA Science Inventory

The Homeland Protection Act of 2002 specifically calls for the investigation and use of Early Warning Systems (EWS) for water security reasons. The EWS is a screening tool for detecting changes in source water and distribution system water quality. A suite of time-relevant biol...

78 FR 68831 - Agency Information Collection Activities; Submission to the Office of Management and Budget for...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-15

... Early Warning and Intervention Monitoring System AGENCY: Institute of Education Sciences/National Center... Intervention Monitoring System. OMB Control Number: 1850-NEW. Type of Review: New collection. Respondents... planning a two-part evaluation of the Early Warning and Intervention Monitoring System (EWIMS), consisting...

78 FR 48863 - Agency Information Collection Activities; Comment Request; Evaluation of the Early Warning and...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-08-12

... DEPARTMENT OF EDUCATION [Docket No.: ED-2013-ICCD-0106] Agency Information Collection Activities; Comment Request; Evaluation of the Early Warning and Intervention Monitoring System AGENCY: Institute of... Intervention Monitoring System. OMB Control Number: 1850-NEW. Type of Review: A new information collection...

Real-time earthquake data feasible

NASA Astrophysics Data System (ADS)

Bush, Susan

Scientists agree that early warning devices and monitoring of both Hurricane Hugo and the Mt. Pinatubo volcanic eruption saved thousands of lives. What would it take to develop this sort of early warning and monitoring system for earthquake activity?Not all that much, claims a panel assigned to study the feasibility, costs, and technology needed to establish a real-time earthquake monitoring (RTEM) system. The panel, drafted by the National Academy of Science's Committee on Seismology, has presented its findings in Real-Time Earthquake Monitoring. The recently released report states that “present technology is entirely capable of recording and processing data so as to provide real-time information, enabling people to mitigate somewhat the earthquake disaster.” RTEM systems would consist of two parts—an early warning system that would give a few seconds warning before severe shaking, and immediate postquake information within minutes of the quake that would give actual measurements of the magnitude. At this time, however, this type of warning system has not been addressed at the national level for the United States and is not included in the National Earthquake Hazard Reduction Program, according to the report.

Experience from three years of local capacity development for tsunami early warning in Indonesia: challenges, lessons and the way ahead

NASA Astrophysics Data System (ADS)

Spahn, H.; Hoppe, M.; Vidiarina, H. D.; Usdianto, B.

2010-07-01

Five years after the 2004 tsunami, a lot has been achieved to make communities in Indonesia better prepared for tsunamis. This achievement is primarily linked to the development of the Indonesian Tsunami Early Warning System (InaTEWS). However, many challenges remain. This paper describes the experience with local capacity development for tsunami early warning (TEW) in Indonesia, based on the activities of a pilot project. TEW in Indonesia is still new to disaster management institutions and the public, as is the paradigm of Disaster Risk Reduction (DRR). The technology components of InaTEWS will soon be fully operational. The major challenge for the system is the establishment of clear institutional arrangements and capacities at national and local levels that support the development of public and institutional response capability at the local level. Due to a lack of information and national guidance, most local actors have a limited understanding of InaTEWS and DRR, and often show little political will and priority to engage in TEW. The often-limited capacity of local governments is contrasted by strong engagement of civil society organisations that opt for early warning based on natural warning signs rather than technology-based early warning. Bringing together the various actors, developing capacities in a multi-stakeholder cooperation for an effective warning system are key challenges for the end-to-end approach of InaTEWS. The development of local response capability needs to receive the same commitment as the development of the system's technology components. Public understanding of and trust in the system comes with knowledge and awareness on the part of the end users of the system and convincing performance on the part of the public service provider. Both sides need to be strengthened. This requires the integration of TEW into DRR, clear institutional arrangements, national guidance and intensive support for capacity development at local levels as well as dialogue between the various actors.

Early warning of orographically induced floods and landslides in Western Norway

NASA Astrophysics Data System (ADS)

Leine, Ann-Live; Wang, Thea; Boje, Søren

2017-04-01

In Western Norway, landslides and debris flows are commonly initiated by short-term orographic rainfall or intensity peaks during a prolonged rainfall event. In recent years, the flood warning service in Norway has evolved from being solely a flood forecasting service to also integrating landslides into its early warning systems. As both floods and landslides are closely related to the same hydrometeorological processes, particularly in small catchments, there is a natural synergy between monitoring flood and landslide risk. The Norwegian Flood and Landslide Hazard Forecasting and Warning Service issues regional landslide hazard warnings based on hydrological models, threshold values, observations and weather forecasts. Intense rainfall events and/or orographic precipitation that, under certain topographic conditions, significantly increase the risk of debris avalanches and debris floods are lately receiving more research focus from the Norwegian warning service. Orographic precipitation is a common feature in W-Norway, when moist and relatively mild air arrives from the Atlantic. Steep mountain slopes covered by glacial till makes the region prone to landslides, as well as flooding. The operational early warning system in Norway requires constant improvement, especially with the enhanced number of intense rainfall events that occur in a warming climate. Here, we examine different cases of intense rainfall events which have lead to landslides and debris flows, as well as increased runoff in fast responding small catchments. The main objective is to increase the understanding of the hydrometeorological conditions related to these events, in order to make priorities for the future development of the warning service.

Early warning system for aftershocks

USGS Publications Warehouse

Bakun, W.H.; Fischer, F.G.; Jensen, E.G.; VanSchaack, J.

1994-01-01

A prototype early warning system to provide San Francisco and Oakland, California a few tens-of-seconds warning of incoming strong ground shaking from already-occurred M ≧ 3.7 aftershocks of the magnitude 7.1 17 October 1989 Loma Prieta earthquake was operational on 28 October 1989. The prototype system consisted of four components: ground motion sensors in the epicentral area, a central receiver, a radio repeater, and radio receivers. One of the radio receivers was deployed at the California Department of Transportation (CALTRANS) headquarters at the damaged Cypress Street section of the I-880 freeway in Oakland, California on 28 October 1989 and provided about 20 sec of warning before shaking from the M 4.5 Loma Prieta aftershock that occurred on 2 November 1989 at 0550 UTC. In its first 6 months of operation, the system generated triggers for all 12 M > 3.7 aftershocks for which trigger documentation is preserved, did not trigger on any M ≦ 3.6 aftershocks, and produced one false trigger as a result of a now-corrected single point of failure design flaw. Because the prototype system demonstrated that potentially useful warnings of strong shaking from aftershocks are feasible, the USGS has completed a portable early warning system for aftershocks that can be deployed anywhere.

Research and application of a hybrid model based on dynamic fuzzy synthetic evaluation for establishing air quality forecasting and early warning system: A case study in China.

PubMed

Xu, Yunzhen; Du, Pei; Wang, Jianzhou

2017-04-01

As the atmospheric environment pollution has been becoming more and more serious in China, it is highly desirable to develop a scientific and effective early warning system that plays a great significant role in analyzing and monitoring air quality. However, establishing a robust early warning system for warning the public in advance and ameliorating air quality is not only an extremely challenging task but also a public concerned problem for human health. Most previous studies are focused on improving the prediction accuracy, which usually ignore the significance of uncertainty information and comprehensive evaluation concerning air pollutants. Therefore, in this paper a novel robust early warning system was successfully developed, which consists of three modules: evaluation module, forecasting module and characteristics estimating module. In this system, a new dynamic fuzzy synthetic evaluation is proposed and applied to determine air quality levels and primary pollutants, which can be regarded as the research objectives; Moreover, to further mine and analyze the characteristics of air pollutants, four different distribution functions and interval forecasting method are also employed that can not only provide predictive range, confidence level and the other uncertain information of the pollutants future values, but also assist decision-makers in reducing and controlling the emissions of atmospheric pollutants. Case studies utilizing hourly PM 2.5 , PM 10 and SO 2 data collected from Tianjin and Shanghai in China are applied as illustrative examples to estimate the effectiveness and efficiency of the proposed system. Experimental results obviously indicated that the developed novel early warning system is much suitable for analyzing and monitoring air pollution, which can also add a novel viable option for decision-makers. Copyright © 2017 Elsevier Ltd. All rights reserved.

Global early warning systems for natural hazards: systematic and people-centred.

PubMed

Basher, Reid

2006-08-15

To be effective, early warning systems for natural hazards need to have not only a sound scientific and technical basis, but also a strong focus on the people exposed to risk, and with a systems approach that incorporates all of the relevant factors in that risk, whether arising from the natural hazards or social vulnerabilities, and from short-term or long-term processes. Disasters are increasing in number and severity and international institutional frameworks to reduce disasters are being strengthened under United Nations oversight. Since the Indian Ocean tsunami of 26 December 2004, there has been a surge of interest in developing early warning systems to cater to the needs of all countries and all hazards.

Surveillance and early warning systems of infectious disease in China: From 2012 to 2014.

PubMed

Zhang, Honglong; Wang, Liping; Lai, Shengjie; Li, Zhongjie; Sun, Qiao; Zhang, Peng

2017-07-01

Appropriate surveillance and early warning of infectious diseases have very useful roles in disease control and prevention. In 2004, China established the National Notifiable Infectious Disease Surveillance System and the Public Health Emergency Event Surveillance System to report disease surveillance and events on the basis of data sources from the National Notifiable Infectious Disease Surveillance System, China Infectious Disease Automated-alert and Response System in this country. This study provided a descriptive summary and a data analysis, from 2012 to 2014, of these 3 key surveillance and early warning systems of infectious disease in China with the intent to provide suggestions for system improvement and perfection. Copyright © 2017 John Wiley & Sons, Ltd.

Storm-based Cloud-to-Ground Lightning Probabilities and Warnings

NASA Astrophysics Data System (ADS)

Calhoun, K. M.; Meyer, T.; Kingfield, D.

2017-12-01

A new cloud-to-ground (CG) lightning probability algorithm has been developed using machine-learning methods. With storm-based inputs of Earth Networks' in-cloud lightning, Vaisala's CG lightning, multi-radar/multi-sensor (MRMS) radar derived products including the Maximum Expected Size of Hail (MESH) and Vertically Integrated Liquid (VIL), and near storm environmental data including lapse rate and CAPE, a random forest algorithm was trained to produce probabilities of CG lightning up to one-hour in advance. As part of the Prototype Probabilistic Hazard Information experiment in the Hazardous Weather Testbed in 2016 and 2017, National Weather Service forecasters were asked to use this CG lightning probability guidance to create rapidly updating probability grids and warnings for the threat of CG lightning for 0-60 minutes. The output from forecasters was shared with end-users, including emergency managers and broadcast meteorologists, as part of an integrated warning team.

Military Intervention to Stop Mass Atrocities

DTIC Science & Technology

2017-05-04

the circumstances where the United States should respond militarily. Early warning and prevention strategies are vital since these approaches allow...34genocide." As early as 1933, Lemkin addressed the issue of genocide in a paper he sent to a League of Nations conference in Madrid, Spain. He...2017, https://www.ushmm.org/confront- genocide/speakers-and-events/all-speakers-and-events/ early -warning-and-prevention/the-united-states- measures-to

Flood and landslide warning based on rainfall thresholds and soil moisture indexes: the HEWS (Hydrohazards Early Warning System) for Sicily

NASA Astrophysics Data System (ADS)

Brigandì, Giuseppina; Tito Aronica, Giuseppe; Bonaccorso, Brunella; Gueli, Roberto; Basile, Giuseppe

2017-09-01

The main focus of the paper is to present a flood and landslide early warning system, named HEWS (Hydrohazards Early Warning System), specifically developed for the Civil Protection Department of Sicily, based on the combined use of rainfall thresholds, soil moisture modelling and quantitative precipitation forecast (QPF). The warning system is referred to 9 different Alert Zones in which Sicily has been divided into and based on a threshold system of three different increasing critical levels: ordinary, moderate and high. In this system, for early flood warning, a Soil Moisture Accounting (SMA) model provides daily soil moisture conditions, which allow to select a specific set of three rainfall thresholds, one for each critical level considered, to be used for issue the alert bulletin. Wetness indexes, representative of the soil moisture conditions of a catchment, are calculated using a simple, spatially-lumped rainfall-streamflow model, based on the SCS-CN method, and on the unit hydrograph approach, that require daily observed and/or predicted rainfall, and temperature data as input. For the calibration of this model daily continuous time series of rainfall, streamflow and air temperature data are used. An event based lumped rainfall-runoff model has been, instead, used for the derivation of the rainfall thresholds for each catchment in Sicily characterised by an area larger than 50 km2. In particular, a Kinematic Instantaneous Unit Hydrograph based lumped rainfall-runoff model with the SCS-CN routine for net rainfall was developed for this purpose. For rainfall-induced shallow landslide warning, empirical rainfall thresholds provided by Gariano et al. (2015) have been included in the system. They were derived on an empirical basis starting from a catalogue of 265 shallow landslides in Sicily in the period 2002-2012. Finally, Delft-FEWS operational forecasting platform has been applied to link input data, SMA model and rainfall threshold models to produce warning on a daily basis for the entire region.

Pre-Launch Algorithms and Risk Reduction in Support of the Geostationary Lightning Mapper for GOES-R and Beyond

NASA Technical Reports Server (NTRS)

Goodman, Steven J.; Blakeslee, R. J.; Koshak, W.; Petersen, W.; Buechler, D. E.; Krehbiel, P. R.; Gatlin, P.; Zubrick, S.

2008-01-01

The Geostationary Lightning Mapper (GLM) is a single channel, near-IR imager/optical transient event detector, used to detect, locate and measure total lightning activity over the full-disk as part of a 3-axis stabilized, geostationary weather satellite system. The next generation NOAA Geostationary Operational Environmental Satellite (GOES-R) series with a planned launch in 2014 will carry a GLM that will provide continuous day and night observations of lightning from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fUlly operational. The mission objectives for the GLM are to 1) provide continuous, full-disk lightning measurements for storm warning and nowcasting, 2) provide early warning of tornadic activity, and 3) accumulate a long-term database to track decadal changes of lightning. The GLM owes its heritage to the NASA Lightning Imaging Sensor (1997-Present) and the Optical Transient Detector (1995-2000), which were developed for the Earth Observing System and have produced a combined 13 year data record of global lightning activity. Instrument formulation studies were completed in March 2007 and the implementation phase to develop a prototype model and up to four flight models is expected to be underway in the latter part of 2007. In parallel with the instrument development, a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2 ground processing algorithms and applications. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional test beds (e.g., Lightning Mapping Arrays in North Alabama and the Washington DC Metropolitan area)

Geoethical considerations in early warning of flooding and landslides: Case study from Norway

NASA Astrophysics Data System (ADS)

Devoli, Graziella; Kleivane Krøgli, Ingeborg; Dahl, Mads Peter; Colleuille, Hervé; Nykjær Boje, Søren; Sund, Monica

2015-04-01

The Norwegian Water Resources and Energy Directorate (NVE) runs the national early warning systems (EWS) for flooding and shallow landslides in Norway. The two EWSs have been operational since the late 1980s and 2013 respectively, and are based on weather forecasts, various hydro-meteorological prognosis and expert evaluation. Daily warning levels and related information to the public is prepared and presented through custom build internet platforms. In natural hazards sciences, the risk of a specific threat is defined as the product of hazard and consequence. In this context an EWS is intended to work as a mitigation measure in lowering the consequence and thus the risk of the threat. One of several factors determining the quality of such an EWS, is how warnings are communicated to the public. In contrary to what is common practice in some other countries, experts working with EWS in Norway cannot be held personally responsible for consequences of warnings being issued or not. However, the communication of warnings for flooding and landslides at NVE still implies many considerations of geoethical kind. Which are the consequences today for the forecasters when erroneous warning messages are sent because based on a poorly documented analysis? What is for example the most responsible way to describe uncertainties in warnings issued? What is the optimal compromise between avoiding false alarms and not sending out a specific warning? Is it responsible to rely on a "gut feeling"? Some authorities complain in receiving warning messages too often. Is it responsible to begin notifying these, only in cases of "high hazard level" and no longer in cases of "moderate hazard level"? Is it acceptable to issue general warnings for large geographical areas without being able to pinpoint the treat on local scale? What responsibility lies within the EWS in recommending evacuation or other practical measures to local authorities? By presenting how early warnings of flooding and landslides are communicated in Norway and discussing the questions above, we intend to add to the discussion on what is the ethical responsibility for scientists performing forecasting and communication of natural hazards.

Using Runtime Analysis to Guide Model Checking of Java Programs

NASA Technical Reports Server (NTRS)

Havelund, Klaus; Norvig, Peter (Technical Monitor)

2001-01-01

This paper describes how two runtime analysis algorithms, an existing data race detection algorithm and a new deadlock detection algorithm, have been implemented to analyze Java programs. Runtime analysis is based on the idea of executing the program once. and observing the generated run to extract various kinds of information. This information can then be used to predict whether other different runs may violate some properties of interest, in addition of course to demonstrate whether the generated run itself violates such properties. These runtime analyses can be performed stand-alone to generate a set of warnings. It is furthermore demonstrated how these warnings can be used to guide a model checker, thereby reducing the search space. The described techniques have been implemented in the b e grown Java model checker called PathFinder.

Application of τc*Pd in earthquake early warning

NASA Astrophysics Data System (ADS)

Huang, Po-Lun; Lin, Ting-Li; Wu, Yih-Min

2015-03-01

Rapid assessment of damage potential and size of an earthquake at the station is highly demanded for onsite earthquake early warning. We study the application of τc*Pd for its estimation on the earthquake size using 123 events recorded by the borehole stations of KiK-net in Japan. The new type of earthquake size determined by τc*Pd is more related to the damage potential. We find that τc*Pd provides another parameter to measure the size of earthquake and the threshold to warn strong ground motion.

ElarmS Earthquake Early Warning System: 2017 Performance and New ElarmS Version 3.0 (E3)

NASA Astrophysics Data System (ADS)

Chung, A. I.; Henson, I. H.; Allen, R. M.; Hellweg, M.; Neuhauser, D. S.

2017-12-01

The ElarmS earthquake early warning (EEW) system has been successfully detecting earthquakes throughout California since 2007. ElarmS version 2.0 (E2) is one of the three algorithms contributing alerts to ShakeAlert, a public EEW system being developed by the USGS in collaboration with UC Berkeley, Caltech, University of Washington, and University of Oregon. E2 began operating in test mode in the Pacific Northwest in 2013, and since April of this year E2 has been contributing real-time alerts from Oregon and Washington to the ShakeAlert production prototype system as part of the ShakeAlert roll-out throughout the West Coast. Since it began operating west-coast-wide, E2 has correctly alerted on 5 events that matched ANSS catalog events with M≥4, missed 1 event with M≥4, and incorrectly created alerts for 5 false events with M≥4. The most recent version of the algorithm, ElarmS version 3.0 (E3), is a significant improvement over E2. It addresses some of the most problematic causes of false events for which E2 produced alerts, without impacting reliability in terms of matched and missed events. Of the 5 false events that were generated by E2 since April, 4 would have been suppressed by E3. In E3, we have added a filterbank teleseismic filter. By analyzing the amplitude of the waveform filtered in various passbands, it is possible to distinguish between local and teleseismic events. We have also added a series of checks to validate triggers and filter out spurious and S-wave triggers. Additional improvements to the waveform associator also improve detections. In this presentation, we describe the improvements and compare the performance of the current production (E2) and development (E3) versions of ElarmS over the past year. The ShakeAlert project is now working through a streamlining process to identify the best components of various algorithms and merge them. The ElarmS team is participating in this effort and we anticipate that much of E3 will continue in the final system.

Early Warning Signs. A Solution-Finding Report

ERIC Educational Resources Information Center

Sullivan, Robert, Comp.

2017-01-01

This Solution-Finding Report provides information, requested by Tara Zuber with the Great Lakes Comprehensive Center (GLCC) at American Institutes for Research (AIR), for resources with evidence-based practices that look at the social and emotional causes that impact the lack of student learning and engagement, for GLCC's Early Warning Signs work.…

Research on Disaster Early Warning and Disaster Relief Integrated Service System Based on Block Data Theory

NASA Astrophysics Data System (ADS)

Yang, J.; Zhang, H.; Wang, C.; Tang, D.

2018-04-01

With the continuous development of social economy, the interaction between mankind and nature has become increasingly evident. Disastrous global catastrophes have occurred from time to time, causing huge losses to people's lives and property. All governments recognize the importance of the establishment of disaster early warning and release mechanisms, and it is also an urgent issue to improve the comprehensive service level of emergency response and disaster relief. However, disaster early warning and emergency relief information is usually generated by different departments, and the diverse data sources, difficult integration, and limited release speed have always been difficult issues to be solved. Block data is the aggregation of various distributed (point data) and segmentation (data) big data on a specific platform and make them happen continuous polymerization effect, block data theory is a good solution to cross-sectoral, cross-platform Disaster information data sharing and integration problems. This paper attempts to discuss the integrated service mechanism of disaster information aggregation and disaster relief based on block data theory and introduces a location-based integrated service system for disaster early warning and disaster relief.

Technical Note: An operational landslide early warning system at regional scale based on space-time variable rainfall thresholds

NASA Astrophysics Data System (ADS)

Segoni, S.; Battistini, A.; Rossi, G.; Rosi, A.; Lagomarsino, D.; Catani, F.; Moretti, S.; Casagli, N.

2014-10-01

We set up an early warning system for rainfall-induced landslides in Tuscany (23 000 km2). The system is based on a set of state-of-the-art intensity-duration rainfall thresholds (Segoni et al., 2014b), makes use of LAMI rainfall forecasts and real-time rainfall data provided by an automated network of more than 300 rain-gauges. The system was implemented in a WebGIS to ease the operational use in civil protection procedures: it is simple and intuitive to consult and it provides different outputs. Switching among different views, the system is able to focus both on monitoring of real time data and on forecasting at different lead times up to 48 h. Moreover, the system can switch between a very straightforward view where a synoptic scenario of the hazard can be shown all over the region and a more in-depth view were the rainfall path of rain-gauges can be displayed and constantly compared with rainfall thresholds. To better account for the high spatial variability of the physical features, which affects the relationship between rainfall and landslides, the region is subdivided into 25 alert zones, each provided with a specific threshold. The warning system reflects this subdivision: using a network of 332 rain gauges, it allows monitoring each alert zone separately and warnings can be issued independently from an alert zone to another. An important feature of the warning system is the use of thresholds that may vary in time adapting at the conditions of the rainfall path recorded by the rain-gauges. Depending on when the starting time of the rainfall event is set, the comparison with the threshold may produce different outcomes. Therefore, a recursive algorithm was developed to check and compare with the thresholds all possible starting times, highlighting the worst scenario and showing in the WebGIS interface at what time and how much the rainfall path has exceeded or will exceed the most critical threshold. Besides forecasting and monitoring the hazard scenario over the whole region with hazard levels differentiated for 25 distinct alert zones, the system can be used to gather, analyze, visualize, explore, interpret and store rainfall data, thus representing a potential support to both decision makers and scientists.

Integrating Remote Sensing and Disease Surveillance to Forecast Malaria Epidemics

NASA Astrophysics Data System (ADS)

Wimberly, M. C.; Beyane, B.; DeVos, M.; Liu, Y.; Merkord, C. L.; Mihretie, A.

2015-12-01

Advance information about the timing and locations of malaria epidemics can facilitate the targeting of resources for prevention and emergency response. Early detection methods can detect incipient outbreaks by identifying deviations from expected seasonal patterns, whereas early warning approaches typically forecast future malaria risk based on lagged responses to meteorological factors. A critical limiting factor for implementing either of these approaches is the need for timely and consistent acquisition, processing and analysis of both environmental and epidemiological data. To address this need, we have developed EPIDEMIA - an integrated system for surveillance and forecasting of malaria epidemics. The EPIDEMIA system includes a public health interface for uploading and querying weekly surveillance reports as well as algorithms for automatically validating incoming data and updating the epidemiological surveillance database. The newly released EASTWeb 2.0 software application automatically downloads, processes, and summaries remotely-sensed environmental data from multiple earth science data archives. EASTWeb was implemented as a component of the EPIDEMIA system, which combines the environmental monitoring data and epidemiological surveillance data into a unified database that supports both early detection and early warning models. Dynamic linear models implemented with Kalman filtering were used to carry out forecasting and model updating. Preliminary forecasts have been disseminated to public health partners in the Amhara Region of Ethiopia and will be validated and refined as the EPIDEMIA system ingests new data. In addition to continued model development and testing, future work will involve updating the public health interface to provide a broader suite of outbreak alerts and data visualization tools that are useful to our public health partners. The EPIDEMIA system demonstrates a feasible approach to synthesizing the information from epidemiological surveillance systems and remotely-sensed environmental monitoring systems to improve malaria epidemic detection and forecasting.

Early warning of illegal development for protected areas by integrating cellular automata with neural networks.

PubMed

Li, Xia; Lao, Chunhua; Liu, Yilun; Liu, Xiaoping; Chen, Yimin; Li, Shaoying; Ai, Bing; He, Zijian

2013-11-30

Ecological security has become a major issue under fast urbanization in China. As the first two cities in this country, Shenzhen and Dongguan issued the ordinance of Eco-designated Line of Control (ELC) to "wire" ecologically important areas for strict protection in 2005 and 2009 respectively. Early warning systems (EWS) are a useful tool for assisting the implementation ELC. In this study, a multi-model approach is proposed for the early warning of illegal development by integrating cellular automata (CA) and artificial neural networks (ANN). The objective is to prevent the ecological risks or catastrophe caused by such development at an early stage. The integrated model is calibrated by using the empirical information from both remote sensing and handheld GPS (global positioning systems). The MAR indicator which is the ratio of missing alarms to all the warnings is proposed for better assessment of the model performance. It is found that the fast urban development has caused significant threats to natural-area protection in the study area. The integration of CA, ANN and GPS provides a powerful tool for describing and predicting illegal development which is in highly non-linear and fragmented forms. The comparison shows that this multi-model approach has much better performances than the single-model approach for the early warning. Compared with the single models of CA and ANN, this integrated multi-model can improve the value of MAR by 65.48% and 5.17% respectively. Copyright © 2013 Elsevier Ltd. All rights reserved.

Relation between stability and resilience determines the performance of early warning signals under different environmental drivers.

PubMed

Dai, Lei; Korolev, Kirill S; Gore, Jeff

2015-08-11

Shifting patterns of temporal fluctuations have been found to signal critical transitions in a variety of systems, from ecological communities to human physiology. However, failure of these early warning signals in some systems calls for a better understanding of their limitations. In particular, little is known about the generality of early warning signals in different deteriorating environments. In this study, we characterized how multiple environmental drivers influence the dynamics of laboratory yeast populations, which was previously shown to display alternative stable states [Dai et al., Science, 2012]. We observed that both the coefficient of variation and autocorrelation increased before population collapse in two slowly deteriorating environments, one with a rising death rate and the other one with decreasing nutrient availability. We compared the performance of early warning signals across multiple environments as "indicators for loss of resilience." We find that the varying performance is determined by how a system responds to changes in a specific driver, which can be captured by a relation between stability (recovery rate) and resilience (size of the basin of attraction). Furthermore, we demonstrate that the positive correlation between stability and resilience, as the essential assumption of indicators based on critical slowing down, can break down in this system when multiple environmental drivers are changed simultaneously. Our results suggest that the stability-resilience relation needs to be better understood for the application of early warning signals in different scenarios.

Relation between stability and resilience determines the performance of early warning signals under different environmental drivers

PubMed Central

Dai, Lei; Korolev, Kirill S.; Gore, Jeff

2015-01-01

Shifting patterns of temporal fluctuations have been found to signal critical transitions in a variety of systems, from ecological communities to human physiology. However, failure of these early warning signals in some systems calls for a better understanding of their limitations. In particular, little is known about the generality of early warning signals in different deteriorating environments. In this study, we characterized how multiple environmental drivers influence the dynamics of laboratory yeast populations, which was previously shown to display alternative stable states [Dai et al., Science, 2012]. We observed that both the coefficient of variation and autocorrelation increased before population collapse in two slowly deteriorating environments, one with a rising death rate and the other one with decreasing nutrient availability. We compared the performance of early warning signals across multiple environments as “indicators for loss of resilience.” We find that the varying performance is determined by how a system responds to changes in a specific driver, which can be captured by a relation between stability (recovery rate) and resilience (size of the basin of attraction). Furthermore, we demonstrate that the positive correlation between stability and resilience, as the essential assumption of indicators based on critical slowing down, can break down in this system when multiple environmental drivers are changed simultaneously. Our results suggest that the stability–resilience relation needs to be better understood for the application of early warning signals in different scenarios. PMID:26216946

Integrated material state awareness system with self-learning symbiotic diagnostic algorithms and models

NASA Astrophysics Data System (ADS)

Banerjee, Sourav; Liu, Lie; Liu, S. T.; Yuan, Fuh-Gwo; Beard, Shawn

2011-04-01

Materials State Awareness (MSA) goes beyond traditional NDE and SHM in its challenge to characterize the current state of material damage before the onset of macro-damage such as cracks. A highly reliable, minimally invasive system for MSA of Aerospace Structures, Naval structures as well as next generation space systems is critically needed. Development of such a system will require a reliable SHM system that can detect the onset of damage well before the flaw grows to a critical size. Therefore, it is important to develop an integrated SHM system that not only detects macroscale damages in the structures but also provides an early indication of flaw precursors and microdamages. The early warning for flaw precursors and their evolution provided by an SHM system can then be used to define remedial strategies before the structural damage leads to failure, and significantly improve the safety and reliability of the structures. Thus, in this article a preliminary concept of developing the Hybrid Distributed Sensor Network Integrated with Self-learning Symbiotic Diagnostic Algorithms and Models to accurately and reliably detect the precursors to damages that occur to the structure are discussed. Experiments conducted in a laboratory environment shows potential of the proposed technique.

Monitoring Regional Forest Disturbances across the US with Near Real Time MODIS NDVI Products included in the ForWarn Forest Threat Early Warning System

NASA Technical Reports Server (NTRS)

Spruce, Joseph; Hargrove, William W.; Gasser, Gerald; Norman, Steve

2013-01-01

U.S. forests occupy approx.1/3 of total land area (approx. 304 million ha). Since 2000, a growing number of regionally evident forest disturbances have occurred due to abiotic and biotic agents. Regional forest disturbances can threaten human life and property, bio-diversity and water supplies. Timely regional forest disturbance monitoring products are needed to aid forest health management work. Near Real Time (NRT) twice daily MODIS NDVI data provide a means to monitor U.S. regional forest disturbances every 8 days. Since 2010, these NRT forest change products have been produced and posted on the US Forest Service ForWarn Early Warning System for Forest Threats.

Early warning signals for critical transitions in a thermoacoustic system

PubMed Central

Gopalakrishnan, E. A.; Sharma, Yogita; John, Tony; Dutta, Partha Sharathi; Sujith, R. I.

2016-01-01

Dynamical systems can undergo critical transitions where the system suddenly shifts from one stable state to another at a critical threshold called the tipping point. The decrease in recovery rate to equilibrium (critical slowing down) as the system approaches the tipping point can be used to identify the proximity to a critical transition. Several measures have been adopted to provide early indications of critical transitions that happen in a variety of complex systems. In this study, we use early warning indicators to predict subcritical Hopf bifurcation occurring in a thermoacoustic system by analyzing the observables from experiments and from a theoretical model. We find that the early warning measures perform as robust indicators in the presence and absence of external noise. Thus, we illustrate the applicability of these indicators in an engineering system depicting critical transitions. PMID:27767065

Development and Experimental Application of International Affairs Indicators. Volume B. Supportive Research

DTIC Science & Technology

1974-06-01

i INDICATORS Approaches to Early Warning The Quantitative Indicators Approach: A ... 5 Summary Indicators and Early Warning...Indicators for Early ^ Wa rning 3. Quantitative Signs of Unusual or Ominous Activity 9 13 4. Simulated Cables 25 5 . TEXSCAN...Behavior) for Czechoslovakia 1 mmmmm mmm LIST OF FIGURES vi Pa£« 1. 2. 3. 4. 5 . 6. 7. 8. 9. 10. U. 12. 13. 14. 15. 16. 17. 18

Neural correlates of cigarette health warning avoidance among smokers.

PubMed

Stothart, George; Maynard, Olivia; Lavis, Rosie; Munafò, Marcus

2016-04-01

Eye-tracking technology has indicated that daily smokers actively avoid pictorial cigarette package health warnings. Avoidance may be due to a pre-cognitive perceptual bias or a higher order cognitive bias, such as reduced emotional processing. Using electroencephalography (EEG), this study aimed to identify the temporal point at which smokers' responses to health warnings begin to differ. Non-smokers (n=20) and daily smokers (n=20) viewed pictorial cigarette package health warnings and neutral control stimuli. These elicited Event Related Potentials reflecting early perceptual processing (visual P1), pre-attentive change detection (visual Mismatch Negativity), selective attentional orientation (P3) and a measure of emotional processing, the Late Positive Potential (LPP). There was no evidence for a difference in P1 responses between smokers and non-smokers. There was no difference in vMMN and P3 amplitude but some evidence for a delay in vMMN latency amongst smokers. There was strong evidence for delayed and reduced LPP to health warning stimuli amongst smokers compared to non-smokers. We find no evidence for an early perceptual bias in smokers' visual perception of health warnings but strong evidence that smokers are less sensitive to the emotional content of cigarette health warnings. Future health warning development should focus on increasing the emotional salience of pictorial health warning content amongst smokers. Copyright © 2016 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Adolescents perceived effectiveness of the proposed European graphic tobacco warning labels.

PubMed

Vardavas, Constantine I; Connolly, Gregory; Karamanolis, Kostas; Kafatos, Anthony

2009-04-01

Graphical tobacco product labelling is a prominent source of health information and has an important position among tobacco control initiatives. However, little is known about its effectiveness among adolescents. With this above in mind, we aimed to research into how adolescents perceive the proposed EU graphic tobacco product warning labels as an effective means of preventing smoking initiation in comparison to the current EU text-only warning labels. Five hundred seventy four adolescents (13-18, 54% male) from Greece were privately interviewed, with the use of a digital questionnaire and randomly shown seven existing EU text-only and proposed EU graphic warning labels. Non-smoking respondents were asked to compare and rate the warnings effectiveness in regard to preventing them from smoking on a 1-5 Likert type scale. Irrespective of the warning category shown, on all occasions, non-smoking adolescents rated the suggested EU graphic labels as more effective in preventing them from smoking in comparison to the existing EU text-only warnings. Controlling for gender, age, current smoking status and number of cigarettes smoked per month, younger adolescents were found to opt for graphic warnings more often, and also perceive graphic warning labels as a more effective means of preventing them from smoking, in comparison to their elder peers (P < 0.001). The proposed EU graphic warning labels may play an important role in preventing of smoking initiation during the crucial years of early adolescence when smoking experimentation and early addiction usually take place.

Big Data and the Global Public Health Intelligence Network (GPHIN)

PubMed Central

Dion, M; AbdelMalik, P; Mawudeku, A

2015-01-01

Background Globalization and the potential for rapid spread of emerging infectious diseases have heightened the need for ongoing surveillance and early detection. The Global Public Health Intelligence Network (GPHIN) was established to increase situational awareness and capacity for the early detection of emerging public health events. Objective To describe how the GPHIN has used Big Data as an effective early detection technique for infectious disease outbreaks worldwide and to identify potential future directions for the GPHIN. Findings Every day the GPHIN analyzes over more than 20,000 online news reports (over 30,000 sources) in nine languages worldwide. A web-based program aggregates data based on an algorithm that provides potential signals of emerging public health events which are then reviewed by a multilingual, multidisciplinary team. An alert is sent out if a potential risk is identified. This process proved useful during the Severe Acute Respiratory Syndrome (SARS) outbreak and was adopted shortly after by a number of countries to meet new International Health Regulations that require each country to have the capacity for early detection and reporting. The GPHIN identified the early SARS outbreak in China, was credited with the first alert on MERS-CoV and has played a significant role in the monitoring of the Ebola outbreak in West Africa. Future developments are being considered to advance the GPHIN’s capacity in light of other Big Data sources such as social media and its analytical capacity in terms of algorithm development. Conclusion The GPHIN’s early adoption of Big Data has increased global capacity to detect international infectious disease outbreaks and other public health events. Integration of additional Big Data sources and advances in analytical capacity could further strengthen the GPHIN’s capability for timely detection and early warning. PMID:29769954

Detecting lane departures from steering wheel signal.

PubMed

Sandström, Max; Lampsijärvi, Eetu; Holmström, Axi; Maconi, Göran; Ahmadzai, Shabana; Meriläinen, Antti; Hæggström, Edward; Forsman, Pia

2017-02-01

Current lane departure warning systems are video-based and lose data when road- and weather conditions are bad. This study sought to develop a lane departure warning algorithm based on the signal drawn from the steering wheel. The rationale is that a car-based lane departure warning system should be robust regardless of road- and weather conditions. N=34 professional driver students drove in a high-fidelity driving simulator at 80km/h for 55min every third hour during 36h of sustained wakefulness. During each driving session we logged the steering wheel- and lane position signals at 60Hz. To derive the lane position signal, we quantified the transfer function of the simulated vehicle and used it to derive the absolute lane position signal from the steering wheel signal. The Pearson correlation between the derived- and actual lane position signals was r=0.48 (based on 12,000km). Next we designed an algorithm that alerted, up to three seconds before they occurred, about upcoming lane deviations that exceeded 0.2m. The sensitivity of the algorithm was 47% and the specificity was 71%. To our knowledge this exceeds the performance of the current video-based systems. Copyright © 2016 Elsevier Ltd. All rights reserved.

Development and Use of Early Warning Systems. SLDS Spotlight

ERIC Educational Resources Information Center

Curtin, Jenny; Hurwitch, Bill; Olson, Tom

2012-01-01

An early warning system is a data-based tool that helps predict which students are on the right path towards eventual graduation or other grade-appropriate goals. Through such systems, stakeholders at the school and district levels can view data from a wide range of perspectives and gain a deeper understanding of student data. This "Statewide…

Vantage point - Early warning flaws.

PubMed

Swinden, Donna

2014-08-28

USING AN EARLY warning score (EWS) system should improve the detection of acutely deteriorating patients. Under such a system, a score is allocated to each of six physiological measurements including respiratory rate and oxygen saturations, which are aggregated to produce an overall score. An aggregated score of seven or higher prompts nursing staff to refer a patient for emergency assessment.

Early Warning Indicator System: Supporting K-12 Educators in the Identification, Support, and Monitoring of At-Risk Students

ERIC Educational Resources Information Center

Massachusetts Department of Elementary and Secondary Education, 2016

2016-01-01

A rise in data availability gives educators the opportunity to tailor instructional practices and interventions to student needs and invest resources in areas where students require the most support. Massachusetts developed the Early Warning Indicator System (EWIS), which synthesizes the wealth of student data available in the state, including…

Regional drought early warning, impacts, and assessment for water and agriculture in the lower Rio Grande basin, 2016-2017

USDA-ARS?s Scientific Manuscript database

USDA’s Southern Plains Climate Hub (SPCH) and the University of Oklahoma’s Southern Climate Impacts Planning Program (SCIPP) contributed to a broad, multi-partnered effort to provide drought early warning information to water and agriculture management interests in the middle and lower Rio Grande ba...

A Practitioner's Guide to Implementing Early Warning Systems. REL 2015-056

ERIC Educational Resources Information Center

Frazelle, Sarah; Nagel, Aisling

2015-01-01

To stem the tide of students dropping out, many schools and districts are turning to early warning systems (EWS) that signal whether a student is at risk of not graduating from high school. While some research exists about establishing these systems, there is little information about the actual implementation strategies that are being used across…

An Analysis of the 1992 New Jersey Grade 8 Early Warning Test.

ERIC Educational Resources Information Center

Tambini, Robert F.

The quality and the effectiveness of the 1992 New Jersey Grade 8 Early Warning Test (NJEWT) are assessed. Standardized tests possess clear advantages for educators, especially in the case of administration and scoring, but there are clear disadvantages as well, including the possibility of bias. Four criteria are applied to the NJEWT: adequacy,…

Overview and highlights of Early Warning and Crop Condition Assessment project

NASA Technical Reports Server (NTRS)

Boatwright, G. O.; Whitehead, V. S.

1985-01-01

Work of the Early Warning and Crop Condition Assessment (EW/CCA) project, one of eight projects in the Agriculture and Resources Inventory Surveys Through Aerospace Remote Sensing (AgRISTARS), is reviewed. Its mission, to develop and test remote sensing techniques that enhance operational methodologies for crop condition assessment, was in response to initiatives issued by the Secretary of Agriculture. Meteorologically driven crop stress indicator models have been developed or modified for wheat, maize, grain sorghum, and soybeans. These models provide early warning alerts of potential or actual crop stresses due to water deficits, adverse temperatures, and water excess that could delay planting or harvesting operations. Recommendations are given for future research involving vegetative index numbers and the NOAA and Landsat satellites.

[New international initiatives to create systems of effective risk prediction and food safety].

PubMed

Efimochkinal, N R; Bagryantseva, E C; Dupouy, E C; Khotimchenko, S A; Permyakov, E V; Sheveleva, S A; Arnautov, O V

2016-01-01

Ensuring food safety is one of the most important problems that is directly related to health protection of the population. The problem is particularly relevant on aglobalscale because ofincreasingnumberoffood-borne diseases andimportance of the health consequence early detection. In accordance with the position of the Codex Alimentarius Commission, food safety concept also includes quality. In this case, creation of the national, supranational and international early warning systems related to the food safety, designed with the purpose to prevent or minimize risks on different stages of the food value chain in various countries, regions and climate zones specific to national nutrition and lifestyle in different groups of population, gains particular importance. The article describes the principles and working examples of international, supranational and national food safety early warning systems. Great importance is given to the hazards of microbial origin - emergent pathogens. Example of the rapid reaction to the appearance of cases, related to the melanin presence in infant formula, are presented. Analysis of the current food safety and quality control system in Russian Federation shows that main improvements are mostly related to the development of the efficient monitoring, diagnostics and rapid alert procedures forfood safety on interregional and international levels that will allow to estimate real contamination of food with the most dangerous pathogens, chemical and biological contaminants, and the development of the electronic database and scientifically proved algorithms for food safety and quality management for targeted prevention activities against existing and emerging microbiological and other etiology risks, and public health protection.

Detection of convective initiation using Meteosat SEVIRI: implementation in and verification with the tracking and nowcasting algorithm Cb-TRAM

NASA Astrophysics Data System (ADS)

Merk, D.; Zinner, T.

2013-02-01

In this paper a new detection scheme for Convective Initation (CI) under day and night conditions is presented. The new algorithm combines the strengths of two existing methods for detecting Convective Initation with geostationary satellite data and uses the channels of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG). For the new algorithm five infrared criteria from the Satellite Convection Analysis and Tracking algorithm (SATCAST) and one High Resolution Visible channel (HRV) criteria from Cb-TRAM were adapted. This set of criteria aims for identifying the typical development of quickly developing convective cells in an early stage. The different criteria include timetrends of the 10.8 IR channel and IR channel differences as well as their timetrends. To provide the trend fields an optical flow based method is used, the Pyramidal Matching algorithm which is part of Cb-TRAM. The new detection scheme is implemented in Cb-TRAM and is verified for seven days which comprise different weather situations in Central Europe. Contrasted with the original early stage detection scheme of Cb-TRAM skill scores are provided. From the comparison against detections of later thunderstorm stages, which are also provided by Cb-TRAM, a decrease in false prior warnings (false alarm ratio) from 91 to 81% is presented, an increase of the critical success index from 7.4 to 12.7%, and a decrease of the BIAS from 320 to 146% for normal scan mode. Similar trends are found for rapid scan mode. Most obvious is the decline of false alarms found for synoptic conditions with upper cold air masses triggering convection.

Detection of convective initiation using Meteosat SEVIRI: implementation in and verification with the tracking and nowcasting algorithm Cb-TRAM

NASA Astrophysics Data System (ADS)

Merk, D.; Zinner, T.

2013-08-01

In this paper a new detection scheme for convective initiation (CI) under day and night conditions is presented. The new algorithm combines the strengths of two existing methods for detecting CI with geostationary satellite data. It uses the channels of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG). For the new algorithm five infrared (IR) criteria from the Satellite Convection Analysis and Tracking algorithm (SATCAST) and one high-resolution visible channel (HRV) criteria from Cb-TRAM were adapted. This set of criteria aims to identify the typical development of quickly developing convective cells in an early stage. The different criteria include time trends of the 10.8 IR channel, and IR channel differences, as well as their time trends. To provide the trend fields an optical-flow-based method is used: the pyramidal matching algorithm, which is part of Cb-TRAM. The new detection scheme is implemented in Cb-TRAM, and is verified for seven days which comprise different weather situations in central Europe. Contrasted with the original early-stage detection scheme of Cb-TRAM, skill scores are provided. From the comparison against detections of later thunderstorm stages, which are also provided by Cb-TRAM, a decrease in false prior warnings (false alarm ratio) from 91 to 81% is presented, an increase of the critical success index from 7.4 to 12.7%, and a decrease of the BIAS from 320 to 146% for normal scan mode. Similar trends are found for rapid scan mode. Most obvious is the decline of false alarms found for the synoptic class "cold air" masses.

An analysis of the early-warning system in emerging markets for reducing the financial crisis

NASA Astrophysics Data System (ADS)

Shen, Xiangguang; Song, Xiaozhong

2009-07-01

The large number of financial crises in emerging markets over the past ten years has left many observers, both from academia and financial institutions, puzzled by an apparent lack of homogenous causal relations between endogenous economic variables and the bursting of large financial shocks. The frequency of financial crises in the last 20 years can be attributed to the lack of a comprehensive theory of financial regulation to guide policy makers. Existing theories fail to define the range of regulatory models, the causes of regulatory failure, and how to measure and prevent it. Faulty design of regulatory models, and the lack of ongoing performance monitoring incorporating early warning systems, is disrupting economic and social development. The main aim of this article is to propose an early warning system (EWS) which purposes issuing warning signal against the possible financial crisis in the emerging market, and makes the emerging market survived the first wave of the crisis be able to continue their operation in the following years.

Packaging: a grounded theory of how to report physiological deterioration effectively.

PubMed

Andrews, Tom; Waterman, Heather

2005-12-01

The aim of this paper is to present a study of how ward-based staff use vital signs and the Early Warning Score to package physiological deterioration effectively to ensure successful referral to doctors. The literature tends to emphasize the identification of premonitory signs in predicting physiological deterioration. However, these signs lack sensitivity and specificity, and there is evidence that nurses rely on subjective and subtle indicators. The Early Warning Score was developed for the early detection of deterioration and has been widely implemented, with various modifications. The data reported here form part of a larger study investigating the practical problems faced by general ward staff in detecting physiological deterioration. During 2002, interviews and observations were carried out using a grounded theory approach, and a total of 44 participants were interviewed (30 nurses, 7 doctors and 7 health care support workers). Participants reported that quantifiable evidence is the most effective means of referring patients to doctors, and the Early Warning Score achieves this by improving communication between professionals. Rather than reporting changes in individual vital signs, the Early Warning Score effectively packages them together, resulting in a much more convincing referral. It gives nurses a precise, concise and unambiguous means of communicating deterioration, and confidence in using medical language. Thus, nurses are empowered and doctors can focus quickly on identified problems. The Early Warning Score leads to successful referral of patients by providing an agreed framework for assessment, increasing confidence in the use of medical language and empowering nurses. It is essential that nurses and nursing students are supported in its use and in developing confidence in using medical language by continued emphasis on physiology and pathophysiology in the nursing curriculum.

Which System Variables Carry Robust Early Signs of Upcoming Phase Transition? An Ecological Example.

PubMed

Negahbani, Ehsan; Steyn-Ross, D Alistair; Steyn-Ross, Moira L; Aguirre, Luis A

2016-01-01

Growth of critical fluctuations prior to catastrophic state transition is generally regarded as a universal phenomenon, providing a valuable early warning signal in dynamical systems. Using an ecological fisheries model of three populations (juvenile prey J, adult prey A and predator P), a recent study has reported silent early warning signals obtained from P and A populations prior to saddle-node (SN) bifurcation, and thus concluded that early warning signals are not universal. By performing a full eigenvalue analysis of the same system we demonstrate that while J and P populations undergo SN bifurcation, A does not jump to a new state, so it is not expected to carry early warning signs. In contrast with the previous study, we capture a significant increase in the noise-induced fluctuations in the P population, but only on close approach to the bifurcation point; it is not clear why the P variance initially shows a decaying trend. Here we resolve this puzzle using observability measures from control theory. By computing the observability coefficient for the system from the recordings of each population considered one at a time, we are able to quantify their ability to describe changing internal dynamics. We demonstrate that precursor fluctuations are best observed using only the J variable, and also P variable if close to transition. Using observability analysis we are able to describe why a poorly observable variable (P) has poor forecasting capabilities although a full eigenvalue analysis shows that this variable undergoes a bifurcation. We conclude that observability analysis provides complementary information to identify the variables carrying early-warning signs about impending state transition.

The analysis of behavior in orbit GSS two series of US early-warning system

NASA Astrophysics Data System (ADS)

Sukhov, P. P.; Epishev, V. P.; Sukhov, K. P.; Motrunych, I. I.

2016-09-01

Satellites Early Warning System Series class SBIRS US Air Force must replace on GEO early series DSP Series. During 2014-2016 the authors received more than 30 light curves "DSP-18 and "Sbirs-Geo 2". The analysis of the behavior of these satellites in orbit by a coordinate and photometric data. It is shown that for the monitoring of the Earth's surface is enough to place GEO 4 unit SBIRS across 90 deg.

Assessing the add value of ensemble forecast in a drought early warning

NASA Astrophysics Data System (ADS)

Calmanti, Sandro; Bosi, Lorenzo; Fernandez, Jesus; De Felice, Matteo

2015-04-01

The EU-FP7 project EUPORIAS is developing a prototype climate service to enhance the existing food security drought early warning system in Ethiopia. The Livelihoods, Early Assessment and Protection (LEAP) system is the Government of Ethiopia's national food security early warning system, established with the support of WFP and the World Bank in 2008. LEAP was designed to increase the predictability and timeliness of response to drought-related food crises in Ethiopia. It combines early warning with contingency planning and contingency funding, to allow the government, WFP and other partners to provide early assistance in anticipation of an impending catastrophes. Currently, LEAP uses satellite based rainfall estimates to monitor drought conditions and to compute needs. The main aim of the prototype is to use seasonal hindcast data to assess the added value of using ensemble climate rainfall forecasts to estimate the cost of assistance of population hit by major droughts. We outline the decision making process that is informed by the prototype climate service, and we discuss the analysis of the expected and skill of the available rainfall forecast data over Ethiopia. One critical outcome of this analysis is the strong dependence of the expected skill on the observational estimate assumed as reference. A preliminary evaluation of the full prototype products (drought indices and needs estimated) using hindcasts data will also be presented.

DISTANT EARLY WARNING SYSTEM for Tsunamis - A wide-area and multi-hazard approach

NASA Astrophysics Data System (ADS)

Hammitzsch, Martin; Lendholt, Matthias; Wächter, Joachim

2010-05-01

The DEWS (Distant Early Warning System) [1] project, funded under the 6th Framework Programme of the European Union, has the objective to create a new generation of interoperable early warning systems based on an open sensor platform. This platform integrates OGC [2] SWE [3] compliant sensor systems for the rapid detection of hazardous events, like earthquakes, sea level anomalies, ocean floor occurrences, and ground displacements in the case of tsunami early warning. Based on the upstream information flow DEWS focuses on the improvement of downstream capacities of warning centres especially by improving information logistics for effective and targeted warning message aggregation for a multilingual environment. Multiple telecommunication channels will be used for the dissemination of warning messages. Wherever possible, existing standards have been integrated. The Command and Control User Interface (CCUI), a rich client application based on Eclipse RCP (Rich Client Platform) [4] and the open source GIS uDig [5], integrates various OGC services. Using WMS (Web Map Service) [6] and WFS (Web Feature Service) [7] spatial data are utilized to depict the situation picture and to integrate a simulation system via WPS (Web Processing Service) [8] to identify affected areas. Warning messages are compiled and transmitted in the OASIS [9] CAP (Common Alerting Protocol) [10] standard together with addressing information defined via EDXL-DE (Emergency Data Exchange Language - Distribution Element) [11]. Internal interfaces are realized with SOAP [12] web services. Based on results of GITEWS [13] - in particular the GITEWS Tsunami Service Bus [14] - the DEWS approach provides an implementation for tsunami early warning systems but other geological paradigms are going to follow, e.g. volcanic eruptions or landslides. Therefore in future also multi-hazard functionality is conceivable. The specific software architecture of DEWS makes it possible to dock varying sensors to the system and to extend the CCUI with hazard specific functionality. The presentation covers the DEWS project, the system architecture and the CCUI in conjunction with details of information logistics. The DEWS Wide Area Centre connecting national centres to allow the international communication and warning exchange is presented also. REFERENCES: [1] DEWS, www.dews-online.org [2] OGC, www.opengeospatial.org [3] SWE, www.opengeospatial.org/projects/groups/sensorweb [4] Eclipse RCP, www.eclipse.org/home/categories/rcp.php [5] uDig, udig.refractions.net [6] WMS, www.opengeospatial.org/standards/wms [7] WFS, www.opengeospatial.org/standards/wfs [8] WPS, www.opengeospatial.org/standards/wps [9] OASIS, www.oasis-open.org [10] CAP, www.oasis-open.org/specs/#capv1.1 [11] EDXL-DE, www.oasis-open.org/specs/#edxlde-v1.0 [12] SOAP, www.w3.org/TR/soap [13] GITEWS (German Indonesian Tsunami Early Warning System) is a project of the German Federal Government to aid the recon¬struction of the tsunami-prone Indian Ocean region, www.gitews.org [14] The Tsunami Service Bus is the GITEWS sensor system integration platform offering standardised services for the detection and monitoring of tsunamis

An integrated earthquake early warning system and its performance at schools in Taiwan

NASA Astrophysics Data System (ADS)

Wu, Bing-Ru; Hsiao, Nai-Chi; Lin, Pei-Yang; Hsu, Ting-Yu; Chen, Chiou-Yun; Huang, Shieh-Kung; Chiang, Hung-Wei

2017-01-01

An earthquake early warning (EEW) system with integration of regional and onsite approaches was installed at nine demonstration stations in several districts of Taiwan for taking advantages of both approaches. The system performance was evaluated by a 3-year experiment at schools, which experienced five major earthquakes during this period. The blind zone of warning was effectively reduced by the integrated EEW system. The predicted intensities from EEW demonstration stations showed acceptable accuracy compared to field observations. The operation experience from an earthquake event proved that students could calmly carry out correct action before the seismic wave arrived using some warning time provided by the EEW system. Through successful operation in practice, the integrated EEW system was verified as an effective tool for disaster prevention at schools.

SafeLand guidelines for landslide monitoring and early warning systems in Europe - Design and required technology

NASA Astrophysics Data System (ADS)

Bazin, S.

2012-04-01

Landslide monitoring means the comparison of landslide characteristics like areal extent, speed of movement, surface topography and soil humidity from different periods in order to assess landslide activity. An ultimate "universal" methodology for this purpose does not exist; every technology has its own advantages and disadvantages. End-users should carefully consider each one to select the methodologies that represent the best compromise between pros and cons, and are best suited for their needs. Besides monitoring technology, there are many factors governing the choice of an Early Warning System (EWS). A people-centred EWS necessarily comprises five key elements: (1) knowledge of the risks; (2) identification, monitoring, analysis and forecasting of the hazards; (3) operational centre; (4) communication or dissemination of alerts and warnings; and (5) local capabilities to respond to the warnings received. The expression "end-to-end warning system" is also used to emphasize that EWSs need to span all steps from hazard detection through to community response. The aim of the present work is to provide guidelines for establishing the different components for landslide EWSs. One of the main deliverables of the EC-FP7 SafeLand project addresses the technical and practical issues related to monitoring and early warning for landslides, and identifies the best technologies available in the context of both hazard assessment and design of EWSs. This deliverable targets the end-users and aims to facilitate the decision process by providing guidelines. For the purpose of sharing the globally accumulated expertise, a screening study was done on 14 EWSs from 8 different countries. On these bases, the report presents a synoptic view of existing monitoring methodologies and early-warning strategies and their applicability for different landslide types, scales and risk management steps. Several comprehensive checklists and toolboxes are also included to support informed decisions. The deliverable was compiled with contributions from experts on landslides, monitoring technologies, remote sensing, and social researchers from 16 European institutions. The deliverable addresses one of the main objectives of the SafeLand project, namely to merge experience and expert judgment and create synergies on European level towards guidelines for early warning and to make these results available to end-users and local stakeholders.

Performance Analysis of a Citywide Real-time Landslide Early Warning System in Korea

NASA Astrophysics Data System (ADS)

Park, Joon-Young; Lee, Seung-Rae; Kang, Sinhang; Lee, Deuk-hwan; Nedumpallile Vasu, Nikhil

2017-04-01

Rainfall-induced landslide has been one of the major disasters in Korea since the beginning of 21st century when the global climate change started to give rise to the growth of the magnitude and frequency of extreme precipitation events. In order to mitigate the increasing damage to properties and loss of lives and to provide an effective tool for public officials to manage the landslide disasters, a real-time landslide early warning system with an advanced concept has been developed by taking into account for Busan, the second largest metropolitan city in Korea, as an operational test-bed. The system provides with warning information based on a five-level alert scheme (Normal, Attention, Watch, Alert, and Emergency) using the forecasted/observed rainfall data or the data obtained from ground monitoring (volumetric water content and matric suction). The alert levels are determined by applying seven different thresholds in a step-wise manner following a decision tree. In the pursuit of improved reliability of an early warning level assigned to a specific area, the system makes assessments repetitively using the thresholds of different theoretical backgrounds including statistical(empirical), physically-based, and mathematical analyses as well as direct measurement-based approaches. By mapping the distribution of the five early warning levels determined independently for each of tens of millions grids covering the entire mountainous area of Busan, the regional-scale system can also provide with the early warning information for a specific local area. The fact that the highest warning level is determined by using a concept of a numerically-modelled potential debris-flow risk is another distinctive feature of the system. This study tested the system performance by applying it for four previous rainy seasons in order to validate the operational applicability. During the rainy seasons of 2009, 2011, and 2014, the number of landslides recorded throughout Busan's territory reached 156, 64, and 37, respectively. In 2016, only three landslides were recorded even though the city experienced a couple of heavy rainfall events during the rainy season. The system performance test results show good agreement with the observation results for the past rainfall events. It seems that the system can also provide with reliable warning information for the future rainfall events.

Preliminary numerical simulations of the 27 February 2010 Chile tsunami: first results and hints in a tsunami early warning perspective

NASA Astrophysics Data System (ADS)

Tinti, S.; Tonini, R.; Armigliato, A.; Zaniboni, F.; Pagnoni, G.; Gallazzi, Sara; Bressan, Lidia

2010-05-01

The tsunamigenic earthquake (M 8.8) that occurred offshore central Chile on 27 February 2010 can be classified as a typical subduction-zone earthquake. The effects of the ensuing tsunami have been devastating along the Chile coasts, and especially between the cities of Valparaiso and Talcahuano, and in the Juan Fernandez islands. The tsunami propagated across the entire Pacific Ocean, hitting with variable intensity almost all the coasts facing the basin. While the far-field propagation was quite well tracked almost in real-time by the warning centres and reasonably well reproduced by the forecast models, the toll of lives and the severity of the damage caused by the tsunami in the near-field occurred with no local alert nor warning and sadly confirms that the protection of the communities placed close to the tsunami sources is still an unresolved problem in the tsunami early warning field. The purpose of this study is two-fold. On one side we perform numerical simulations of the tsunami starting from different earthquake models which we built on the basis of the preliminary seismic parameters (location, magnitude and focal mechanism) made available by the seismological agencies immediately after the event, or retrieved from more detailed and refined studies published online in the following days and weeks. The comparison with the available records of both offshore DART buoys and coastal tide-gauges is used to put some preliminary constraints on the best-fitting fault model. The numerical simulations are performed by means of the finite-difference code UBO-TSUFD, developed and maintained by the Tsunami Research Team of the University of Bologna, Italy, which can solve both the linear and non-linear versions of the shallow-water equations on nested grids. The second purpose of this study is to use the conclusions drawn in the previous part in a tsunami early warning perspective. In the framework of the EU-funded project DEWS (Distant Early Warning System), we will try to give some clues for discussion on the deficiencies of the existing tsunami early warning concepts as regards the warning to the areas which are found close to the tsunami source, and on the strategies that should be followed in the near future in order to make significant progress in the protection and safeguarding of local communities.

Patient attitudes towards remote continuous vital signs monitoring on general surgery wards: An interview study.

PubMed

Downey, C L; Brown, J M; Jayne, D G; Randell, R

2018-06-01

Vital signs monitoring is used to identify deteriorating patients in hospital. The most common tool for vital signs monitoring is an early warning score, although emerging technologies allow for remote, continuous patient monitoring. A number of reviews have examined the impact of continuous monitoring on patient outcomes, but little is known about the patient experience. This study aims to discover what patients think of monitoring in hospital, with a particular emphasis on intermittent early warning scores versus remote continuous monitoring, in order to inform future implementations of continuous monitoring technology. Semi-structured interviews were undertaken with 12 surgical inpatients as part of a study testing a remote continuous monitoring device. All patients were monitored with both an early warning score and the new device. Interviews were audio-recorded, transcribed verbatim and analysed using thematic analysis. Patients can see the value in remote, continuous monitoring, particularly overnight. However, patients appreciate the face-to-face aspect of early warning score monitoring as it allows for reassurance, social interaction, and gives them further opportunity to ask questions about their medical care. Early warning score systems are widely used to facilitate detection of the deteriorating patient. Continuous monitoring technologies may provide added reassurance. However, patients value personal contact with their healthcare professionals and remote monitoring should not replace this. We suggest that remote monitoring is best introduced in a phased manner, and initially as an adjunct to usual care, with careful consideration of the patient experience throughout. Copyright © 2018 Elsevier B.V. All rights reserved.

Developing a drought early warning information system for coastal ecosystems in the Carolinas

Treesearch

Kirsten Lackstrom; Amanda Brennan; Paul Conrads; Lisa Darby; Kirstin Dow; Daniel Tuford

2016-01-01

The National Integrated Drought Information System (NIDIS) and the Carolinas Integrated Sciences and Assessments (CISA), a National Oceanic and Atmospheric Administration (NOAA)- funded Regional Integrated Sciences and Assessments (RISA) program, are partnering to develop and support a Carolinas Drought Early Warning System pilot program. Research and projects focus on...

Comparing Methodologies for Developing an Early Warning System: Classification and Regression Tree Model versus Logistic Regression. REL 2015-077

ERIC Educational Resources Information Center

Koon, Sharon; Petscher, Yaacov

2015-01-01

The purpose of this report was to explicate the use of logistic regression and classification and regression tree (CART) analysis in the development of early warning systems. It was motivated by state education leaders' interest in maintaining high classification accuracy while simultaneously improving practitioner understanding of the rules by…

Early warning signals of regime shifts from cross-scale connectivity of land-cover patterns

Treesearch

Giovanni Zurlini; Kenneth Bruce Jones; Kurt Hans Riitters; Bai-Lian Li; Irene Petrosillo

2014-01-01

Increasing external pressures from human activities and climate change can lead to desertification, affecting the livelihood of more than 25% of the world’s population. Thus, determining proximity to transition to desertification is particularly central for arid regions before they may convert into deserts, and recent research has focused on devising early warning...

On Track for Success: The Use of Early Warning Indicator and Intervention Systems to Build a Grad Nation

ERIC Educational Resources Information Center

Bruce, Mary; Bridgeland, John M.; Fox, Joanna Hornig; Balfanz, Robert

2011-01-01

Over the past decade, schools, districts, and states have become increasingly savvy with data collection and analysis to drive student outcomes. The development and use of Early Warning Indicator and Intervention Systems (EWS) are at the cutting edge of the data- driven, outcomes-focused, high-impact education movement. These systems can increase…

Of Needles and Haystacks: Building an Accurate Statewide Dropout Early Warning System in Wisconsin

ERIC Educational Resources Information Center

Knowles, Jared E.

2015-01-01

The state of Wisconsin has one of the highest four year graduation rates in the nation, but deep disparities among student subgroups remain. To address this the state has created the Wisconsin Dropout Early Warning System (DEWS), a predictive model of student dropout risk for students in grades six through nine. The Wisconsin DEWS is in use…

Detection of the Early Warning Signs of Cancer by Community Pharmacists: An Evaluation of Training on Professional Behavior

ERIC Educational Resources Information Center

Benfield, William R.; And Others

1977-01-01

In a study of 702 pharmacists in 211 communities, an effort was made to determine the effect of a unit of education on the community pharmacist's ability and/or tendency to detect the early warning signs of cancer when manifested by patrons. The success of such a program is shown. (LBH)

Four Signs Your District Is Ready for an Early Warning System. A Discussion Guide

ERIC Educational Resources Information Center

Regional Educational Laboratory Pacific, 2016

2016-01-01

Although high school graduation rates continue to rise in the United States, reaching 81 percent in the 2012-2013 school year (U.S. Department of Education, 2015), dropout remains a pervasive issue for education systems across the nation. In recent years, Early Warning Systems (EWS), which utilize administrative data to identify students at risk…

Toward a national early warning system for forest disturbances using remotely sensed canopy phenology

Treesearch

William W. Hargrove; Joseph P. Spruce; Gerald E. Gasser; Forrest M. Hoffman

2009-01-01

Imagine a national system with the ability to quickly identify forested areas under attack from insects or disease. Such an early warning system might minimize surprises such as the explosion of caterpillars referred to in the quotation above. Moderate resolution (ca. 500m) remote sensing repeated at frequent (ca. weekly) intervals could power such a monitoring system...

Geospatiotemporal data mining in an early warning system for forest threats in the United States

Treesearch

F.M. Hoffman; R.T. Mills; J. Kumar; S.S. Vulli; W.W. Hargrove

2010-01-01

We investigate the potential of geospatiotemporal data mining of multi-year land surface phenology data (250 m Normalized Difference Vegetation Index (NDVI) values derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) in this study) for the conterminous United States as part of an early warning system to identify threats to forest ecosystems. Cluster...

Application of satellite products and hydrological modelling for flood early warning

NASA Astrophysics Data System (ADS)

Koriche, Sifan A.; Rientjes, Tom H. M.

2016-06-01

Floods have caused devastating impacts to the environment and society in Awash River Basin, Ethiopia. Since flooding events are frequent, this marks the need to develop tools for flood early warning. In this study, we propose a satellite based flood index to identify the runoff source areas that largely contribute to extreme runoff production and floods in the basin. Satellite based products used for development of the flood index are CMORPH (Climate Prediction Center MORPHing technique: 0.25° by 0.25°, daily) product for calculation of the Standard Precipitation Index (SPI) and a Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) for calculation of the Topographic Wetness Index (TWI). Other satellite products used in this study are for rainfall-runoff modelling to represent rainfall, potential evapotranspiration, vegetation cover and topography. Results of the study show that assessment of spatial and temporal rainfall variability by satellite products may well serve in flood early warning. Preliminary findings on effectiveness of the flood index developed in this study indicate that the index is well suited for flood early warning. The index combines SPI and TWI, and preliminary results illustrate the spatial distribution of likely runoff source areas that cause floods in flood prone areas.

Demonstration of the Cascadia G‐FAST geodetic earthquake early warning system for the Nisqually, Washington, earthquake

USGS Publications Warehouse

Crowell, Brendan; Schmidt, David; Bodin, Paul; Vidale, John; Gomberg, Joan S.; Hartog, Renate; Kress, Victor; Melbourne, Tim; Santillian, Marcelo; Minson, Sarah E.; Jamison, Dylan

2016-01-01

A prototype earthquake early warning (EEW) system is currently in development in the Pacific Northwest. We have taken a two‐stage approach to EEW: (1) detection and initial characterization using strong‐motion data with the Earthquake Alarm Systems (ElarmS) seismic early warning package and (2) the triggering of geodetic modeling modules using Global Navigation Satellite Systems data that help provide robust estimates of large‐magnitude earthquakes. In this article we demonstrate the performance of the latter, the Geodetic First Approximation of Size and Time (G‐FAST) geodetic early warning system, using simulated displacements for the 2001Mw 6.8 Nisqually earthquake. We test the timing and performance of the two G‐FAST source characterization modules, peak ground displacement scaling, and Centroid Moment Tensor‐driven finite‐fault‐slip modeling under ideal, latent, noisy, and incomplete data conditions. We show good agreement between source parameters computed by G‐FAST with previously published and postprocessed seismic and geodetic results for all test cases and modeling modules, and we discuss the challenges with integration into the U.S. Geological Survey’s ShakeAlert EEW system.

Tipping point analysis of seismological data

NASA Astrophysics Data System (ADS)

Livina, Valerie N.; Tolkova, Elena

2014-05-01

We apply the tipping point toolbox [1-7] to study sensor data of pressure variations and vertical velocity of the sea floor after two seismic events: 21 October 2010, M6.9, D10km (California) and 11 March 2011, M9.0, D30km (Japan). One type of datasets was measured by nano-resolution pressure sensor [8], while the other, for comparison, by a co-located ocean bottom seismometer. Both sensors registered the seismic wave, and we investigated the early warning and detection signals of the wave arrival for possible application with a remote and cabled tsunami warning detector network (NOAA DART system and Japan Trench Tsunami Observation System). We study the early warning and detection signals of the wave arrival using methodology that combines degenerate fingerprinting and potential analysis techniques for anticipation, detection and forecast of tipping points in a dynamical system. Degenerate fingerprinting indicator is a dynamically derived lag-1 autocorrelation, ACF (or, alternatively, short-range scaling exponent of Detrended Fluctuation Analysis, DFA [1]), which shows short-term memory in a series. When such values rise monotonically, this indicates an upcoming transition or bifurcation in a series and can be used for early warning signals analysis. The potential analysis detects a transition or bifurcation in a series at the time when it happens, which is illustrated in a special contour plot mapping the potential dynamics of the system [2-6]. The methodology has been extensively tested on artificial data and on various geophysical, ecological and industrial sensor datasets [2-5,7], and proved to be applicable to trajectories of dynamical systems of arbitrary origin [9]. In this seismological application, we have obtained early warning signals in the described series using ACF- and DFA-indicators and detected the Rayleigh wave arrival in the potential contour plots. In the case of the event in 2010, the early warning signal starts appearing about 2 min before the first peak of the Rayleigh train is detected by the sensor, whereas in the case of event of 2011, the early warning signal appears closer to the peak arrival, within 1 min. The different strength of early warning signals of the Rayleigh trains may be due to different depths of the events (10 and 30 km), which we plan to test in further analysis. References: [1] Livina and Lenton, GRL 2007; [2] Livina et al, Climate of the Past 2010; [3] Livina et al, Climate Dynamics 2011; [4] Livina et al, Physica A 2012; [5] Livina and Lenton, Cryosphere 2013; [6] Livina et al, Physica A 2013; [7] Livina et al, Journal of Civil Structural Health Monitoring, in press; [8] Tolkova and Schaad, arXiv:1401.0096v1; [9] Vaz Martins et al, PRE 2010.

Performance analysis of landslide early warning systems at regional scale: the EDuMaP method

NASA Astrophysics Data System (ADS)

Piciullo, Luca; Calvello, Michele

2016-04-01

Landslide early warning systems (LEWSs) reduce landslide risk by disseminating timely and meaningful warnings when the level of risk is judged intolerably high. Two categories of LEWSs, can be defined on the basis of their scale of analysis: "local" systems and "regional" systems. LEWSs at regional scale (ReLEWSs) are used to assess the probability of occurrence of landslides over appropriately-defined homogeneous warning zones of relevant extension, typically through the prediction and monitoring of meteorological variables, in order to give generalized warnings to the public. Despite many studies on ReLEWSs, no standard requirements exist for assessing their performance. Empirical evaluations are often carried out by simply analysing the time frames during which significant high-consequence landslides occurred in the test area. Alternatively, the performance evaluation is based on 2x2 contingency tables computed for the joint frequency distribution of landslides and alerts, both considered as dichotomous variables. In all these cases, model performance is assessed neglecting some important aspects which are peculiar to ReLEWSs, among which: the possible occurrence of multiple landslides in the warning zone; the duration of the warnings in relation to the time of occurrence of the landslides; the level of the warning issued in relation to the landslide spatial density in the warning zone; the relative importance system managers attribute to different types of errors. An original approach, called EDuMaP method, is proposed to assess the performance of landslide early warning models operating at regional scale. The method is composed by three main phases: Events analysis, Duration Matrix, Performance analysis. The events analysis phase focuses on the definition of landslide (LEs) and warning events (WEs), which are derived from available landslides and warnings databases according to their spatial and temporal characteristics by means of ten input parameters. The evaluation of time associated with the occurrence of landslide events (LE) in relation to the occurrence of warning events (WE) in their respective classes is a fundamental step to determine the duration matrix elements. On the other hand the classification of LEs and WEs establishes the structure of the duration matrix. Indeed, the number of rows and columns of the matrix is equal to the number of classes defined for the warning and landslide events, respectively. Thus the matrix is not expressed as a 2x2 contingency and LEs and WEs are not expressed as dichotomous variables. The final phase of the method is the evaluation of the duration matrix based on a set of performance criteria assigning a performance meaning to the element of the matrix. To this aim different criteria can be defined, for instance employing an alert classification scheme derived from 2x2 contingency tables or assigning a colour code to the elements of the matrix in relation to their grade of correctness. Finally, performance indicators can be derived from the performance criteria to quantify successes and errors of the early warning models. EDuMaP has been already applied to different real case studies, highlighting the adaptability of the method to analyse the performance of structurally different ReLEWSs.

Capturing early signs of deterioration: the dutch-early-nurse-worry-indicator-score and its value in the Rapid Response System.

PubMed

Douw, Gooske; Huisman-de Waal, Getty; van Zanten, Arthur R H; van der Hoeven, Johannes G; Schoonhoven, Lisette

2017-09-01

To determine the predictive value of individual and combined dutch-early-nurse-worry-indicator-score indicators at various Early Warning Score levels, differentiating between Early Warning Scores reaching the trigger threshold to call a rapid response team and Early Warning Score levels not reaching this point. Dutch-early-nurse-worry-indicator-score comprises nine indicators underlying nurses' 'worry' about a patient's condition. All indicators independently show significant association with unplanned intensive care/high dependency unit admission or unexpected mortality. Prediction of this outcome improved by adding the dutch-early-nurse-worry-indicator-score indicators to an Early Warning Score based on vital signs. An observational cohort study was conducted on three surgical wards in a tertiary university-affiliated teaching hospital. Included were surgical, native-speaking, adult patients. Nurses scored presence of 'worry' and/or dutch-early-nurse-worry-indicator-score indicators every shift or when worried. Vital signs were measured according to the prevailing protocol. Unplanned intensive care/high dependency unit admission or unexpected mortality was the composite endpoint. Percentages of 'worry' and dutch-early-nurse-worry-indicator-score indicators were calculated at various Early Warning Score levels in control and event groups. Entering all dutch-early-nurse-worry-indicator-score indicators in a multiple logistic regression analysis, we calculated a weighted score and calculated sensitivity, specificity, positive predicted value and negative predicted value for each possible total score. In 3522 patients, 102 (2·9%) had an unplanned intensive care/high dependency unit admissions (n = 97) or unexpected mortality (n = 5). Patients with such events and only slightly changed vital signs had significantly higher percentages of 'worry' and dutch-early-nurse-worry-indicator-score indicators expressed than patients in the control group. Increasing number of dutch-early-nurse-worry-indicator-score indicators showed higher positive predictive values. Dutch-early-nurse-worry-indicator-score indicators alert in an early stage of deterioration, before reaching the trigger threshold to call a rapid response team and can improve interdisciplinary communication on surgical wards during regular rounds, and when calling for assistance. Dutch-early-nurse-worry-indicator-score structures communication and recording of signs known to be associated with a decline in a patient's condition and can empower nurses to call assistance on the 'worry' criterion in an early stage of deterioration. © 2016 John Wiley & Sons Ltd.

The limits of earthquake early warning: Timeliness of ground motion estimates

USGS Publications Warehouse

Minson, Sarah E.; Meier, Men-Andrin; Baltay, Annemarie S.; Hanks, Thomas C.; Cochran, Elizabeth S.

2018-01-01

The basic physics of earthquakes is such that strong ground motion cannot be expected from an earthquake unless the earthquake itself is very close or has grown to be very large. We use simple seismological relationships to calculate the minimum time that must elapse before such ground motion can be expected at a distance from the earthquake, assuming that the earthquake magnitude is not predictable. Earthquake early warning (EEW) systems are in operation or development for many regions around the world, with the goal of providing enough warning of incoming ground shaking to allow people and automated systems to take protective actions to mitigate losses. However, the question of how much warning time is physically possible for specified levels of ground motion has not been addressed. We consider a zero-latency EEW system to determine possible warning times a user could receive in an ideal case. In this case, the only limitation on warning time is the time required for the earthquake to evolve and the time for strong ground motion to arrive at a user’s location. We find that users who wish to be alerted at lower ground motion thresholds will receive more robust warnings with longer average warning times than users who receive warnings for higher ground motion thresholds. EEW systems have the greatest potential benefit for users willing to take action at relatively low ground motion thresholds, whereas users who set relatively high thresholds for taking action are less likely to receive timely and actionable information.

The limits of earthquake early warning: Timeliness of ground motion estimates

PubMed Central

Hanks, Thomas C.

2018-01-01

The basic physics of earthquakes is such that strong ground motion cannot be expected from an earthquake unless the earthquake itself is very close or has grown to be very large. We use simple seismological relationships to calculate the minimum time that must elapse before such ground motion can be expected at a distance from the earthquake, assuming that the earthquake magnitude is not predictable. Earthquake early warning (EEW) systems are in operation or development for many regions around the world, with the goal of providing enough warning of incoming ground shaking to allow people and automated systems to take protective actions to mitigate losses. However, the question of how much warning time is physically possible for specified levels of ground motion has not been addressed. We consider a zero-latency EEW system to determine possible warning times a user could receive in an ideal case. In this case, the only limitation on warning time is the time required for the earthquake to evolve and the time for strong ground motion to arrive at a user’s location. We find that users who wish to be alerted at lower ground motion thresholds will receive more robust warnings with longer average warning times than users who receive warnings for higher ground motion thresholds. EEW systems have the greatest potential benefit for users willing to take action at relatively low ground motion thresholds, whereas users who set relatively high thresholds for taking action are less likely to receive timely and actionable information. PMID:29750190

Design Principles for resilient cyber-physical Early Warning Systems - Challenges, Experiences, Design Patterns, and Best Practices

NASA Astrophysics Data System (ADS)

Gensch, S.; Wächter, J.; Schnor, B.

2014-12-01

Early warning systems (EWS) are safety-critical IT-infrastructures that serve the purpose of potentially saving lives or assets by observing real-world phenomena and issuing timely warning products to authorities and communities. An EWS consists of sensors, communication networks, data centers, simulation platforms, and dissemination channels. The components of this cyber-physical system may all be affected by both natural hazards and malfunctions of components alike. Resilience engineering so far has mostly been applied to safety-critical systems and processes in transportation (aviation, automobile), construction and medicine. Early warning systems need equivalent techniques to compensate for failures, and furthermore means to adapt to changing threats, emerging technology and research findings. We present threats and pitfalls from our experiences with the German and Indonesian tsunami early warning system, as well as architectural, technological and organizational concepts employed that can enhance an EWS' resilience. The current EWS is comprised of a multi-type sensor data upstream part, different processing and analysis engines, a decision support system, and various warning dissemination channels. Each subsystem requires a set of approaches towards ensuring stable functionality across system layer boundaries, including also institutional borders. Not only must services be available, but also produce correct results. Most sensors are distributed components with restricted resources, communication channels and power supply. An example for successful resilience engineering is the power capacity based functional management for buoy and tide gauge stations. We discuss various fault-models like cause and effect models on linear pathways, interaction of multiple events, complex and non-linear interaction of assumedly reliable subsystems and fault tolerance means implemented to tackle these threats.

Research on Early Warning of Chinese Food Safety Based on Social Physics

NASA Astrophysics Data System (ADS)

Ma, Yonghuan; Niu, Wenyuan; Li, Qianqian

Based on social physics, this paper designs the index system of food safety, builds early warning model of food safety, calculates the degree of food safety, and assesses the state of early warning of 2007 in China. The result shows the degree of food safety is near 0.7 in securer state, belonging to slight emergency. It is much lower in eastern areas of developed regions, belonging to insecure state in the mass. That the food safety is ensured in major grain producing areas, Inner Mongolia, Ningxia and Xinjiang is the prerequisite of realizing the food safety of China. The result also shows four significant indices, grain production capacity, grain circulation order, grain demand and grain supply, which are important indicatio to control food safety.

Electric Field Sensor for Lightning Early Warning System

NASA Astrophysics Data System (ADS)

Premlet, B.; Mohammed, R.; Sabu, S.; Joby, N. E.

2017-12-01

Electric field mills are used popularly for atmospheric electric field measurements. Atmospheric Electric Field variation is the primary signature for Lightning Early Warning systems. There is a characteristic change in the atmospheric electric field before lightning during a thundercloud formation.A voltage controlled variable capacitance is being proposed as a method for non-contacting measurement of electric fields. A varactor based mini electric field measurement system is developed, to detect any change in the atmospheric electric field and to issue lightning early warning system. Since this is a low-cost device, this can be used for developing countries which are facing adversities. A network of these devices can help in forming a spatial map of electric field variations over a region, and this can be used for more improved atmospheric electricity studies in developing countries.

Early warning signals detect critical impacts of experimental warming.

PubMed

Jarvis, Lauren; McCann, Kevin; Tunney, Tyler; Gellner, Gabriel; Fryxell, John M

2016-09-01

Earth's surface temperatures are projected to increase by ~1-4°C over the next century, threatening the future of global biodiversity and ecosystem stability. While this has fueled major progress in the field of physiological trait responses to warming, it is currently unclear whether routine population monitoring data can be used to predict temperature-induced population collapse. Here, we integrate trait performance theory with that of critical tipping points to test whether early warning signals can be reliably used to anticipate thermally induced extinction events. We find that a model parameterized by experimental growth rates exhibits critical slowing down in the vicinity of an experimentally tested critical threshold, suggesting that dynamical early warning signals may be useful in detecting the potentially precipitous onset of population collapse due to global climate change.

Feasibility study of earthquake early warning (EEW) in Hawaii

USGS Publications Warehouse

Thelen, Weston A.; Hotovec-Ellis, Alicia J.; Bodin, Paul

2016-09-30

The effects of earthquake shaking on the population and infrastructure across the State of Hawaii could be catastrophic, and the high seismic hazard in the region emphasizes the likelihood of such an event. Earthquake early warning (EEW) has the potential to give several seconds of warning before strong shaking starts, and thus reduce loss of life and damage to property. The two approaches to EEW are (1) a network approach (such as ShakeAlert or ElarmS) where the regional seismic network is used to detect the earthquake and distribute the alarm and (2) a local approach where a critical facility has a single seismometer (or small array) and a warning system on the premises.The network approach, also referred to here as ShakeAlert or ElarmS, uses the closest stations within a regional seismic network to detect and characterize an earthquake. Most parameters used for a network approach require observations on multiple stations (typically 3 or 4), which slows down the alarm time slightly, but the alarms are generally more reliable than with single-station EEW approaches. The network approach also benefits from having stations closer to the source of any potentially damaging earthquake, so that alarms can be sent ahead to anyone who subscribes to receive the notification. Thus, a fully implemented ShakeAlert system can provide seconds of warning for both critical facilities and general populations ahead of damaging earthquake shaking.The cost to implement and maintain a fully operational ShakeAlert system is high compared to a local approach or single-station solution, but the benefits of a ShakeAlert system would be felt statewide—the warning times for strong shaking are potentially longer for most sources at most locations.The local approach, referred to herein as “single station,” uses measurements from a single seismometer to assess whether strong earthquake shaking can be expected. Because of the reliance on a single station, false alarms are more common than when using a regional network of seismometers. Given the current network, a single-station approach provides more warning for damaging earthquakes that occur close to the station, but it would have limited benefit compared to a fully implemented ShakeAlert system. For Honolulu, for example, the single-station approach provides an advantage over ShakeAlert only for earthquakes that occur in a narrow zone extending northeast and southwest of O‘ahu. Instrumentation and alarms associated with the single-station approach are typically maintained and assessed within the target facility, and thus no outside connectivity is required. A single-station approach, then, is unlikely to help broader populations beyond the individuals at the target facility, but they have the benefit of being commercially available for relatively little cost. The USGS Hawaiian Volcano Observatory (HVO) is the Advanced National Seismic System (ANSS) regional seismic network responsible for locating and characterizing earthquakes across the State of Hawaii. During 2014 and 2015, HVO tested a network-based EEW algorithm within the current seismic network in order to assess the suitability for building a full EEW system. Using the current seismic instrumentation and processing setup at HVO, it is possible for a network approach to release an alarm a little more than 3 seconds after the earthquake is recorded on the fourth seismometer. Presently, earthquakes having M≥3 detected with the ElarmS algorithm have an average location error of approximately 4.5 km and an average magnitude error of -0.3 compared to the reviewed catalog locations from the HVO. Additional stations and upgrades to existing seismic stations would serve to improve solution precision and warning times and additional staffing would be required to provide support for a robust, network-based EEW system. For a critical facility on the Island of Hawaiʻi, such as the telescopes atop Mauna Kea, one phased approach to mitigate losses could be to immediately install a single station system to establish some level of warning. Subsequently, supporting the implementation of a full network-based EEW system on the Island of Hawaiʻi would provide additional benefit in the form of improved warning times once the system is fully installed and operational, which may take several years. Distributed populations across the Hawaiian Islands, including those outside the major cities and far from the likely earthquake source areas, would likely only benefit from a network approach such as ShakeAlert to provide warnings of strong shaking.

The design of composite monitoring scheme for multilevel information in crop early diseases

NASA Astrophysics Data System (ADS)

Zhang, Yan; Meng, Qinglong; Shang, Jing

2018-02-01

It is difficult to monitor and predict the crops early diseases in that the crop disease monitoring is usually monitored by visible light images and the availabilities in early warning are poor at present. The features of common nondestructive testing technology applied to the crop diseases were analyzed in this paper. Based on the changeable characteristics of the virus from the incubation period to the onset period of crop activities, the multilevel composite information monitoring scheme were designed by applying infrared thermal imaging, visible near infrared hyperspectral imaging, micro-imaging technology to the monitoring of multilevel information of crop disease infection comprehensively. The early warning process and key monitoring parameters of compound monitoring scheme are given by taking the temperature, color, structure and texture of crops as the key monitoring characteristics of disease. With overcoming the deficiency that the conventional monitoring scheme is only suitable for the observation of diseases with naked eyes, the monitoring and early warning of the incubation and early onset of the infection crops can be realized by the composite monitoring program as mentioned in this paper.

Comparison of the Between the Flags calling criteria to the MEWS, NEWS and the electronic Cardiac Arrest Risk Triage (eCART) score for the identification of deteriorating ward patients.

PubMed

Green, Malcolm; Lander, Harvey; Snyder, Ashley; Hudson, Paul; Churpek, Matthew; Edelson, Dana

2018-02-01

Traditionally, paper based observation charts have been used to identify deteriorating patients, with emerging recent electronic medical records allowing electronic algorithms to risk stratify and help direct the response to deterioration. We sought to compare the Between the Flags (BTF) calling criteria to the Modified Early Warning Score (MEWS), National Early Warning Score (NEWS) and electronic Cardiac Arrest Risk Triage (eCART) score. Multicenter retrospective analysis of electronic health record data from all patients admitted to five US hospitals from November 2008-August 2013. Cardiac arrest, ICU transfer or death within 24h of a score RESULTS: Overall accuracy was highest for eCART, with an AUC of 0.801 (95% CI 0.799-0.802), followed by NEWS, MEWS and BTF respectively (0.718 [0.716-0.720]; 0.698 [0.696-0.700]; 0.663 [0.661-0.664]). BTF criteria had a high risk (Red Zone) specificity of 95.0% and a moderate risk (Yellow Zone) specificity of 27.5%, which corresponded to MEWS thresholds of >=4 and >=2, NEWS thresholds of >=5 and >=2, and eCART thresholds of >=12 and >=4, respectively. At those thresholds, eCART caught 22 more adverse events per 10,000 patients than BTF using the moderate risk criteria and 13 more using high risk criteria, while MEWS and NEWS identified the same or fewer. An electronically generated eCART score was more accurate than commonly used paper based observation tools for predicting the composite outcome of in-hospital cardiac arrest, ICU transfer and death within 24h of observation. The outcomes of this analysis lend weight for a move towards an algorithm based electronic risk identification tool for deteriorating patients to ensure earlier detection and prevent adverse events in the hospital. Copyright © 2017 Elsevier B.V. All rights reserved.

Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards.

PubMed

Churpek, Matthew M; Yuen, Trevor C; Winslow, Christopher; Meltzer, David O; Kattan, Michael W; Edelson, Dana P

2016-02-01

Machine learning methods are flexible prediction algorithms that may be more accurate than conventional regression. We compared the accuracy of different techniques for detecting clinical deterioration on the wards in a large, multicenter database. Observational cohort study. Five hospitals, from November 2008 until January 2013. Hospitalized ward patients None Demographic variables, laboratory values, and vital signs were utilized in a discrete-time survival analysis framework to predict the combined outcome of cardiac arrest, intensive care unit transfer, or death. Two logistic regression models (one using linear predictor terms and a second utilizing restricted cubic splines) were compared to several different machine learning methods. The models were derived in the first 60% of the data by date and then validated in the next 40%. For model derivation, each event time window was matched to a non-event window. All models were compared to each other and to the Modified Early Warning score, a commonly cited early warning score, using the area under the receiver operating characteristic curve (AUC). A total of 269,999 patients were admitted, and 424 cardiac arrests, 13,188 intensive care unit transfers, and 2,840 deaths occurred in the study. In the validation dataset, the random forest model was the most accurate model (AUC, 0.80 [95% CI, 0.80-0.80]). The logistic regression model with spline predictors was more accurate than the model utilizing linear predictors (AUC, 0.77 vs 0.74; p < 0.01), and all models were more accurate than the MEWS (AUC, 0.70 [95% CI, 0.70-0.70]). In this multicenter study, we found that several machine learning methods more accurately predicted clinical deterioration than logistic regression. Use of detection algorithms derived from these techniques may result in improved identification of critically ill patients on the wards.

The TRIDEC Project: Future-Saving FOSS GIS Applications for Tsunami Early Warning

NASA Astrophysics Data System (ADS)

Loewe, P.; Wächter, J.; Hammitzsch, M.

2011-12-01

The Boxing Day Tsunami of 2004 killed over 240,000 people in 14 countries and inundated the affected shorelines with waves reaching heights up to 30m. This natural disaster coincided with an information catastrophy, as potentially life-saving early warning information existed, yet no means were available to deliver it to the communities under imminent threat. Tsunami Early Warning Capabilities have improved in the meantime by continuing development of modular Tsunami Early Warning Systems (TEWS). However, recent tsunami events, like the Chile 2010 and the Tohoku 2011 tsunami demonstrate that the key challenge for ongoing TEWS research on the supranational scale still lies in the timely issuing of reliable early warning messages. Since 2004, the GFZ German Research Centre for Geosciences has built up expertise in the field of TEWS. Within GFZ, the Centre for GeoInformation Technology (CEGIT) has focused its work on the geoinformatics aspects of TEWS in two projects already: The German Indonesian Tsunami Early Warning System (GITEWS) funded by the German Federal Ministry of Education and Research (BMBF) and the Distant Early Warning System (DEWS), a European project funded under the sixth Framework Programme (FP6). These developments are continued in the TRIDEC project (Collaborative, Complex, and Critical Decision Processes in Evolving Crises) funded under the European Union's seventh Framework Programme (FP7). This ongoing project focuses on real-time intelligent information management in Earth management and its long-term application. All TRIDEC developments are based on Free and Open Source Software (FOSS) components and industry standards where-ever possible. Tsunami Early Warning in TRIDEC is also based on mature system architecture models to ensure long-term usability and the flexibility to adapt to future generations of Tsunami sensors. All open source software produced by the project consortium are foreseen to be published on FOSSLAB, a publicly available software repository provided by CEGIT. FOSSLAB serves as a platform for the development of FOSS projects in geospatial context, allowing to save, advance and reuse results achieved in previous and on-going project activities and enabling further development and collaboration with a wide community including scientists, developers, users and stakeholders. FOSSLABs potential to preserve and advance existing best practices for reuse in new scenarios is documented by a first case study: For TEWS education and public outreach a comprehensive approach to generate high resolution globe maps was compiled using GRASS GIS and the POV-Ray rendering software. The task resulted in the merging of isolated technical know-how into publicly available best practices, which had been previously maintained in disparate GIS- and rendering communities. Beyond the scope of TRIDEC, FOSSLAB constitutes an umbrella encompassing several geoinformatics-related activities, such as the documentation of Best Practices for experiences and results while working with Spatial Data Infrastructures (SDI), Geographic Information Systems (GIS), Geomatics, and future spatial processing on Computation Clusters and in Cloud Computing.

Developing effective warning systems: Ongoing research at Ruapehu volcano, New Zealand

NASA Astrophysics Data System (ADS)

Leonard, Graham S.; Johnston, David M.; Paton, Douglas; Christianson, Amy; Becker, Julia; Keys, Harry

2008-05-01

PurposeThis paper examines the unique challenges to volcanic risk management associated with having a ski area on an active volcano. Using a series of simulated eruption/lahar events at Ruapehu volcano, New Zealand, as a context, a model of risk management that integrates warning system design and technology, risk perceptions and the human response is explored. Principal resultsDespite increases in the observed audibility and comprehension of the warning message, recall of public education content, and people's awareness of volcanic risk, a persistent minority of the public continued to demonstrate only moderate awareness of the correct actions to take during a warning and failed to respond effectively. A relationship between level of staff competence and correct public response allowed the level of public response to be used to identify residual risk and additional staff training needs. The quality of staff awareness, action and decision-making has emerged as a critical factor, from detailed staff and public interviews and from exercise observations. Staff actions are especially important for mobilising correct public response at Ruapehu ski areas due to the transient nature of the visitor population. Introduction of education material and staff training strategies that included the development of emergency decision-making competencies improved knowledge of correct actions, and increased the proportion of people moving out of harm's way during blind tests. Major conclusionsWarning effectiveness is a function of more than good hazard knowledge and the generation and notification of an early warning message. For warning systems to be effective, these factors must be complemented by accurate knowledge of risk and risk management actions. By combining the Ruapehu findings with those of other warning system studies in New Zealand, and internationally, a practical five-step model for effective early warning systems is discussed. These steps must be based upon sound and regularly updated underpinning science and be tied to formal effectiveness evaluation, which is fed back into system improvements. The model presented emphasises human considerations, the development of which arguably require even more effort than the hardware components of early warning systems.

Red warning for air pollution in China: Exploring residents' perceptions of the first two red warnings in Beijing.

PubMed

Zhao, Hanping; Wang, Fangping; Niu, Chence; Wang, Han; Zhang, Xiaoxue

2018-02-01

Air pollution early warnings have been issued in China to mitigate the effects of high pollution days. Public perceptions and views about early warning signals can affect individual behaviors and play a major role in the public's response to air pollution risks. This study examined public attitudes and responses to the first two red warnings for air pollution in Beijing in 2015. An online survey was sent out, and 664 respondents (response rate = 90%) provided their perspectives on the red warnings. Descriptive statistics, sign tests and binary logit models were used to analyze the data. More than half of the respondents reported that their life and work were affected by the red warning in December 2015. In contrast to their perceptions about the second red warning period, the public thought that the first red warning should have been issued earlier and that the number of consecutive days of warnings should have been reduced. The respondents also recommended that instead of reducing the number of red warnings, the red warning emergency measures should be adjusted. Specifically, the public preferred the installation of air purifiers in schools rather than closing schools and strengthening road flushing and dust pollution controls over restrictions on driving. Data analyses were conducted to examine the affected groups and different groups' perceptions of the necessity of implementing emergency measures. The results indicated that men and more educated respondents were more likely to be affected by driving limitations, and men were less supportive of these limitations. The age and education of respondents were significantly negatively associated with the opinion that schools should be closed, whereas wealthier respondents were more supportive of school closings. The finding of a negative attitude among the public toward the first two red warnings may be used to help local governments modify protective measures and pollution mitigation initiatives to increase acceptance. Copyright © 2017 Elsevier Inc. All rights reserved.

Development of Smart Grid for Community and Cyber based Landslide Hazard Monitoring and Early Warning System

NASA Astrophysics Data System (ADS)

Karnawati, D.; Wilopo, W.; Fathani, T. F.; Fukuoka, H.; Andayani, B.

2012-12-01

A Smart Grid is a cyber-based tool to facilitate a network of sensors for monitoring and communicating the landslide hazard and providing the early warning. The sensor is designed as an electronic sensor installed in the existing monitoring and early warning instruments, and also as the human sensors which comprise selected committed-people at the local community, such as the local surveyor, local observer, member of the local task force for disaster risk reduction, and any person at the local community who has been registered to dedicate their commitments for sending reports related to the landslide symptoms observed at their living environment. This tool is designed to be capable to receive up to thousands of reports/information at the same time through the electronic sensors, text message (mobile phone), the on-line participatory web as well as various social media such as Twitter and Face book. The information that should be recorded/ reported by the sensors is related to the parameters of landslide symptoms, for example the progress of cracks occurrence, ground subsidence or ground deformation. Within 10 minutes, this tool will be able to automatically elaborate and analyse the reported symptoms to predict the landslide hazard and risk levels. The predicted level of hazard/ risk can be sent back to the network of electronic and human sensors as the early warning information. The key parameters indicating the symptoms of landslide hazard were recorded/ monitored by the electrical and the human sensors. Those parameters were identified based on the investigation on geological and geotechnical conditions, supported with the laboratory analysis. The cause and triggering mechanism of landslide in the study area was also analysed in order to define the critical condition to launch the early warning. However, not only the technical but also social system were developed to raise community awareness and commitments to serve the mission as the human sensors, which will be responsible for reporting and informing the early warning. Therefore, a community empowerment and encouragement program through public education was conducted. Strategy and approach for this program was formulated based on the socio-engineering investigation. Finally, the results of technical and social engineering investigations, have been elaborated to further enhance the performance of expert system of the Smart Grid, in order to completely establish this system as an innovative and effective tool for the landslide monitoring and early warning in tropical-developing country.

Evaluation of Intersection Collision Warning Systems in Minnesota

DOT National Transportation Integrated Search

2017-10-01

The Minnesota Department of Transportation (MnDOT) is investing significant resources in intersection collision warning systems (ICWS) based on early indications of effectiveness. However, the effectiveness is not well documented, and negative change...

Early Warning System for reducing disaster risk: the technological platform DEWETRA for the Republic of Serbia

NASA Astrophysics Data System (ADS)

Massabo, Marco; Molini, Luca; Kostic, Bojan; Campanella, Paolo; Stevanovic, Slavimir

2015-04-01

Disaster risk reduction has long been recognized for its role in mitigating the negative environmental, social and economic impacts of natural hazards. Flood Early Warning System is a disaster risk reduction measure based on the capacities of institutions to observe and predict extreme hydro-meteorological events and to disseminate timely and meaningful warning information; it is furthermore based on the capacities of individuals, communities and organizations to prepare and to act appropriately and in sufficient time to reduce the possibility of harm or loss. An operational definition of an Early Warning System has been suggested by ISDR - UN Office for DRR [15 January 2009]: "EWS is the set of capacities needed to generate and disseminate timely and meaningful warning information to enable individuals, communities and organizations threatened by a hazard to prepare and to act appropriately and in sufficient time to reduce the possibility of harm or loss.". ISDR continues by commenting that a people-centered early warning system necessarily comprises four key elements: 1-knowledge of the risks; 2-monitoring, analysis and forecasting of the hazards; 3-communication or dissemination of alerts and warnings; and 4- local capabilities to respond to the warnings received." The technological platform DEWETRA supports the strengthening of the first three key elements of EWS suggested by ISDR definition, hence to improve the capacities to build real-time risk scenarios and to inform and warn the population in advance The technological platform DEWETRA has been implemented for the Republic of Serbia. DEWETRA is a real time-integrate system that supports decision makers for risk forecasting and monitoring and for distributing warnings to end-user and to the general public. The system is based on the rapid availability of different data that helps to establish up-to-date and reliable risk scenarios. The integration of all relevant data for risk management significantly increases the value of available information and the level of knowledge of forecasters and disaster managers. Different data, forecast and monitoring products, which are generated by different national and international institution and organizations, can be visualized and processed in real-time within the platform. DEWETRA is a web application ensuring the capillary distribution of information among institutions. The system is used as an infrastructure for exchanging and sharing data, procedures, models and expertise among the Sector of Emergency Management (SEM), the Republic Hydro-Meteorological Service of Serbia (RHMSS) and the Serbian Public Water Companies (PWCs): Serbia Waters, Vojvodina Waters and Belgrade Waters.

National High School Center Early Warning System Tool v2.0: Technical Manual

ERIC Educational Resources Information Center

National High School Center, 2011

2011-01-01

The Early Warning System (EWS) Tool v2.0 is a Microsoft Excel-based tool developed by the National High School Center at the American Institutes for Research in collaboration with Matrix Knowledge Group. The tool enables schools, districts, and states to identify students who may be at risk of dropping out of high school and to monitor these…

Bistatic Space Borne Radar for Early Warning

DTIC Science & Technology

2006-08-01

bandwidth of about 1.2 MHz. hr ht RX TX z x α α α α αr αt y R30 R10 R31 R11 vRx vTx P Bistatic Space Borne Radar for Early Warning...B V R == (12) where VRX is the receiver velocity and BA is the Doppler chirp bandwidth defined by equation (5). The time necessary to obtain

Does the Computer-Assisted Remedial Mathematics Program at Kearny High School Lead to Improved Scores on the N.J. Early Warning Test?

ERIC Educational Resources Information Center

Schalago-Schirm, Cynthia

Eighth-grade students in New Jersey take the Early Warning Test (EWT), which involves reading, writing, and mathematics. Students with EWT scores below the state level of competency take a remedial mathematics course that provides students with computer-assisted instruction (2 days per week) as well as regular classroom instruction (3 days per…

Learning by Teaching: Undergraduate Engineering Students Improving a Community's Response Capability to an Early Warning System

ERIC Educational Resources Information Center

Suvannatsiri, Ratchasak; Santichaianant, Kitidech; Murphy, Elizabeth

2015-01-01

This paper reports on a project in which students designed, constructed and tested a model of an existing early warning system with simulation of debris flow in a context of a landslide. Students also assessed rural community members' knowledge of this system and subsequently taught them to estimate the time needed for evacuation of the community…

Response Characteristics of an Aquatic Biomonitor Used for Rapid Toxicity Detection

DTIC Science & Technology

2004-05-15

for drinking water protection. 14. SUBJECT TERMS 15. NUMBER OF PAGES biological early warning system; Lepomis macrochirus; bluegill; aquatic toxicity...Fort Detrick, MD 21702-5010, USA Key words: biomonitor; biological early warning system; Lepomis macrochirus; bluegill; aquatic toxicity; water ...narcosis are most likely to cause rapid aquatic biomonitor depth related to variations in water quality (primarily responses. Other modes of action may

Understanding the High School Proficiency Test and the Early Warning Test in Relation to HCCC Enrollment Trends.

ERIC Educational Resources Information Center

Taffy, Fred

The Grade 11 High School Proficiency Test (HSPT) and the New Jersey Early Warning Test (EWT) are two key standardized tests that indicate academic ability of county high school graduates which colleges will need to address. While HSPT scores for county high school districts reflect a range of competency in reading, math, and writing, the majority…

Delivery of State-Provided Predictive Analytics to Schools: Wisconsin's DEWS and the Proposed EWIMS Dashboard. WCER Working Paper No. 2016-3

ERIC Educational Resources Information Center

Clune, Bill; Knowles, Jared

2016-01-01

Since 2012, the Wisconsin Department of Public Instruction (DPI) has maintained a statewide predictive analytics system providing schools with an early warning in middle grades of students at risk for not completing high school. DPI is considering extending and enhancing this system, known as the Dropout Early Warning System (DEWS). The proposed…

Development of Laboratory Model Ecosystems as Early Warning Elements of Environmental Pollution

DTIC Science & Technology

1974-12-01

AD-AOll 851 DEVELOPMENT OF LABORATORY MODEL ECOSYSTEMS AS EARLY WARNING ELEMENTS OF ENVIRONMENTAL POLLUTION Robert L. Metcalf... ENVIRONMENTAL POLLUTION Robert L. Metcalf, Ph. D. University of Illinois Urbana-Champaign, Illinois INTRODUCTION Problems of environmental pollution with...house dust is unsafe to breathe (Ewing and Pearson, 1974). Most of the source of our concern about environmental pollution by trace substances relates

Looking beyond the destruction of the GLOF-Early Warning System of the Lake 513 in the Peruvian Andes by the local rural population

NASA Astrophysics Data System (ADS)

Christine, Jurt; Vicuña, Luis; Dulce Burga, María; Huggel, Christian; Frey, Holger

2017-04-01

The news about the destruction of the early warning system of the glacial Lake 513 at the headwaters of the Chucchún catchment in Peru's Cordillera Blanca left many people perplexed. The early warning system was installed after around 40 years of glacier hazard management in the region. It was developed within a project that is widely considered as particularly successful with a close cooperation of several Peruvian institutions, the local municipality, the community and Swiss scientists. From a risk reduction point of view, the early warning system is a critical factor and its destruction by local people themselves is hardly comprehensible. Three month of fieldwork on site in the local communities of the Chucchún catchment during and after the installation of the system, including semi-structured interviews, group discussions and participatory observations as well the participation in the project allowed us to get deeper insights into the context and background of what has occurred. Here, we approach the destruction of the early warning system by analyzing different perspectives on encounters between different actors involved - local groups, scientists from Peru and Switzerland, technical staff, NGO in the field of development, representatives of governmental institutions. Such encounters between the different actors during the practice of science (e.g. doing fieldwork) or during the installation of the early warning system (as for instance in meetings on site) are crucial for overcoming gaps between scientific and local knowledge as well as between knowledge and practice. This led to new insights into the discussion of the case of destruction in Chucchún. Mutual perceptions among the groups, self-perceptions and perceptions of both visible and invisible risks shape the discourses about risks and measures in specific situations of encounters during the project. Particularly striking, however, are different perspectives on encounters in the past between representatives of groups which are now involved in the project, and how these encounters are analyzed in the actual in terms of the present and future.

Establishing the fundamentals for an elephant early warning and monitoring system.

PubMed

Zeppelzauer, Matthias; Stoeger, Angela S

2015-09-04

The decline of habitat for elephants due to expanding human activity is a serious conservation problem. This has continuously escalated the human-elephant conflict in Africa and Asia. Elephants make extensive use of powerful infrasonic calls (rumbles) that travel distances of up to several kilometers. This makes elephants well-suited for acoustic monitoring because it enables detecting elephants even if they are out of sight. In sight, their distinct visual appearance makes them a good candidate for visual monitoring. We provide an integrated overview of our interdisciplinary project that established the scientific fundamentals for a future early warning and monitoring system for humans who regularly experience serious conflict with elephants. We first draw the big picture of an early warning and monitoring system, then review the developed solutions for automatic acoustic and visual detection, discuss specific challenges and present open future work necessary to build a robust and reliable early warning and monitoring system that is able to operate in situ. We present a method for the automated detection of elephant rumbles that is robust to the diverse noise sources present in situ. We evaluated the method on an extensive set of audio data recorded under natural field conditions. Results show that the proposed method outperforms existing approaches and accurately detects elephant rumbles. Our visual detection method shows that tracking elephants in wildlife videos (of different sizes and postures) is feasible and particularly robust at near distances. From our project results we draw a number of conclusions that are discussed and summarized. We clearly identified the most critical challenges and necessary improvements of the proposed detection methods and conclude that our findings have the potential to form the basis for a future automated early warning system for elephants. We discuss challenges that need to be solved and summarize open topics in the context of a future early warning and monitoring system. We conclude that a long-term evaluation of the presented methods in situ using real-time prototypes is the most important next step to transfer the developed methods into practical implementation.

Enhancing Famine Early Warning Systems with Improved Forecasts, Satellite Observations and Hydrologic Simulations

NASA Astrophysics Data System (ADS)

Funk, C. C.; Verdin, J.; Thiaw, W. M.; Hoell, A.; Korecha, D.; McNally, A.; Shukla, S.; Arsenault, K. R.; Magadzire, T.; Novella, N.; Peters-Lidard, C. D.; Robjohn, M.; Pomposi, C.; Galu, G.; Rowland, J.; Budde, M. E.; Landsfeld, M. F.; Harrison, L.; Davenport, F.; Husak, G. J.; Endalkachew, E.

2017-12-01

Drought early warning science, in support of famine prevention, is a rapidly advancing field that is helping to save lives and livelihoods. In 2015-2017, a series of extreme droughts afflicted Ethiopia, Southern Africa, Eastern Africa in OND and Eastern Africa in MAM, pushing more than 50 million people into severe food insecurity. Improved drought forecasts and monitoring tools, however, helped motivate and target large and effective humanitarian responses. Here we describe new science being developed by a long-established early warning system - the USAID Famine Early Warning Systems Network (FEWS NET). FEWS NET is a leading provider of early warning and analysis on food insecurity. FEWS NET research is advancing rapidly on several fronts, providing better climate forecasts and more effective drought monitoring tools that are being used to support enhanced famine early warning. We explore the philosophy and science underlying these successes, suggesting that a modal view of climate change can support enhanced seasonal prediction. Under this modal perspective, warming of the tropical oceans may interact with natural modes of variability, like the El Niño-Southern Oscillation, to enhance Indo-Pacific sea surface temperature gradients during both El Niño and La Niña-like climate states. Using empirical data and climate change simulations, we suggest that a sequence of droughts may commence in northern Ethiopia and Southern Africa with the advent of a moderate-to-strong El Niño, and then continue with La Niña/West Pacific related droughts in equatorial eastern East Africa. Scientifically, we show that a new hybrid statistical-dynamic precipitation forecast system, the FEWS NET Integrated Forecast System (FIFS), based on reformulations of the Global Ensemble Forecast System weather forecasts and National Multi-Model Ensemble (NMME) seasonal climate predictions, can effectively anticipate recent East and Southern African drought events. Using cross-validation, we evaluate FIFS' skill and compare it to the NMME and the International Research Institute forecasts. Our study concludes with an overview of the satellite observations provided by FEWS NET partners at NOAA, NASA, USGS, and UC Santa Barbara, and the assimilation of these products within the FEWS NET Land Data Assimilation System (FLDAS).

Drought early warning and risk management in a changing environment

NASA Astrophysics Data System (ADS)

Pulwarty, R. S.

2011-12-01

Drought has long been recognized as falling into the category of incremental but long-term and cumulative environmental changes, also termed slow-onset or creeping events. These event types would include: air and water quality decline, desertification processes, deforestation and forest fragmentation, loss of biodiversity and habitats, and nitrogen overloading, among others. Climate scientists continue to struggle with recognizing the onset of drought and scientists and policy makers continue to debate the basis (i.e., criteria) for declaring an end to a drought. Risk-based management approaches to drought planning at the national and regional levels have been recommended repeatedly over the years but their prototyping, testing and operational implementation have been limited. This presentation will outline two avenues for disaster risk reduction in the context of drought (1) integrated early warning information systems, and (2) linking disaster risk reduction to climate change adaptation strategies. Adaptation involves not only using operational facilities and infrastructure to cope with the immediate problems but also leaving slack or reserve for coping with multiple stress problems that produce extreme impacts and surprise. Increasing the 'anticipatability' of an event, involves both monitoring of key indicators from appropriate baseline data, and observing early warning signs that assumptions in risk management plans are failing and critical transitions are occurring. Illustrative cases will be drawn from the IPCC Special Report on Managing the Risks of Extreme Events and Disasters (2011), the UN Global Assessment of Disaster Risk Reduction (2011) and implementation activities in which the author has been engaged. Most drought early warning systems have tended to focus on the development and use of physical system indicators and forecasts of trends and thresholds. We show that successful early warning systems that meet expectations of risk management also have explicit foci on (1) integrating physical and social vulnerability indicators across timescales, (2) analytical capacity to generate local scenarios of risk using both analogs and projections, (3) the communication of risk-based information, and (4) the support and governance of a collaborative framework for early warning structures across spatial scales.

Coronal Mass Ejection early-warning mission by solar-photon sailcraft

NASA Astrophysics Data System (ADS)

Vulpetti, Giovanni; Circi, Christian; Pino, Tommaso

2017-11-01

A preliminary investigation of the early warning of solar storms caused by Coronal Mass Ejection has been carried out. A long warning time could be obtained with a sailcraft synchronous with the Earth-Moon barycenter, and stationed well below the L1 point. In this paper, the theory of heliocentric synchronous sailcraft is set up, its perturbed orbit is analyzed, and a potential solution capable of providing an annual synchrony is carried out. A simple analysis of the response from a low-mass electrochromic actuator for the realization of station-keeping attitude maneuvers is put forwards, and an example of propellantless re-orientation maneuver is studied.

Robust automated classification of first-motion polarities for focal mechanism determination with machine learning

NASA Astrophysics Data System (ADS)

Ross, Z. E.; Meier, M. A.; Hauksson, E.

2017-12-01

Accurate first-motion polarities are essential for determining earthquake focal mechanisms, but are difficult to measure automatically because of picking errors and signal to noise issues. Here we develop an algorithm for reliable automated classification of first-motion polarities using machine learning algorithms. A classifier is designed to identify whether the first-motion polarity is up, down, or undefined by examining the waveform data directly. We first improve the accuracy of automatic P-wave onset picks by maximizing a weighted signal/noise ratio for a suite of candidate picks around the automatic pick. We then use the waveform amplitudes before and after the optimized pick as features for the classification. We demonstrate the method's potential by training and testing the classifier on tens of thousands of hand-made first-motion picks by the Southern California Seismic Network. The classifier assigned the same polarity as chosen by an analyst in more than 94% of the records. We show that the method is generalizable to a variety of learning algorithms, including neural networks and random forest classifiers. The method is suitable for automated processing of large seismic waveform datasets, and can potentially be used in real-time applications, e.g. for improving the source characterizations of earthquake early warning algorithms.

Wireless Sensor Networks - Node Localization for Various Industry Problems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Derr, Kurt; Manic, Milos

Fast, effective monitoring following airborne releases of toxic substances is critical to mitigate risks to threatened population areas. Wireless sensor nodes at fixed predetermined locations may monitor such airborne releases and provide early warnings to the public. A challenging algorithmic problem is determining the locations to place these sensor nodes while meeting several criteria: 1) provide complete coverage of the domain, and 2) create a topology with problem dependent node densities, while 3) minimizing the number of sensor nodes. This manuscript presents a novel approach to determining optimal sensor placement, Advancing Front mEsh generation with Constrained dElaunay Triangulation and Smoothingmore » (AFECETS) that addresses these criteria. A unique aspect of AFECETS is the ability to determine wireless sensor node locations for areas of high interest (hospitals, schools, high population density areas) that require higher density of nodes for monitoring environmental conditions, a feature that is difficult to find in other research work. The AFECETS algorithm was tested on several arbitrary shaped domains. AFECETS simulation results show that the algorithm 1) provides significant reduction in the number of nodes, in some cases over 40%, compared to an advancing front mesh generation algorithm, 2) maintains and improves optimal spacing between nodes, and 3) produces simulation run times suitable for real-time applications.« less

Wireless Sensor Networks - Node Localization for Various Industry Problems

DOE PAGES

Derr, Kurt; Manic, Milos

2015-06-01

Fast, effective monitoring following airborne releases of toxic substances is critical to mitigate risks to threatened population areas. Wireless sensor nodes at fixed predetermined locations may monitor such airborne releases and provide early warnings to the public. A challenging algorithmic problem is determining the locations to place these sensor nodes while meeting several criteria: 1) provide complete coverage of the domain, and 2) create a topology with problem dependent node densities, while 3) minimizing the number of sensor nodes. This manuscript presents a novel approach to determining optimal sensor placement, Advancing Front mEsh generation with Constrained dElaunay Triangulation and Smoothingmore » (AFECETS) that addresses these criteria. A unique aspect of AFECETS is the ability to determine wireless sensor node locations for areas of high interest (hospitals, schools, high population density areas) that require higher density of nodes for monitoring environmental conditions, a feature that is difficult to find in other research work. The AFECETS algorithm was tested on several arbitrary shaped domains. AFECETS simulation results show that the algorithm 1) provides significant reduction in the number of nodes, in some cases over 40%, compared to an advancing front mesh generation algorithm, 2) maintains and improves optimal spacing between nodes, and 3) produces simulation run times suitable for real-time applications.« less

Longitudinal driver model and collision warning and avoidance algorithms based on human driving databases

NASA Astrophysics Data System (ADS)

Lee, Kangwon

Intelligent vehicle systems, such as Adaptive Cruise Control (ACC) or Collision Warning/Collision Avoidance (CW/CA), are currently under development, and several companies have already offered ACC on selected models. Control or decision-making algorithms of these systems are commonly evaluated under extensive computer simulations and well-defined scenarios on test tracks. However, they have rarely been validated with large quantities of naturalistic human driving data. This dissertation utilized two University of Michigan Transportation Research Institute databases (Intelligent Cruise Control Field Operational Test and System for Assessment of Vehicle Motion Environment) in the development and evaluation of longitudinal driver models and CW/CA algorithms. First, to examine how drivers normally follow other vehicles, the vehicle motion data from the databases were processed using a Kalman smoother. The processed data was then used to fit and evaluate existing longitudinal driver models (e.g., the linear follow-the-leader model, the Newell's special model, the nonlinear follow-the-leader model, the linear optimal control model, the Gipps model and the optimal velocity model). A modified version of the Gipps model was proposed and found to be accurate in both microscopic (vehicle) and macroscopic (traffic) senses. Second, to examine emergency braking behavior and to evaluate CW/CA algorithms, the concepts of signal detection theory and a performance index suitable for unbalanced situations (few threatening data points vs. many safe data points) are introduced. Selected existing CW/CA algorithms were found to have a performance index (geometric mean of true-positive rate and precision) not exceeding 20%. To optimize the parameters of the CW/CA algorithms, a new numerical optimization scheme was developed to replace the original data points with their representative statistics. A new CW/CA algorithm was proposed, which was found to score higher than 55% in the performance index. This dissertation provides a model of how drivers follow lead-vehicles that is much more accurate than other models in the literature. Furthermore, the data-based approach was used to confirm that a CW/CA algorithm utilizing lead-vehicle braking was substantially more effective than existing algorithms, leading to collision warning systems that are much more likely to contribute to driver safety.

Earthquake magnitude estimation using the τ c and P d method for earthquake early warning systems

NASA Astrophysics Data System (ADS)

Jin, Xing; Zhang, Hongcai; Li, Jun; Wei, Yongxiang; Ma, Qiang

2013-10-01

Earthquake early warning (EEW) systems are one of the most effective ways to reduce earthquake disaster. Earthquake magnitude estimation is one of the most important and also the most difficult parts of the entire EEW system. In this paper, based on 142 earthquake events and 253 seismic records that were recorded by the KiK-net in Japan, and aftershocks of the large Wenchuan earthquake in Sichuan, we obtained earthquake magnitude estimation relationships using the τ c and P d methods. The standard variances of magnitude calculation of these two formulas are ±0.65 and ±0.56, respectively. The P d value can also be used to estimate the peak ground motion of velocity, then warning information can be released to the public rapidly, according to the estimation results. In order to insure the stability and reliability of magnitude estimation results, we propose a compatibility test according to the natures of these two parameters. The reliability of the early warning information is significantly improved though this test.

The Lake Victoria Intense Storm Early Warning System (VIEWS)

NASA Astrophysics Data System (ADS)

Thiery, Wim; Gudmundsson, Lukas; Bedka, Kristopher; Semazzi, Fredrick; Lhermitte, Stef; Willems, Patrick; van Lipzig, Nicole; Seneviratne, Sonia I.

2017-04-01

Weather extremes have harmful impacts on communities around Lake Victoria in East Africa. Every year, intense nighttime thunderstorms cause numerous boating accidents on the lake, resulting in thousands of deaths among fishermen. Operational storm warning systems are therefore crucial. Here we complement ongoing early warning efforts based on NWP, by presenting a new satellite data-driven storm prediction system, the prototype Lake Victoria Intense storm Early Warning System (VIEWS). VIEWS derives predictability from the correlation between afternoon land storm activity and nighttime storm intensity on Lake Victoria, and relies on logistic regression techniques to forecast extreme thunderstorms from satellite observations. Evaluation of the statistical model reveals that predictive power is high and independent of the input dataset. We then optimise the configuration and show that also false alarms contain valuable information. Our results suggest that regression-based models that are motivated through process understanding have the potential to reduce the vulnerability of local fishing communities around Lake Victoria. The experimental prediction system is publicly available under the MIT licence at http://github.com/wthiery/VIEWS.

Early warnings of hazardous thunderstorms over Lake Victoria

NASA Astrophysics Data System (ADS)

Thiery, Wim; Gudmundsson, Lukas; Bedka, Kristopher; Semazzi, Fredrick H. M.; Lhermitte, Stef; Willems, Patrick; van Lipzig, Nicole P. M.; Seneviratne, Sonia I.

2017-07-01

Weather extremes have harmful impacts on communities around Lake Victoria in East Africa. Every year, intense nighttime thunderstorms cause numerous boating accidents on the lake, resulting in thousands of deaths among fishermen. Operational storm warning systems are therefore crucial. Here we complement ongoing early warning efforts based on numerical weather prediction, by presenting a new satellite data-driven storm prediction system, the prototype Lake Victoria Intense storm Early Warning System (VIEWS). VIEWS derives predictability from the correlation between afternoon land storm activity and nighttime storm intensity on Lake Victoria, and relies on logistic regression techniques to forecast extreme thunderstorms from satellite observations. Evaluation of the statistical model reveals that predictive power is high and independent of the type of input dataset. We then optimise the configuration and show that false alarms also contain valuable information. Our results suggest that regression-based models that are motivated through process understanding have the potential to reduce the vulnerability of local fishing communities around Lake Victoria. The experimental prediction system is publicly available under the MIT licence at http://github.com/wthiery/VIEWS.

Integrating real-time subsurface hydrologic monitoring with empirical rainfall thresholds to improve landslide early warning

USGS Publications Warehouse

Mirus, Benjamin B.; Becker, Rachel E.; Baum, Rex L.; Smith, Joel B.

2018-01-01

Early warning for rainfall-induced shallow landsliding can help reduce fatalities and economic losses. Although these commonly occurring landslides are typically triggered by subsurface hydrological processes, most early warning criteria rely exclusively on empirical rainfall thresholds and other indirect proxies for subsurface wetness. We explore the utility of explicitly accounting for antecedent wetness by integrating real-time subsurface hydrologic measurements into landslide early warning criteria. Our efforts build on previous progress with rainfall thresholds, monitoring, and numerical modeling along the landslide-prone railway corridor between Everett and Seattle, Washington, USA. We propose a modification to a previously established recent versus antecedent (RA) cumulative rainfall thresholds by replacing the antecedent 15-day rainfall component with an average saturation observed over the same timeframe. We calculate this antecedent saturation with real-time telemetered measurements from five volumetric water content probes installed in the shallow subsurface within a steep vegetated hillslope. Our hybrid rainfall versus saturation (RS) threshold still relies on the same recent 3-day rainfall component as the existing RA thresholds, to facilitate ready integration with quantitative precipitation forecasts. During the 2015–2017 monitoring period, this RS hybrid approach has an increase of true positives and a decrease of false positives and false negatives relative to the previous RA rainfall-only thresholds. We also demonstrate that alternative hybrid threshold formats could be even more accurate, which suggests that further development and testing during future landslide seasons is needed. The positive results confirm that accounting for antecedent wetness conditions with direct subsurface hydrologic measurements can improve thresholds for alert systems and early warning of rainfall-induced shallow landsliding.

Implementation of a landslide early warning system based on near-real-time monitoring, multisensor mapping and geophysical measurements

NASA Astrophysics Data System (ADS)

Teza, Giordano; Galgaro, Antonio; Francese, Roberto; Ninfo, Andrea; Mariani, Rocco

2017-04-01

An early warning system has been implemented to monitor the Perarolo di Cadore landslide (North-Eastern Italian Alps), which is a slump whose induced risk is fairly high because a slope collapse could form a temporary dam on the underlying torrent and, therefore, could directly threaten the close village. A robotic total station (RTS) measures, with 6h returning time, the positions of 23 retro-reflectors placed on the landslide upper and middle sectors. The landslide's kinematical behavior derived from these near-real-time (NRT) surface displacements is interpreted on the basis of available geomorphological and geological information, geometrical data provided by some laser scanning and photogrammetric surveys, and a landslide model obtained by means of 3D Electrical Resistivity Tomography (3D ERT) measurements. In this way, an analysis of the time series provided by RTS and a pluviometer, which cover several years, allows the definition of some pre-alert and alert kinematical and rainfall thresholds. These thresholds, as well as the corresponding operational recommendations, are currently used for early warning purposes by Authorities involved in risk management for the Perarolo landslide. It should be noted the fact that, as new RTS and pluviometric data are available, the thresholds can be updated and, therefore, a fine tuning of the early warning system can be carried out in order to improve its performance. Although the proposed approach has been implemented in a particular case, it can be used to develop an early warning system based on NRT data in each site where a landslide threatens infrastructures and/or villages that cannot be relocated.

[Assessment and early warning of land ecological security in rapidly urbanizing coastal area: A case study of Caofeidian new district, Hebei, China].

PubMed

Zhang, Li; Chen, Ying; Wang, Shu-tao; Men, Ming-xin; Xu, Hao

2015-08-01

Assessment and early warning of land ecological security (LES) in rapidly urbanizing coastal area is an important issue to ensure sustainable land use and effective maintenance of land ecological security. In this study, an index system for the land ecological security of Caofeidian new district was established based on the Pressure-State-Response (P-S-R) model. Initial assessment units of 1 km x 1 km created with the remote sensing data and GIS methods were spatially interpolated to a fine pixel size of 30 m x 30 m, which were combined with the early warning method (using classification tree method) to evaluate the land ecological security of Caofeidian in 2005 and 2013. The early warning level was classed into four categories: security with degradation potential, sub-security with slow degradation, sub-security with rapid degradation, and insecurity. Result indicated that, from 2005 to 2013, the average LES of Caofeidian dropped from 0.55 to 0.52, indicating a degradation of land ecological security from medium security level to medium-low security level. The areas at the levels of insecurity with rapid degradation were mainly located in the rapid urbanization areas, illustrating that rapid expansion of urban construction land was the key factor to the deterioration of the regional land ecological security. Industrial District, Shilihai town and Nanpu saltern, in which the lands at the levels of insecurity and sub-security with rapid degradation or slow degradation accounted for 58.3%, 98.9% and 81.2% of their respective districts, were at the stage of high early warning. Thus, land ecological security regulation for these districts should be strengthened in near future. The study could provide a reference for land use planning and ecological protection of Caofeidian new district.

Solar Energetic Particle Warnings from a Coronagraph

NASA Technical Reports Server (NTRS)

St Cyr, O. C.; Posner, A.; Burkepile, J. T.

2017-01-01

We report here the concept of using near-real time observations from a coronagraph to provide early warning of a fast coronal mass ejection (CME) and the possible onset of a solar energetic particle (SEP) event. The 1 January 2016, fast CME, and its associated SEP event are cited as an example. The CME was detected by the ground-based K-Cor coronagraph at Mauna Loa Solar Observatory and by the SOHO Large Angle and Spectrometric Coronagraph. The near-real-time availability of the high-cadence K-Cor observations in the low corona leads to an obvious question: Why has no one attempted to use a coronagraph as an early warning device for SEP events? The answer is that the low image cadence and the long latency of existing spaceborne coronagraphs make them valid for archival studies but typically unsuitable for near-real-time forecasting. The January 2016 event provided favorable CME viewing geometry and demonstrated that the primary component of a prototype ground-based system for SEP warnings is available several hours on most days. We discuss how a conceptual CME-based warning system relates to other techniques, including an estimate of the relative SEP warning times, and how such a system might be realized.

Influence of warning information changes on emergency response

NASA Astrophysics Data System (ADS)

Heisterkamp, Tobias; Ulbrich, Uwe; Glade, Thomas; Tetzlaff, Gerd

2014-05-01

Mitigation and risk reduction of natural hazards is significantly related to the possibility of predicting the actual event. Some hazards can already be forecasted several days in advance. For these hazards, early warning systems have been developed, installed and improved over the years. The formation of winter storms for example can be recognized up to one week before they pass through Central Europe. This relative long early warning time has the advantage that forecasters can concretise the warnings over time. Therefore, warnings can even be adapted to alternating conditions within the process, the observation or changes in its modelling. Emergency managers are one group of warning recipients in the civil protection sector. They have to prepare or initiate prevention or response measures at a specific point of time, depending on the required lead time of the referring actions. At this point of time already, the forecast and its equivalent warning, has to be assumed as a stage of reality, hence the decision-makers have to come to a conclusion. These decisions are based on spatial and temporal knowledge of the forecasted event and the consequential situation of risk. With incoming warning updates, the detailed status of information is permanently being alternated. Consequently, decisions can be influenced by the development of the warning situation and the inherent tendency before a certain point of time. They can also be adapted to updates later on, according to the changing 'decision reality'. The influence of these dynamic hazard situations on operational planning and response by emergency managers is investigated in case studies on winter storms for Berlin, Germany. Therefore, the issued warnings by the weather service and data of operation of Berlin Fire Brigades are analysed and compared. This presentation shows and discusses first results.

Role of remote sensing in desert locust early warning

NASA Astrophysics Data System (ADS)

Cressman, Keith

2013-01-01

Desert locust (Schistocerca gregaria, Forskål) plagues have historically had devastating consequences on food security in Africa and Asia. The current strategy to reduce the frequency of plagues and manage desert locust infestations is early warning and preventive control. To achieve this, the Food and Agriculture Organization of the United Nations operates one of the oldest, largest, and best-known migratory pest monitoring systems in the world. Within this system, remote sensing plays an important role in detecting rainfall and green vegetation. Despite recent technological advances in data management and analysis, communications, and remote sensing, monitoring desert locusts and preventing plagues in the years ahead will continue to be a challenge from a geopolitical and financial standpoint for affected countries and the international donor community. We present an overview of the use of remote sensing in desert locust early warning.

Tsunami Early Warning via a Physics-Based Simulation Pipeline

NASA Astrophysics Data System (ADS)

Wilson, J. M.; Rundle, J. B.; Donnellan, A.; Ward, S. N.; Komjathy, A.

2017-12-01

Through independent efforts, physics-based simulations of earthquakes, tsunamis, and atmospheric signatures of these phenomenon have been developed. With the goal of producing tsunami forecasts and early warning tools for at-risk regions, we join these three spheres to create a simulation pipeline. The Virtual Quake simulator can produce thousands of years of synthetic seismicity on large, complex fault geometries, as well as the expected surface displacement in tsunamigenic regions. These displacements are used as initial conditions for tsunami simulators, such as Tsunami Squares, to produce catalogs of potential tsunami scenarios with probabilities. Finally, these tsunami scenarios can act as input for simulations of associated ionospheric total electron content, signals which can be detected by GNSS satellites for purposes of early warning in the event of a real tsunami. We present the most recent developments in this project.

Automatic Traffic Advisory and Resolution Service (ATARS) Multi-Site Algorithms. Revision 1,

DTIC Science & Technology

1980-10-01

Summary Concept Description The Automatic Traffic Advisory and Resolution Service is a ground based collision avoidance system to be implemented in the...capability. A ground based computer processes the data and continuously provides proximity warning information and, when necessary, resolution advisories to...of ground- based air traffic control which provides proximity warning and separation services to uncontrolled aircraft in a given region of airspace. it

Seismic instrumentation plan for the Hawaiian Volcano Observatory

USGS Publications Warehouse

Thelen, Weston A.

2014-01-01

The installation of new seismic stations is only the first part of building a volcanic early warning capability for seismicity in the State of Hawaii. Additional personnel will likely be required to study the volcanic processes at work under each volcano, analyze the current seismic activity at a level sufficient for early warning, build new tools for monitoring, maintain seismic computing resources, and maintain the new seismic stations.

Simulated NASA Satellite Data Products for the NOAA Integrated Coral Reef Observation Network/Coral Reef Early Warning System

NASA Technical Reports Server (NTRS)

Estep, Leland; Spruce, Joseph P.

2007-01-01

This RPC (Rapid Prototyping Capability) experiment will demonstrate the use of VIIRS (Visible/Infrared Imager/Radiometer Suite) and LDCM (Landsat Data Continuity Mission) sensor data as significant input to the NOAA (National Oceanic and Atmospheric Administration) ICON/ CREWS (Integrated Coral Reef Observation System/Coral Reef Early Warning System). The project affects the Coastal Management Program Element of the Applied Sciences Program.

Are Two Commonly Used Early Warning Indicators Accurate Predictors of Dropout for English Learner Students? Evidence from Six Districts in Washington State. REL 2017-261

ERIC Educational Resources Information Center

Deussen, Theresa; Hanson, Havala; Bisht, Biraj

2017-01-01

Students who drop out of high school are at increased risk of a range of negative social and economic consequences, including lower earnings and poorer health. To reduce dropout rates and lessen these negative consequences, districts around the country are using early warning indicators to identify and provide supports for students at risk of…

Wellness engineering for better quality of life of aging baby boomer

NASA Astrophysics Data System (ADS)

Szu, Harold

2007-04-01

Current health care system serving 78M aging baby-boomers is no longer sustainable, as the cost about 1/5 GDP will reach 1/4 GDT when all is retired in decades, unless the system is changed. We design a high-tech safe net to enhance the timeliness of early correct treatment execution (otherwise, causing 1/4 mortality associated with an escalating legal fee waste). We follow the common sense that "a stitch in time saves nine," and adopt the military surveillance know-how in designing early warning health management system, comprising of smart sensor pairs for point-care surveillance. However, the grand plan of affordable smart sensors hardware for households requires an ODM & OEM teaming to conduct parallel designing and sequential marketing strategy. The military software strategy combating a treacherous adversary enemy match well with point cares surveillance overcoming real world microorganism variability. Moreover, such smart military software provides self-reference change detection, not by traditional cohort ensemble average, but by individual own higher order statistics (HOS) independent component analysis (ICA), which take the advantage of known initial condition for each individual and desirable over-sampling daily dynamics. The triggering of warning follows the military algorithms comprising of Receiver Operation Characteristics (ROC) and Automatic Target Recognition (ATR). To further reduce the unwanted false negative rate, a benchmarked is made against the traditional cohort-ensemble baseline average & the upper & lower bounds of variance as adopted by the gatekeepers - Medical Doctors (MD) and Nurses.

The forecasting research of early warning systems for atmospheric pollutants: A case in Yangtze River Delta region

NASA Astrophysics Data System (ADS)

Song, Yiliao; Qin, Shanshan; Qu, Jiansheng; Liu, Feng

2015-10-01

The issue of air quality regarding PM pollution levels in China is a focus of public attention. To address that issue, to date, a series of studies is in progress, including PM monitoring programs, PM source apportionment, and the enactment of new ambient air quality index standards. However, related research concerning computer modeling for PM future trends estimation is rare, despite its significance to forecasting and early warning systems. Thereby, a study regarding deterministic and interval forecasts of PM is performed. In this study, data on hourly and 12 h-averaged air pollutants are applied to forecast PM concentrations within the Yangtze River Delta (YRD) region of China. The characteristics of PM emissions have been primarily examined and analyzed using different distribution functions. To improve the distribution fitting that is crucial for estimating PM levels, an artificial intelligence algorithm is incorporated to select the optimal parameters. Following that step, an ANF model is used to conduct deterministic forecasts of PM. With the identified distributions and deterministic forecasts, different levels of PM intervals are estimated. The results indicate that the lognormal or gamma distributions are highly representative of the recorded PM data with a goodness-of-fit R2 of approximately 0.998. Furthermore, the results of the evaluation metrics (MSE, MAPE and CP, AW) also show high accuracy within the deterministic and interval forecasts of PM, indicating that this method enables the informative and effective quantification of future PM trends.

Methods for Detecting Early Warnings of Critical Transitions in Time Series Illustrated Using Simulated Ecological Data

PubMed Central

Dakos, Vasilis; Carpenter, Stephen R.; Brock, William A.; Ellison, Aaron M.; Guttal, Vishwesha; Ives, Anthony R.; Kéfi, Sonia; Livina, Valerie; Seekell, David A.; van Nes, Egbert H.; Scheffer, Marten

2012-01-01

Many dynamical systems, including lakes, organisms, ocean circulation patterns, or financial markets, are now thought to have tipping points where critical transitions to a contrasting state can happen. Because critical transitions can occur unexpectedly and are difficult to manage, there is a need for methods that can be used to identify when a critical transition is approaching. Recent theory shows that we can identify the proximity of a system to a critical transition using a variety of so-called ‘early warning signals’, and successful empirical examples suggest a potential for practical applicability. However, while the range of proposed methods for predicting critical transitions is rapidly expanding, opinions on their practical use differ widely, and there is no comparative study that tests the limitations of the different methods to identify approaching critical transitions using time-series data. Here, we summarize a range of currently available early warning methods and apply them to two simulated time series that are typical of systems undergoing a critical transition. In addition to a methodological guide, our work offers a practical toolbox that may be used in a wide range of fields to help detect early warning signals of critical transitions in time series data. PMID:22815897

Early warning of climate tipping points from critical slowing down: comparing methods to improve robustness

PubMed Central

Lenton, T. M.; Livina, V. N.; Dakos, V.; Van Nes, E. H.; Scheffer, M.

2012-01-01

We address whether robust early warning signals can, in principle, be provided before a climate tipping point is reached, focusing on methods that seek to detect critical slowing down as a precursor of bifurcation. As a test bed, six previously analysed datasets are reconsidered, three palaeoclimate records approaching abrupt transitions at the end of the last ice age and three models of varying complexity forced through a collapse of the Atlantic thermohaline circulation. Approaches based on examining the lag-1 autocorrelation function or on detrended fluctuation analysis are applied together and compared. The effects of aggregating the data, detrending method, sliding window length and filtering bandwidth are examined. Robust indicators of critical slowing down are found prior to the abrupt warming event at the end of the Younger Dryas, but the indicators are less clear prior to the Bølling-Allerød warming, or glacial termination in Antarctica. Early warnings of thermohaline circulation collapse can be masked by inter-annual variability driven by atmospheric dynamics. However, rapidly decaying modes can be successfully filtered out by using a long bandwidth or by aggregating data. The two methods have complementary strengths and weaknesses and we recommend applying them together to improve the robustness of early warnings. PMID:22291229

An early warning indicator for atmospheric blocking events using transfer operators

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tantet, Alexis, E-mail: a.j.j.tantet@uu.nl; Burgt, Fiona R. van der; Dijkstra, Henk A.

The existence of persistent midlatitude atmospheric flow regimes with time-scales larger than 5–10 days and indications of preferred transitions between them motivates to develop early warning indicators for such regime transitions. In this paper, we use a hemispheric barotropic model together with estimates of transfer operators on a reduced phase space to develop an early warning indicator of the zonal to blocked flow transition in this model. It is shown that the spectrum of the transfer operators can be used to study the slow dynamics of the flow as well as the non-Markovian character of the reduction. The slowest motionsmore » are thereby found to have time scales of three to six weeks and to be associated with meta-stable regimes (and their transitions) which can be detected as almost-invariant sets of the transfer operator. From the energy budget of the model, we are able to explain the meta-stability of the regimes and the existence of preferred transition paths. Even though the model is highly simplified, the skill of the early warning indicator is promising, suggesting that the transfer operator approach can be used in parallel to an operational deterministic model for stochastic prediction or to assess forecast uncertainty.« less

Determination of the actual evapotranspiration by using remote sensing methods

NASA Astrophysics Data System (ADS)

Bora, Eser

2017-10-01

Evapotranspiration is so crucial for determining amount of the irrigation and the effective water management planning. Moreover, it is vital for determining agricultural drought management and determination the actual evapotranspiration ın a region is critical for early drought warning systems. The main object of this study was to assess accuracy of the remote sensing method (METRIC) by calibrating with the bowen ratio observations at the same time. The research was carried out in the west of Marmara Region, Turkey. Landsat 5 images was used to determine the metric algorithm. By using this algorithms are found. Landsat 5 images file were used to determine actual evapotranspiration and the image's date was June 11 in 2010. This date was used for calibration with available terrestrial observation by using bowen ratio in that time. Landsat images obtained from the web site, earthexplorer.usgs.gov, and results of bowen ratio taken from micrometeorology station. As a result, energy balance parameters that are net radiation, soil heat flux and latent heat flux were compared both metric algorithm and the bowen ration in the images time. The results are found so close to each other.

Advances in the mechanisms and early warning indicators of the postoperative cognitive dysfunction after the extracorporeal circulation.

PubMed

Liu, Chao; Han, Jian-ge

2015-02-01

The high incidence of postoperative cognitive dysfunction (POCD) after extracorporeal circulation has seriously affected the prognosis and quality of life. Its mechanism may involve the inflammatory response and oxidative stress,the excessive phosphorylation of tau protein, the decreased blood volume and oxygen in the cerebral cortex. Appropriate early warning indicators of POCD after the extracorporeal circulation should be chosen to facilitate the cross validation of the results obtained different technical approaches and thus promote the early diagnosis and treatment of POCD.

The Earthquake Early Warning System In Southern Italy: Performance Tests And Next Developments

NASA Astrophysics Data System (ADS)

Zollo, A.; Elia, L.; Martino, C.; Colombelli, S.; Emolo, A.; Festa, G.; Iannaccone, G.

2011-12-01

PRESTo (PRobabilistic and Evolutionary early warning SysTem) is the software platform for Earthquake Early Warning (EEW) in Southern Italy, that integrates recent algorithms for real-time earthquake location, magnitude estimation and damage assessment, into a highly configurable and easily portable package. The system is under active experimentation based on the Irpinia Seismic Network (ISNet). PRESTo processes the live streams of 3C acceleration data for P-wave arrival detection and, while an event is occurring, promptly performs event detection and provides location, magnitude estimations and peak ground shaking predictions at target sites. The earthquake location is obtained by an evolutionary, real-time probabilistic approach based on an equal differential time formulation. At each time step, it uses information from both triggered and not-yet-triggered stations. Magnitude estimation exploits an empirical relationship that correlates it to the filtered Peak Displacement (Pd), measured over the first 2-4 s of P-signal. Peak ground-motion parameters at any distance can be finally estimated by ground motion prediction equations. Alarm messages containing the updated estimates of these parameters can thus reach target sites before the destructive waves, enabling automatic safety procedures. Using the real-time data streaming from the ISNet network, PRESTo has produced a bulletin for about a hundred low-magnitude events occurred during last two years. Meanwhile, the performances of the EEW system were assessed off-line playing-back the records for moderate and large events from Italy, Spain and Japan and synthetic waveforms for large historical events in Italy. These tests have shown that, when a dense seismic network is deployed in the fault area, PRESTo produces reliable estimates of earthquake location and size within 5-6 s from the event origin time (To). Estimates are provided as probability density functions whose uncertainty typically decreases with time, obtaining a stable solution within 10 s from To. The regional approach was recently integrated with a threshold-based early warning method for the definition of alert levels and the estimation of the Potential Damaged Zone (PDZ) in which the highest intensity levels are expected. The dominant period Tau_c and the peak displacement (Pd) are simultaneously measured in a 3s window after the first P-arrival time. Pd and Tau_c are then compared with threshold values, previously established through an empirical regression analysis, that define a decisional table with four alert levels. According to the real-time measured values of Pd and tau_c, each station provides a local alert level that can be used to warn distant sites and to define the extent of the PDZ. The integrated system was validated off-line for the M6.3, 2009 Central Italy earthquake and ten large Japanese events, due to the low-magnitude events currently occurring in Irpinia. The results confirmed the feasibility and the robustness of such an approach, providing reliable predictions of the earthquake damaging effects, that is a relevant information for the efficient planning of the rescue operations in the immediate post-event emergency phase.

Dengue Contingency Planning: From Research to Policy and Practice

PubMed Central

Runge-Ranzinger, Silvia; Kroeger, Axel; Olliaro, Piero; McCall, Philip J.; Sánchez Tejeda, Gustavo; Lloyd, Linda S.; Hakim, Lokman; Bowman, Leigh R.; Horstick, Olaf; Coelho, Giovanini

2016-01-01

Background Dengue is an increasingly incident disease across many parts of the world. In response, an evidence-based handbook to translate research into policy and practice was developed. This handbook facilitates contingency planning as well as the development and use of early warning and response systems for dengue fever epidemics, by identifying decision-making processes that contribute to the success or failure of dengue surveillance, as well as triggers that initiate effective responses to incipient outbreaks. Methodology/Principal findings Available evidence was evaluated using a step-wise process that included systematic literature reviews, policymaker and stakeholder interviews, a study to assess dengue contingency planning and outbreak management in 10 countries, and a retrospective logistic regression analysis to identify alarm signals for an outbreak warning system using datasets from five dengue endemic countries. Best practices for managing a dengue outbreak are provided for key elements of a dengue contingency plan including timely contingency planning, the importance of a detailed, context-specific dengue contingency plan that clearly distinguishes between routine and outbreak interventions, surveillance systems for outbreak preparedness, outbreak definitions, alert algorithms, managerial capacity, vector control capacity, and clinical management of large caseloads. Additionally, a computer-assisted early warning system, which enables countries to identify and respond to context-specific variables that predict forthcoming dengue outbreaks, has been developed. Conclusions/Significance Most countries do not have comprehensive, detailed contingency plans for dengue outbreaks. Countries tend to rely on intensified vector control as their outbreak response, with minimal focus on integrated management of clinical care, epidemiological, laboratory and vector surveillance, and risk communication. The Technical Handbook for Surveillance, Dengue Outbreak Prediction/ Detection and Outbreak Response seeks to provide countries with evidence-based best practices to justify the declaration of an outbreak and the mobilization of the resources required to implement an effective dengue contingency plan. PMID:27653786

SPIRALE: early warning optical space demonstrator

NASA Astrophysics Data System (ADS)

Galindo, D.; Carucci, A.

2004-11-01

Thanks to its global coverage, its peacetime capabilities and its availability, ballistic missiles Early Warning (EW) space systems are identified as a key node of a global missile defence system. Since the Gulf war in 1991, several feasibility studies of such an Early Warning system have been conducted in France. The main conclusions are first that the most appropriate concept is to use infra-red (IR) sensors on geo- stationary orbit satellites and second that the required satellite performances are achievable and accessible to European industries, even if technological developments are necessary. Besides that, it was recommended to prepare the development of the EW operational system, by demonstrating its achievable performances on the basis of collected background images and available target IR signatures. This is the objective of the "EW optical space demonstrator", also named SPIRALE (this a French acronym which stands for "Preparatory IR Program for EW"). A contract has been awarded early 2004, by DGA/SPOTI (French Armament Procurement Agency), to EADS Astrium France, with a significant participation of Alcatel Space, to perform this demonstration.

Early Flood Warning in Africa: Results of a Feasibility study in the JUBA, SHABELLE and ZAMBEZI

NASA Astrophysics Data System (ADS)

Pappenberger, F. P.; de Roo, A. D.; Buizza, Roberto; Bodis, Katalin; Thiemig, Vera

2009-04-01

Building on the experiences gained with the European Flood Alert System (EFAS), pilot studies are carried out in three river basins in Africa. The European Flood Alert System, pre-operational since 2003, provides early flood alerts for European rivers. At present, the experiences with the European EFAS system are used to evaluate the feasibility of flood early warning for Africa. Three case studies are carried in the Juba and Shabelle rivers (Somalia and Ethiopia), and in the Zambesi river (Southern Africa). Predictions in these data scarce regions are extremely difficult to make as records of observations are scarce and often unreliable. Meteorological and Discharge observations are used to calibrate and test the model, as well as soils, landuse and topographic data available within the JRC African Observatory. ECMWF ERA-40, ERA-Interim data and re-forecasts of flood events from January to March 1978, and in March 2001 are evaluated to examine the feasibility for early flood warning. First results will be presented.

From Data to Knowledge — Faster: GOES Early Fire Detection System to Inform Operational Wildfire Response and Management

NASA Astrophysics Data System (ADS)

Koltunov, A.; Quayle, B.; Prins, E. M.; Ambrosia, V. G.; Ustin, S.

2014-12-01

Fire managers at various levels require near-real-time, low-cost, systematic, and reliable early detection capabilities with minimal latency to effectively respond to wildfire ignitions and minimize the risk of catastrophic development. The GOES satellite images collected for vast territories at high temporal frequencies provide a consistent and reliable source for operational active fire mapping realized by the WF-ABBA algorithm. However, their potential to provide early warning or rapid confirmation of initial fire ignition reports from conventional sources remains underutilized, partly because the operational wildfire detection has been successfully optimized for users and applications for which timeliness of initial detection is a low priority, contrasting to the needs of first responders. We present our progress in developing the GOES Early Fire Detection (GOES-EFD) system, a collaborative effort led by University of California-Davis and USDA Forest Service. The GOES-EFD specifically focuses on first detection timeliness for wildfire incidents. It is automatically trained for a monitored scene and capitalizes on multiyear cross-disciplinary algorithm research. Initial retrospective tests in Western US demonstrate significantly earlier identification detection of new ignitions than existing operational capabilities and a further improvement prospect. The GOES-EFD-β prototype will be initially deployed for the Western US region to process imagery from GOES-NOP and the rapid and 4 times higher spatial resolution imagery from GOES-R — the upcoming next generation of GOES satellites. These and other enhanced capabilities of GOES-R are expected to significantly improve the timeliness of fire ignition information from GOES-EFD.

Combining Real-Time Seismic and GPS Data for Earthquake Early Warning (Invited)

NASA Astrophysics Data System (ADS)

Boese, M.; Heaton, T. H.; Hudnut, K. W.

2013-12-01

Scientists at Caltech, UC Berkeley, the Univ. of SoCal, the Univ. of Washington, the US Geological Survey, and ETH Zurich have developed an earthquake early warning (EEW) demonstration system for California and the Pacific Northwest. To quickly determine the earthquake magnitude and location, 'ShakeAlert' currently processes and interprets real-time data-streams from ~400 seismic broadband and strong-motion stations within the California Integrated Seismic Network (CISN). Based on these parameters, the 'UserDisplay' software predicts and displays the arrival and intensity of shaking at a given user site. Real-time ShakeAlert feeds are currently shared with around 160 individuals, companies, and emergency response organizations to educate potential users about EEW and to identify needs and applications of EEW in a future operational warning system. Recently, scientists at the contributing institutions have started to develop algorithms for ShakeAlert that make use of high-rate real-time GPS data to improve the magnitude estimates for large earthquakes (M>6.5) and to determine slip distributions. Knowing the fault slip in (near) real-time is crucial for users relying on or operating distributed systems, such as for power, water or transportation, especially if these networks run close to or across large faults. As shown in an earlier study, slip information is also useful to predict (in a probabilistic sense) how far a fault rupture will propagate, thus enabling more robust probabilistic ground-motion predictions at distant locations. Finally, fault slip information is needed for tsunami warning, such as in the Cascadia subduction-zone. To handle extended fault-ruptures of large earthquakes in real-time, Caltech and USGS Pasadena are currently developing and testing a two-step procedure that combines seismic and geodetic data; in the first step, high-frequency strong-motion amplitudes are used to rapidly classify near-and far-source stations. Then, the location and extent of the 2D fault rupture is determined from comparison with pre-calculated generic and fault-specific templates ('FinDer' algorithm, Finite Fault Rupture Detector). In the second step, long-period dynamic displacement amplitudes from the GPS sites are back-projected onto this rupture line/plane to estimate the slip amplitudes ('GPSlip' algorithm). The corresponding back-projection relations were empirically derived from a suite of 3D waveform simulations. We are currently testing our approach in southern California (both real-time and offline), although not yet included in the current distribution of ShakeAlert. RTK/PPP(AR) solutions from the RTNet software at USGS Pasadena currently provide 1 Hz real-time position times series at ~100 GPS sensor locations. Output is in openly available in JSON format. We and UNAVCO have tested onsite (in-receiver) PPP(AR) processing using Trimble NetR9 receivers with RTX & GLONASS options enabled, of which Caltech has recently purchased 41 new units. These special GPS receivers will provide 5 Hz position and velocity streams. We will deliver the GPS RTX output (in GSOF format) into the EEW system (in Earthworm tracebuf2 format). The new receivers are to be installed at 'zipper array' stations of the SCSN in upcoming months. In addition, we have developed a framework for end-to-end offline testing with archived and simulated waveform data.

Earthquake Early Warning Beta Users: Java, Modeling, and Mobile Apps

NASA Astrophysics Data System (ADS)

Strauss, J. A.; Vinci, M.; Steele, W. P.; Allen, R. M.; Hellweg, M.

2014-12-01

Earthquake Early Warning (EEW) is a system that can provide a few to tens of seconds warning prior to ground shaking at a user's location. The goal and purpose of such a system is to reduce, or minimize, the damage, costs, and casualties resulting from an earthquake. A demonstration earthquake early warning system (ShakeAlert) is undergoing testing in the United States by the UC Berkeley Seismological Laboratory, Caltech, ETH Zurich, University of Washington, the USGS, and beta users in California and the Pacific Northwest. The beta users receive earthquake information very rapidly in real-time and are providing feedback on their experiences of performance and potential uses within their organization. Beta user interactions allow the ShakeAlert team to discern: which alert delivery options are most effective, what changes would make the UserDisplay more useful in a pre-disaster situation, and most importantly, what actions users plan to take for various scenarios. Actions could include: personal safety approaches, such as drop cover, and hold on; automated processes and procedures, such as opening elevator or fire stations doors; or situational awareness. Users are beginning to determine which policy and technological changes may need to be enacted, and funding requirements to implement their automated controls. The use of models and mobile apps are beginning to augment the basic Java desktop applet. Modeling allows beta users to test their early warning responses against various scenarios without having to wait for a real event. Mobile apps are also changing the possible response landscape, providing other avenues for people to receive information. All of these combine to improve business continuity and resiliency.

Traffic sign recognition by color segmentation and neural network

NASA Astrophysics Data System (ADS)

Surinwarangkoon, Thongchai; Nitsuwat, Supot; Moore, Elvin J.

2011-12-01

An algorithm is proposed for traffic sign detection and identification based on color filtering, color segmentation and neural networks. Traffic signs in Thailand are classified by color into four types: namely, prohibitory signs (red or blue), general warning signs (yellow) and construction area warning signs (amber). A color filtering method is first used to detect traffic signs and classify them by type. Then color segmentation methods adapted for each color type are used to extract inner features, e.g., arrows, bars etc. Finally, neural networks trained to recognize signs in each color type are used to identify any given traffic sign. Experiments show that the algorithm can improve the accuracy of traffic sign detection and recognition for the traffic signs used in Thailand.

Vision-based algorithms for near-host object detection and multilane sensing

NASA Astrophysics Data System (ADS)

Kenue, Surender K.

1995-01-01

Vision-based sensing can be used for lane sensing, adaptive cruise control, collision warning, and driver performance monitoring functions of intelligent vehicles. Current computer vision algorithms are not robust for handling multiple vehicles in highway scenarios. Several new algorithms are proposed for multi-lane sensing, near-host object detection, vehicle cut-in situations, and specifying regions of interest for object tracking. These algorithms were tested successfully on more than 6000 images taken from real-highway scenes under different daytime lighting conditions.

Time-to-impact estimation in passive missile warning systems

NASA Astrophysics Data System (ADS)

Şahıngıl, Mehmet Cihan

2017-05-01

A missile warning system can detect the incoming missile threat(s) and automatically cue the other Electronic Attack (EA) systems in the suit, such as Directed Infrared Counter Measure (DIRCM) system and/or Counter Measure Dispensing System (CMDS). Most missile warning systems are currently based on passive sensor technology operating in either Solar Blind Ultraviolet (SBUV) or Midwave Infrared (MWIR) bands on which there is an intensive emission from the exhaust plume of the threatening missile. Although passive missile warning systems have some clear advantages over pulse-Doppler radar (PDR) based active missile warning systems, they show poorer performance in terms of time-to-impact (TTI) estimation which is critical for optimizing the countermeasures and also "passive kill assessment". In this paper, we consider this problem, namely, TTI estimation from passive measurements and present a TTI estimation scheme which can be used in passive missile warning systems. Our problem formulation is based on Extended Kalman Filter (EKF). The algorithm uses the area parameter of the threat plume which is derived from the used image frame.

Tsunami Early Warning for the Indian Ocean Region - Status and Outlook

NASA Astrophysics Data System (ADS)

Lauterjung, Joern; Rudloff, Alexander; Muench, Ute; Gitews Project Team

2010-05-01

The German-Indonesian Tsunami Early Warning System (GITEWS) for the Indian Ocean region has gone into operation in Indonesia in November 2008. The system includes a seismological network, together with GPS stations and a network of GPS buoys additionally equipped with ocean bottom pressure sensors and a tide gauge network. The different sensor systems have, for the most part, been installed and now deliver respective data either online or interactively upon request to the Warning Centre in Jakarta. Before 2011, however, the different components requires further optimization and fine tuning, local personnel needs to be trained and eventual problems in the daily operation have to be dealt with. Furthermore a company will be founded in the near future, which will guarantee a sustainable maintenance and operation of the system. This concludes the transfer from a temporarily project into a permanent service. This system established in Indonesia differs from other Tsunami Warning Systems through its application of modern scientific methods and technologies. New procedures for the fast and reliable determination of strong earthquakes, deformation monitoring by GPS, the modeling of tsunamis and the assessment of the situation have been implemented in the Warning System architecture. In particular, the direct incorporation of different sensors provides broad information already at the early stages of Early Warning thus resulting in a stable system and minimizing breakdowns and false alarms. The warning system is designed in an open and modular structure based on the most recent developments and standards of information technology. Therefore, the system can easily integrate additional sensor components to be used for other multi-hazard purposes e.g. meteorological and hydrological events. Up to now the German project group is cooperating in the Indian Ocean region with Sri Lanka, the Maldives, Iran, Yemen, Tanzania and Kenya to set up the equipment primarily for seismological monitoring and data analysis. The automatic seismic data processing software SeisComP3, is not only operational in the warning centre in Jakarta and successfully used for rapid earthquake information, but also in different Indian Ocean rim countries like the once mentioned before as well as in India, Thailand and Pakistan. Close cooperation has been established with Australia, South Africa and India for the real-time exchange mainly of seismological and sea level data.

GASAKe: forecasting landslide activations by a genetic-algorithms based hydrological model

NASA Astrophysics Data System (ADS)

Terranova, O. G.; Gariano, S. L.; Iaquinta, P.; Iovine, G. G. R.

2015-02-01

GASAKe is a new hydrological model aimed at forecasting the triggering of landslides. The model is based on genetic-algorithms and allows to obtaining thresholds of landslide activation from the set of historical occurrences and from the rainfall series. GASAKe can be applied to either single landslides or set of similar slope movements in a homogeneous environment. Calibration of the model is based on genetic-algorithms, and provides for families of optimal, discretized solutions (kernels) that maximize the fitness function. Starting from these latter, the corresponding mobility functions (i.e. the predictive tools) can be obtained through convolution with the rain series. The base time of the kernel is related to the magnitude of the considered slope movement, as well as to hydro-geological complexity of the site. Generally, smaller values are expected for shallow slope instabilities with respect to large-scale phenomena. Once validated, the model can be applied to estimate the timing of future landslide activations in the same study area, by employing recorded or forecasted rainfall series. Example of application of GASAKe to a medium-scale slope movement (the Uncino landslide at San Fili, in Calabria, Southern Italy) and to a set of shallow landslides (in the Sorrento Peninsula, Campania, Southern Italy) are discussed. In both cases, a successful calibration of the model has been achieved, despite unavoidable uncertainties concerning the dates of landslide occurrence. In particular, for the Sorrento Peninsula case, a fitness of 0.81 has been obtained by calibrating the model against 10 dates of landslide activation; in the Uncino case, a fitness of 1 (i.e. neither missing nor false alarms) has been achieved against 5 activations. As for temporal validation, the experiments performed by considering the extra dates of landslide activation have also proved satisfactory. In view of early-warning applications for civil protection purposes, the capability of the model to simulate the occurrences of the Uncino landslide has been tested by means of a progressive, self-adaptive procedure. Finally, a sensitivity analysis has been performed by taking into account the main parameters of the model. The obtained results are quite promising, given the high performance of the model obtained against different types of slope instabilities, characterized by several historical activations. Nevertheless, further refinements are still needed for applications to landslide risk mitigation within early-warning and decision-support systems.

The Trend of Voluntary Warnings in Electronic Nicotine Delivery System Magazine Advertisements.

PubMed

Shang, Ce; Chaloupka, Frank J

2017-01-10

Some manufacturers of electronic nicotine delivery systems (ENDS) voluntarily carried health warnings in their advertisements. This study examined these voluntary warnings in magazine ads and plotted their trends between 2012 and early 2015. ENDS magazine ads were obtained through Kantar media and warnings were collected from the Chicago Public Library or the Trinkets and Trash surveillance system. The prevalence of voluntary warnings, warnings with the specific capitalized word "WARNING", and MarkTen warnings were examined after being weighted using factors related to exposure between January 2012 and March 2015. Five brands (MarkTen, NJOY, MISTIC, and some Blu) carried warnings during the study period. The prevalence of warnings post 2012 that contained a description of nicotine did not significantly increase until the launch of MarkTen, which also happened several months before April 2014 when the U.S. food and drug administration (FDA) published its proposed deeming rule. In addition, none of these warnings met the criteria required by the FDA in the final rules. Voluntary warnings, particularly MarkTen warnings, significantly increased in ENDS magazine ads between 2014 and 2015. It is important to monitor how ENDS manufacturers will comply with the FDA regulation related to warnings and how this regulation will ultimately impact ENDS risk perceptions and use.

Aerial Refueling For NATO’s Smart Defence Initiative

DTIC Science & Technology

2012-04-01

Rome: NATO Defense College, 2012, 148. 40 David A. Brown , "NATO Studying Development of Dedicated Refueling Unit Similar to Early Warning Force...accessed March 1, 2012). Brown , David A. "NATO Studying Development of Dedicated Refueling Unit Similar to Early Warning Force." Aviation Week...Aircraft. Coulsdon, Surrey: IHS Global Limited, 2011. Jennings, Gareth . "Nations Pool for NATO C-17A Fleet." Jane’s Defence Weekly, October 2008

Getting Students on Track for Graduation: Impacts of the Early Warning Intervention and Monitoring System after One Year. REL 2017-272

ERIC Educational Resources Information Center

Faria, Ann-Marie; Sorensen, Nicholas; Heppen, Jessica; Bowdon, Jill; Taylor, Suzanne; Eisner, Ryan; Foster, Shandu

2017-01-01

Although high school graduation rates are rising--the national rate was 82 percent during the 2013/14 school year (U.S. Department of Education, 2015)--dropping out remains a persistent problem in the Midwest and nationally. Many schools now use early warning systems to identify students who are at risk of not graduating, with the goal of…

Net Warrior D10 Technology Report: Airborne Early Warning and Control (AEW&C) and Data Link Nodes

DTIC Science & Technology

2012-04-01

ADO ) approach to implementing Network Centric Warfare (NCW) through ‘learning by doing’. Net Warrior was conceived to address, through... frameworks are able to satisfy design needs of applications to produce stable mission and net centric systems. NW-D10 employed a SOA approach to...UNCLASSIFIED Net Warrior D10 Technology Report: Airborne Early Warning and Control (AEW&C) and Data Link Nodes Derek Dominish

Efficient Flame Detection and Early Warning Sensors on Combustible Materials Using Hierarchical Graphene Oxide/Silicone Coatings.

PubMed

Wu, Qian; Gong, Li-Xiu; Li, Yang; Cao, Cheng-Fei; Tang, Long-Cheng; Wu, Lianbin; Zhao, Li; Zhang, Guo-Dong; Li, Shi-Neng; Gao, Jiefeng; Li, Yongjin; Mai, Yiu-Wing

2018-01-23

Design and development of smart sensors for rapid flame detection in postcombustion and early fire warning in precombustion situations are critically needed to improve the fire safety of combustible materials in many applications. Herein, we describe the fabrication of hierarchical coatings created by assembling a multilayered graphene oxide (GO)/silicone structure onto different combustible substrate materials. The resulting coatings exhibit distinct temperature-responsive electrical resistance change as efficient early warning sensors for detecting abnormal high environmental temperature, thus enabling fire prevention below the ignition temperature of combustible materials. After encountering a flame attack, we demonstrate extremely rapid flame detection response in 2-3 s and excellent flame self-extinguishing retardancy for the multilayered GO/silicone structure that can be synergistically transformed to a multiscale graphene/nanosilica protection layer. The hierarchical coatings developed are promising for fire prevention and protection applications in various critical fire risk and related perilous circumstances.

A holistic approach to SIM platform and its application to early-warning satellite system

NASA Astrophysics Data System (ADS)

Sun, Fuyu; Zhou, Jianping; Xu, Zheyao

2018-01-01

This study proposes a new simulation platform named Simulation Integrated Management (SIM) for the analysis of parallel and distributed systems. The platform eases the process of designing and testing both applications and architectures. The main characteristics of SIM are flexibility, scalability, and expandability. To improve the efficiency of project development, new models of early-warning satellite system were designed based on the SIM platform. Finally, through a series of experiments, the correctness of SIM platform and the aforementioned early-warning satellite models was validated, and the systematical analyses for the orbital determination precision of the ballistic missile during its entire flight process were presented, as well as the deviation of the launch/landing point. Furthermore, the causes of deviation and prevention methods will be fully explained. The simulation platform and the models will lay the foundations for further validations of autonomy technology in space attack-defense architecture research.

Changing skewness: an early warning signal of regime shifts in ecosystems.

PubMed

Guttal, Vishwesha; Jayaprakash, Ciriyam

2008-05-01

Empirical evidence for large-scale abrupt changes in ecosystems such as lakes and vegetation of semi-arid regions is growing. Such changes, called regime shifts, can lead to degradation of ecological services. We study simple ecological models that show a catastrophic transition as a control parameter is varied and propose a novel early warning signal that exploits two ubiquitous features of ecological systems: nonlinearity and large external fluctuations. Either reduced resilience or increased external fluctuations can tip ecosystems to an alternative stable state. It is shown that changes in asymmetry in the distribution of time series data, quantified by changing skewness, is a model-independent and reliable early warning signal for both routes to regime shifts. Furthermore, using model simulations that mimic field measurements and a simple analysis of real data from abrupt climate change in the Sahara, we study the feasibility of skewness calculations using data available from routine monitoring.

Evaluating probabilistic dengue risk forecasts from a prototype early warning system for Brazil.

PubMed

Lowe, Rachel; Coelho, Caio As; Barcellos, Christovam; Carvalho, Marilia Sá; Catão, Rafael De Castro; Coelho, Giovanini E; Ramalho, Walter Massa; Bailey, Trevor C; Stephenson, David B; Rodó, Xavier

2016-02-24

Recently, a prototype dengue early warning system was developed to produce probabilistic forecasts of dengue risk three months ahead of the 2014 World Cup in Brazil. Here, we evaluate the categorical dengue forecasts across all microregions in Brazil, using dengue cases reported in June 2014 to validate the model. We also compare the forecast model framework to a null model, based on seasonal averages of previously observed dengue incidence. When considering the ability of the two models to predict high dengue risk across Brazil, the forecast model produced more hits and fewer missed events than the null model, with a hit rate of 57% for the forecast model compared to 33% for the null model. This early warning model framework may be useful to public health services, not only ahead of mass gatherings, but also before the peak dengue season each year, to control potentially explosive dengue epidemics.

The Self-Organising Seismic Early Warning Information Network: Scenarios

NASA Astrophysics Data System (ADS)

Kühnlenz, F.; Fischer, J.; Eveslage, I.

2009-04-01

SAFER and EDIM working groups, the Department of Computer Science, Humboldt-Universität zu Berlin, Berlin, Germany, and Section 2.1 Earthquake Risk and Early Warning, GFZ German Research Centre for Geosciences, Germany Contact: Frank Kühnlenz, kuehnlenz@informatik.hu-berlin.de The Self-Organising Seismic Early Warning Information Network (SOSEWIN) represents a new approach for Earthquake Early Warning Systems (EEWS), consisting in taking advantage of novel wireless communications technologies without the need of a planned, centralised infrastructure. It also sets out to overcome problems of insufficient node density, which typically affects present existing early warning systems, by having the SOSEWIN seismological sensing units being comprised of low-cost components (generally bought "off-the-shelf"), with each unit initially costing 100's of Euros, in contrast to 1,000's to 10,000's for standard seismological stations. The reduced sensitivity of the new sensing units arising from the use of lower-cost components will be compensated by the network's density, which in the future is expected to number 100's to 1000's over areas served currently by the order of 10's of standard stations. The robustness, independence of infrastructure, spontaneous extensibility due to a self-healing/self-organizing character in the case of removing/failing or adding sensors makes SOSEWIN potentially useful for various use cases, e.g. monitoring of building structures or seismic microzonation. Nevertheless its main purpose is the earthquake early warning, for which reason the ground motion is continuously monitored by conventional accelerometers (3-component). It uses SEEDLink to store and provide access to the sensor data. SOSEWIN considers also the needs of earthquake task forces, which want to set-up a temporary seismic network rapidly and with light-weighted stations to record after-shocks. The wireless and self-organising character of this sensor network should be of great value to do this job in a shorter time and with less manpower compared to using common seismic stations. We present here the graphical front-end of SOSEWIN in its usage for different scenarios. It belongs to a management infrastructure based on GIS and database technologies and therefore coupling with existing infrastructures should be simplified. Connecting the domain expert's laptop running the management software with a SOSEWIN may be fulfilled via any arbitrary node in the network (on-site access) or via a gateway node from a remote location using the internet. The scenarios focus on the needs of certain domain experts (seismologists or maybe engineers) and include the planning of a network installation, support during the installation process and testing of this installation. Another scenario mentions monitoring aspects of an already installed network and finally a scenario deals with the visualization of the alarming protocol detecting an earthquake event and issuing an early warning.

Development of a GNSS-Enhanced Tsunami Early Warning System

NASA Astrophysics Data System (ADS)

Bawden, G. W.; Melbourne, T. I.; Bock, Y.; Song, Y. T.; Komjathy, A.

2015-12-01

The past decade has witnessed a terrible loss of life and economic disruption caused by large earthquakes and resultant tsunamis impacting coastal communities and infrastructure across the Indo-Pacific region. NASA has funded the early development of a prototype real-time Global Navigation Satellite System (RT-GNSS) based rapid earthquake and tsunami early warning (GNSS-TEW) system that may be used to enhance seismic tsunami early warning systems for large earthquakes. This prototype GNSS-TEW system geodetically estimates fault parameters (earthquake magnitude, location, strike, dip, and slip magnitude/direction on a gridded fault plane both along strike and at depth) and tsunami source parameters (seafloor displacement, tsunami energy scale, and 3D tsunami initials) within minutes after the mainshock based on dynamic numerical inversions/regressions of the real-time measured displacements within a spatially distributed real-time GNSS network(s) spanning the epicentral region. It is also possible to measure fluctuations in the ionosphere's total electron content (TEC) in the RT-GNSS data caused by the pressure wave from the tsunami. This TEC approach can detect if a tsunami has been triggered by an earthquake, track its waves as they propagate through the oceanic basins, and provide upwards of 45 minutes early warning. These combined real-time geodetic approaches will very quickly address a number of important questions in the immediate minutes following a major earthquake: How big was the earthquake and what are its fault parameters? Could the earthquake have produced a tsunami and was a tsunami generated?

Tsunami prevention and mitigation necessities and options derived from tsunami risk assessment in Indonesia

NASA Astrophysics Data System (ADS)

Post, J.; Zosseder, K.; Wegscheider, S.; Steinmetz, T.; Mück, M.; Strunz, G.; Riedlinger, T.; Anwar, H. Z.; Birkmann, J.; Gebert, N.

2009-04-01

Risk and vulnerability assessment is an important component of an effective End-to-End Tsunami Early Warning System and therefore contributes significantly to disaster risk reduction. Risk assessment is a key strategy to implement and design adequate disaster prevention and mitigation measures. The knowledge about expected tsunami hazard impacts, exposed elements, their susceptibility, coping and adaptation mechanisms is a precondition for the development of people-centred warning structures, local specific response and recovery policy planning. The developed risk assessment and its components reflect the disaster management cycle (disaster time line) and cover the early warning as well as the emergency response phase. Consequently the components hazard assessment, exposure (e.g. how many people/ critical facilities are affected?), susceptibility (e.g. are the people able to receive a tsunami warning?), coping capacity (are the people able to evacuate in time?) and recovery (are the people able to restore their livelihoods?) are addressed and quantified. Thereby the risk assessment encompasses three steps: (i) identifying the nature, location, intensity and probability of potential tsunami threats (hazard assessment); (ii) determining the existence and degree of exposure and susceptibility to those threats; and (iii) identifying the coping capacities and resources available to address or manage these threats. The paper presents results of the research work, which is conducted in the framework of the GITEWS project and the Joint Indonesian-German Working Group on Risk Modelling and Vulnerability Assessment. The assessment methodology applied follows a people-centred approach to deliver relevant risk and vulnerability information for the purposes of early warning and disaster management. The analyses are considering the entire coastal areas of Sumatra, Java and Bali facing the Sunda trench. Selected results and products like risk maps, guidelines, decision support information and other GIS products will be presented. The focus of the products is on the one hand to provide relevant risk assessment products as decision support to issue a tsunami warning within the early warning stage. On the other hand the maps and GIS products shall provide relevant information to enable local decision makers to act adequately concerning their local risks. It is shown that effective prevention and mitigation measures can be designed based on risk assessment results and information especially when used pro-active and beforehand a disaster strikes. The conducted hazard assessment provides the probability of an area to be affected by a tsunami threat divided into two ranked impact zones. The two divided impact zones directly relate to tsunami warning levels issued by the Early Warning Center and consequently enable the local decision maker to base their planning (e.g. evacuation) accordingly. Within the tsunami hazard assessment several hundred pre-computed tsunami scenarios are analysed. This is combined with statistical analysis of historical event data. Probabilities of tsunami occurrence considering probabilities of different earthquake magnitudes, occurrences of specific wave heights at coast and spatial inundation probability are computed. Hazard assessment is then combined with a comprehensive vulnerability assessment. Here deficits in e.g. people's ability to receive and understand a tsunami warning and deficits in their ability to respond adequately (evacuate on time) are quantified and are visualized for the respective coastal areas. Hereby socio-economic properties (determining peoples ability to understand a warning and to react) are combined with environmental conditions (land cover, slope, population density) to calculate the time needed to evacuate (reach a tsunami safe area derived through the hazard assessment). This is implemented using a newly developed GIS cost-distance weighting approach. For example, the amount of people affected in a certain area is dependent on expected tsunami intensity, inundated area, estimated tsunami arrival time and available time for evacuation. Referring to the Aceh 2004 Tsunami, an estimated amount of people affected (dead/injured) of 21000 for Kabubaten Aceh Jaya and 85000 for Kab. Banda Aceh is in a comparable range with reported values of 19661 and 78417 (JICA 2005) respectively. Hence the established methodology provides reliable estimates of people affected and people's ability to reach a safe area. Based on the spatial explicit detection of e.g. high tsunami risk areas (and the assessed root causes therefore), specific disaster risk reduction and early warning strategies can be designed. For example additional installation of technical warning dissemination device, community based preparedness and awareness programmes (education), structural and non-structural measures (e.g. land use conversion, coastal engineering), effective evacuation, contingency and household recovery aid planning can be employed and/or optimized within high tsunami risk areas as a first priority. In the context of early warning, spatially distributed information like degree of expected hazard impact, exposure of critical facilities (e.g. hospitals, schools), potential people dead/injured depending on available response times, location of safe and shelter areas can be disseminated and used for decision making. Keywords: Tsunami risk, hazard and vulnerability assessment, early warning, tsunami mitigation and prevention, Indonesia

Global Environmental Alert Service

NASA Astrophysics Data System (ADS)

Grasso, V. F.; Cervone, G.; Singh, A.; Kafatos, M.

2006-12-01

Every year natural disasters such as earthquakes, floods, hurricanes, tsunamis, etc. occur around the world, causing hundreds of thousands of deaths and injuries, billions of dollars in economic losses, and destroying natural landmarks and adveresely affecting ecosystems. Due to increasing urbanization, and increasingly higher percentage of the world's population living in megacities, the existence of nuclear power plants and other facilities whose potential destruction poses unacceptable high risks, natural hazards represent an increasing threat for economic losses, as well as risk to people and property. Warning systems represent an innovative and effective approach to mitigate the risks associated with natural hazards. Several state-of-the-art analyses show that early warning technologies are now available for most natural hazards and systems are already in operation in some parts of the world. Nevertheless, recent disasters such as the 2004 Indian Ocean tsunami, the 2005 Kashmir earthquake and the 2005 Katrina hurricane, highlighted inadequacies in early warning system technologies. Furthermore, not all available technologies are deployed in every part of the world, due to the lack of awareness and resources in the poorer countries, leaving very large and densely populated areas at risk. Efforts towards the development of a global warning system are necessary for filling the gaps of existing technologies. A globally comprehensive early warning system based on existing technologies will be a means to consolidate scientific knowledge, package it in a form usable to international and national decision makers and actively disseminate this information to protect people and properties. There is not a single information broker who searches and packages the policy relevant material and delivers it in an understandable format to the public and decision makers. A critical review of existing systems reveals the need for the innovative service. We propose here a Global Environmental Alert Service (GEAS) that could provide information from monitoring, Earth observing and early warning systems to users in a near real time mode and bridge the gap between the scientific community and policy makers. Characteristics and operational aspects of GEAS are discussed.

Study of Water Pollution Early Warning Framework Based on Internet of Things

NASA Astrophysics Data System (ADS)

Chengfang, H.; Xiao, X.; Dingtao, S.; Bo, C.; Xiongfei, W.

2016-06-01

In recent years, with the increasing world environmental pollution happening, sudden water pollution incident has become more and more frequently in China. It has posed a serious threat to water safety of the people living in the water source area. Conventional water pollution monitoring method is manual periodic testing, it maybe miss the best time to find that pollution incident. This paper proposes a water pollution warning framework to change this state. On the basis of the Internet of things, we uses automatic water quality monitoring technology to realize monitoring. We calculate the monitoring data with water pollution model to judge whether the water pollution incident is happen or not. Water pollution warning framework is divided into three layers: terminal as the sensing layer, it with the deployment of the automatic water quality pollution monitoring sensor. The middle layer is the transfer network layer, data information implementation is based on GPRS wireless network transmission. The upper one is the application layer. With these application systems, early warning information of water pollution will realize the high-speed transmission between grassroots units and superior units. The paper finally gives an example that applying this pollution warning framework to water quality monitoring of Beijing, China, it greatly improves the speed of the pollution warning responding of Beijing.

Exploring radar and lightning variables associated with the Lightning Jump. Can we predict the size of the hail?

NASA Astrophysics Data System (ADS)

Farnell, C.; Rigo, T.; Pineda, N.

2018-04-01

Severe weather regularly hits the Lleida Plain (western part of Catalonia, NE of Iberian Peninsula), causing important damage to the local agriculture. In order to help severe weather surveillance tasks, the Meteorological Service of Catalonia (SMC) implemented in 2016 the Lightning Jump (LJ) algorithm as operative warning tool after an exhaustive validation phase of several months. The present study delves into the analysis of the relationship between Lightning Jump alerts and hail occurrence, through the analysis of lightning and radar variables in the moment when the warning is issued. Overall, the study has consisted of the analysis of 149 cases, grouping them into two categories according to hail size: small and large hail, with a threshold of 2 cm of diameter. The thunderstorms related to big sized hail presented remarkable differences in some of the variables analysed that could help forecast the size of hail when the LJ alert is triggered. Moreover, other variables have been allowed to observe and to corroborate how the LJ algorithm works during the 13 min before the warning is triggered.

PRESSCA: A regional operative Early Warning System for landslides risk scenario assessment

NASA Astrophysics Data System (ADS)

Ponziani, Francesco; Stelluti, Marco; Berni, Nicola; Brocca, Luca; Moramarco, Tommaso

2013-04-01

The Italian national alert system for the hydraulic and hydrogeological risk is ensured by the National Civil Protection Department, through the "Functional Centres" Network, together with scientific/technical Support Centres, named "Competence Centres". The role of the Functional Centres is to alert regional/national civil protection network, to manage the prediction and the monitoring phases, thus ensuring the flow of data for the management of the emergency. The Umbria regional alerting procedure is based on three increasing warning levels of criticality for 6 sub-areas (~1200 km²). Specifically, for each duration (from 1 to 48 hours), three criticality levels are assigned to the rainfall values corresponding to a recurrence interval of 2, 5, and 10 years. In order to improve confidence on the daily work for hydrogeological risk assessment and management, a simple and operational early warning system for the prediction of shallow landslide triggering on regional scale was implemented. The system is primarily based on rainfall thresholds, which represent the main element of evaluation for the early-warning procedures of the Italian Civil Protection system. Following previous studies highlighting that soil moisture conditions play a key role on landslide triggering, a continuous physically-based soil water balance model was implemented for the estimation of soil moisture conditions over the whole regional territory. In fact, a decreasing trend between the cumulated rainfall values over 24, 36 and 48 hours and the soil moisture conditions prior to past landslide events was observed. This trend provides an easy-to-use tool to dynamically adjust the operational rainfall thresholds with the soil moisture conditions simulated by the soil water balance model prior to rainfall events. The application of this procedure allowed decreasing the uncertainties tied to the application of the rainfall thresholds only. The system is actually operational in real-time and it was recently coupled with quantitative rainfall and temperature forecasts (given by the COSMO ME local scale models for Umbria) to extend the prediction up to 72 hours forecast. The main output is constituted by four spatially distributed early warning indicators (normal, caution, warning, alarm), in compliance with national and regional law, based on the comparison between the observed (forecasted) rainfall and the dynamic thresholds. The early warning indicators, calculated over the whole regional territory, are combined with susceptibility and vulnerability layers using a WEB-GIS platform, in order to build a near real time risk scenario. The main outcome of the system is a spatially distributed landslide hazard map with the highlight of areas where local risk situations may arise due to landslides induced by the interaction between meteorological forcing and the presence of vulnerability elements. The System is inclusive of specific sections dedicated to areas with specific risks (as debris flows prone areas), with specific thresholds. The main purpose of this study is firstly to describe the operational early warning system. Then, the integration of near real-time soil moisture data obtained through the satellite sensor ASCAT (Advanced SCATterometer) within the system is shown. This could allow enhancing the reliability of the modelled soil moisture data over the regional territory. The recent rainfall event of 11-14 November 2012 is used as case study. Reported triggered landslides are studied and used in order to check/refine the early warning system.

Geostationary Lightning Mapper for GOES-R and Beyond

NASA Technical Reports Server (NTRS)

Goodman, Steven J.; Blakeslee, R. J.; Koshak, W.

2008-01-01

The Geostationary Lightning Mapper (GLM) is a single channel, near-IR imager/optical transient event detector, used to detect, locate and measure total lightning activity over the full-disk as part of a 3-axis stabilized, geostationary weather satellite system. The next generation NOAA Geostationary Operational Environmental Satellite (GOES-R) series with a planned launch readiness in December 2014 will carry a GLM that will provide continuous day and night observations of lightning from the west coast of Africa (GOES-E) to New Zealand (GOES-W) when the constellation is fUlly operational. The mission objectives for the GLM are to 1) provide continuous, full-disk lightning measurements for storm warning and nowcasting, 2) provide early warning of tornadic activity, and 3) accumulate a long-term database to track decadal changes of lightning. The GLM owes its heritage to the NASA Lightning Imaging Sensor (1997-Present) and the Optical Transient Detector (1995-2000), which were developed for the Earth Observing System and have produced a combined 13 year data record of global lightning activity. Instrument formulation studies were completed in March 2007 and the implementation phase to develop a prototype model and up to four flight models will be underway in the latter part of 2007. In parallel with the instrument development, a GOES-R Risk Reduction Team and Algorithm Working Group Lightning Applications Team have begun to develop the Level 2 algorithms and applications. Proxy total lightning data from the NASA Lightning Imaging Sensor on the Tropical Rainfall Measuring Mission (TRMM) satellite and regional test beds (e.g., Lightning Mapping Arrays in North Alabama and the Washington DC Metropolitan area) are being used to develop the pre-launch algorithms and applications, and also improve our knowledge of thunderstorm initiation and evolution. Real time lightning mapping data are being provided in an experimental mode to selected National Weather Service (NWS) forecast offices in Southern and Eastern Region. This effort is designed to help improve our understanding of the application of these data in operational settings.

Development of Algorithms and Error Analyses for the Short Baseline Lightning Detection and Ranging System

NASA Technical Reports Server (NTRS)

Starr, Stanley O.

1998-01-01

NASA, at the John F. Kennedy Space Center (KSC), developed and operates a unique high-precision lightning location system to provide lightning-related weather warnings. These warnings are used to stop lightning- sensitive operations such as space vehicle launches and ground operations where equipment and personnel are at risk. The data is provided to the Range Weather Operations (45th Weather Squadron, U.S. Air Force) where it is used with other meteorological data to issue weather advisories and warnings for Cape Canaveral Air Station and KSC operations. This system, called Lightning Detection and Ranging (LDAR), provides users with a graphical display in three dimensions of 66 megahertz radio frequency events generated by lightning processes. The locations of these events provide a sound basis for the prediction of lightning hazards. This document provides the basis for the design approach and data analysis for a system of radio frequency receivers to provide azimuth and elevation data for lightning pulses detected simultaneously by the LDAR system. The intent is for this direction-finding system to correct and augment the data provided by LDAR and, thereby, increase the rate of valid data and to correct or discard any invalid data. This document develops the necessary equations and algorithms, identifies sources of systematic errors and means to correct them, and analyzes the algorithms for random error. This data analysis approach is not found in the existing literature and was developed to facilitate the operation of this Short Baseline LDAR (SBLDAR). These algorithms may also be useful for other direction-finding systems using radio pulses or ultrasonic pulse data.

Detection of Deteriorating Patients on Surgical Wards Outside the ICU by an Automated MEWS-Based Early Warning System With Paging Functionality.

PubMed

Heller, Axel R; Mees, Sören T; Lauterwald, Benjamin; Reeps, Christian; Koch, Thea; Weitz, Jürgen

2018-05-16

The establishment of early warning systems in hospitals was strongly recommended in recent guidelines to detect deteriorating patients early and direct them to adequate care. Upon reaching predefined trigger criteria, Medical Emergency Teams (MET) should be notified and directed to these patients. The present study analyses the effect of introducing an automated multiparameter early warning score (MEWS)-based early warning system with paging functionality on 2 wards hosting patients recovering from highly complex surgical interventions. The deployment of the system was accompanied by retrospective data acquisition during 12 months (intervention) using 4 routine databases: Hospital patient data management, anesthesia database, local data of the German Resuscitation Registry, and measurement logs of the deployed system (intervention period only). A retrospective 12-month data review using the same aforementioned databases before the deployment of the system served as control. Control and intervention phases were separated by a 6-month washout period for the installation of the system and for training. Data from 3827 patients could be acquired from 2 surgical wards during the two 12-month periods, 1896 patients in the control and 1931 in the intervention cohorts. Patient characteristics differed between the 2 observation phases. American Society of Anesthesiologists risk classification and duration of surgery as well as German DRG case-weight were significantly higher in the intervention period. However, the rate of cardiac arrests significantly dropped from 5.3 to 2.1 per 1000 admissions in the intervention period (P < 0.001). This observation was paralleled by a reduction of unplanned ICU admissions from 3.6% to 3.0% (P < 0.001), and an increase of notifications of critical conditions to the ward surgeon. The primary triggers for MET activation were abnormal ECG alerts, specifically asystole (n = 5), and pulseless electric activity (n = 8). In concert with a well-trained and organized MET, the early deterioration detection of patients on surgical wards outside the ICU may be improved by introducing an automated MEWS-based early warning system with paging functionality.

False alarms: How early warning signals falsely predict abrupt sea ice loss

NASA Astrophysics Data System (ADS)

Wagner, Till J. W.; Eisenman, Ian

2016-04-01

Uncovering universal early warning signals for critical transitions has become a coveted goal in diverse scientific disciplines, ranging from climate science to financial mathematics. There has been a flurry of recent research proposing such signals, with increasing autocorrelation and increasing variance being among the most widely discussed candidates. A number of studies have suggested that increasing autocorrelation alone may suffice to signal an impending transition, although some others have questioned this. Here we consider variance and autocorrelation in the context of sea ice loss in an idealized model of the global climate system. The model features no bifurcation, nor increased rate of retreat, as the ice disappears. Nonetheless, the autocorrelation of summer sea ice area is found to increase in a global warming scenario. The variance, by contrast, decreases. A simple physical mechanism is proposed to explain the occurrence of increasing autocorrelation but not variance when there is no approaching bifurcation. Additionally, a similar mechanism is shown to allow an increase in both indicators with no physically attainable bifurcation. This implies that relying on autocorrelation and variance as early warning signals can raise false alarms in the climate system, warning of "tipping points" that are not actually there.

A spatial Bayesian network model to assess the benefits of early warning for urban flood risk to people

NASA Astrophysics Data System (ADS)

Balbi, Stefano; Villa, Ferdinando; Mojtahed, Vahid; Hegetschweiler, Karin Tessa; Giupponi, Carlo

2016-06-01

This article presents a novel methodology to assess flood risk to people by integrating people's vulnerability and ability to cushion hazards through coping and adapting. The proposed approach extends traditional risk assessments beyond material damages; complements quantitative and semi-quantitative data with subjective and local knowledge, improving the use of commonly available information; and produces estimates of model uncertainty by providing probability distributions for all of its outputs. Flood risk to people is modeled using a spatially explicit Bayesian network model calibrated on expert opinion. Risk is assessed in terms of (1) likelihood of non-fatal physical injury, (2) likelihood of post-traumatic stress disorder and (3) likelihood of death. The study area covers the lower part of the Sihl valley (Switzerland) including the city of Zurich. The model is used to estimate the effect of improving an existing early warning system, taking into account the reliability, lead time and scope (i.e., coverage of people reached by the warning). Model results indicate that the potential benefits of an improved early warning in terms of avoided human impacts are particularly relevant in case of a major flood event.

Design and field test equipment of river water level detection based on ultrasonic sensor and SMS gateway as flood early warning

NASA Astrophysics Data System (ADS)

Sulistyowati, Riny; Sujono, Hari Agus; Musthofa, Ahmad Khamdi

2017-06-01

Due to the high rainfall, flood often occurs in some regions, especially in the area adjacent to the river banks that led to the idea to make the river water level detection system as a flood early warning. Several researches have produced flood detection equipment based on ultrasonic sensors and android as flood early warning system. This paper reported the results of a field test detection equipment to measure the river water level of the Bengawansolo River that was conducted in three villages in the district of Bungah, Dukun, and Manyar in Gresik regency. Tests were conducted simultaneously for 21 hours during heavy rainfall. The test results demonstrated the accuracy of the equipment of 97.28% for all categories of observation. The application of AFD (Android Flood Detection) via android smartphone demonstrated its precision in conveying the information of water level as represented by the status of SAFE, STAND, WARNING, and DANGER. Some charts presented from the analysis of data was derived from the data acquisition time of testing that can be used as an evaluation of flooding at some points prone to flood.

Potential economic value of drought information to support early warning in Africa

NASA Astrophysics Data System (ADS)

Quiroga, S.; Iglesias, A.; Diz, A.; Garrote, L.

2012-04-01

We present a methodology to estimate the economic value of advanced climate information for food production in Africa under climate change scenarios. The results aim to facilitate better choices in water resources management. The methodology includes 4 sequential steps. First two contrasting management strategies (with and without early warning) are defined. Second, the associated impacts of the management actions are estimated by calculating the effect of drought in crop productivity under climate change scenarios. Third, the optimal management option is calculated as a function of the drought information and risk aversion of potential information users. Finally we use these optimal management simulations to compute the economic value of enhanced water allocation rules to support stable food production in Africa. Our results show how a timely response to climate variations can help reduce loses in food production. The proposed framework is developed within the Dewfora project (Early warning and forecasting systems to predict climate related drought vulnerability and risk in Africa) that aims to improve the knowledge on drought forecasting, warning and mitigation, and advance the understanding of climate related vulnerability to drought and to develop a prototype operational forecasting.

Challenges for operational forecasting and early warning of rainfall induced landslides

NASA Astrophysics Data System (ADS)

Guzzetti, Fausto

2017-04-01

In many areas of the world, landslides occur every year, claiming lives and producing severe economic and environmental damage. Many of the landslides with human or economic consequences are the result of intense or prolonged rainfall. For this reason, in many areas the timely forecast of rainfall-induced landslides is of both scientific interest and social relevance. In the recent years, there has been a mounting interest and an increasing demand for operational landslide forecasting, and for associated landslide early warning systems. Despite the relevance of the problem, and the increasing interest and demand, only a few systems have been designed, and are currently operated. Inspection of the - limited - literature on operational landslide forecasting, and on the associated early warning systems, reveals that common criteria and standards for the design, the implementation, the operation, and the evaluation of the performances of the systems, are lacking. This limits the possibility to compare and to evaluate the systems critically, to identify their inherent strengths and weaknesses, and to improve the performance of the systems. Lack of common criteria and of established standards can also limit the credibility of the systems, and consequently their usefulness and potential practical impact. Landslides are very diversified phenomena, and the information and the modelling tools used to attempt landslide forecasting vary largely, depending on the type and size of the landslides, the extent of the geographical area considered, the timeframe of the forecasts, and the scope of the predictions. Consequently, systems for landslide forecasting and early warning can be designed and implemented at several different geographical scales, from the local (site or slope specific) to the regional, or even national scale. The talk focuses on regional to national scale landslide forecasting systems, and specifically on operational systems based on empirical rainfall threshold models. Building on the experience gained in designing, implementing, and operating national and regional landslide forecasting systems in Italy, and on a preliminary review of the existing literature on regional landslide early warning systems, the talk discusses concepts, limitations and challenges inherent to the design of reliable forecasting and early warning systems for rainfall-triggered landslides, the evaluation of the performances of the systems, and on problems related to the use of the forecasts and the issuing of landslide warnings. Several of the typical elements of an operational landslide forecasting system are considered, including: (i) the rainfall and landslide information used to establish the threshold models, (ii) the methods and tools used to define the empirical rainfall thresholds, and their associated uncertainty, (iii) the quality (e.g., the temporal and spatial resolution) of the rainfall information used for operational forecasting, including rain gauge and radar measurements, satellite estimates, and quantitative weather forecasts, (iv) the ancillary information used to prepare the forecasts, including e.g., the terrain subdivisions and the landslide susceptibility zonations, (v) the criteria used to transform the forecasts into landslide warnings and the methods used to communicate the warnings, and (vi) the criteria and strategies adopted to evaluate the performances of the systems, and to define minimum or optimal performance levels.

The Trend of Voluntary Warnings in Electronic Nicotine Delivery System Magazine Advertisements

PubMed Central

Shang, Ce; Chaloupka, Frank J.

2017-01-01

Some manufacturers of electronic nicotine delivery systems (ENDS) voluntarily carried health warnings in their advertisements. This study examined these voluntary warnings in magazine ads and plotted their trends between 2012 and early 2015. ENDS magazine ads were obtained through Kantar media and warnings were collected from the Chicago Public Library or the Trinkets and Trash surveillance system. The prevalence of voluntary warnings, warnings with the specific capitalized word “WARNING”, and MarkTen warnings were examined after being weighted using factors related to exposure between January 2012 and March 2015. Five brands (MarkTen, NJOY, MISTIC, and some Blu) carried warnings during the study period. The prevalence of warnings post 2012 that contained a description of nicotine did not significantly increase until the launch of MarkTen, which also happened several months before April 2014 when the U.S. food and drug administration (FDA) published its proposed deeming rule. In addition, none of these warnings met the criteria required by the FDA in the final rules. Voluntary warnings, particularly MarkTen warnings, significantly increased in ENDS magazine ads between 2014 and 2015. It is important to monitor how ENDS manufacturers will comply with the FDA regulation related to warnings and how this regulation will ultimately impact ENDS risk perceptions and use. PMID:28075420

Detecting Tsunami Source Energy and Scales from GNSS & Laboratory Experiments

NASA Astrophysics Data System (ADS)

Song, Y. T.; Yim, S. C.; Mohtat, A.

2016-12-01

Historically, tsunami warnings based on the earthquake magnitude have not been very accurate. According to the 2006 U.S. Government Accountability Office report, an unacceptable 75% false alarm rate has prevailed in the Pacific Ocean (GAO-06-519). One of the main reasons for those inaccurate warnings is that an earthquake's magnitude is not the scale or power of the resulting tsunami. For the last 10 years, we have been developing both theories and algorithms to detect tsunami source energy and scales, instead of earthquake magnitudes per se, directly from real-time Global Navigation Satellite System (GNSS) stations along coastlines for early warnings [Song 2007; Song et al., 2008; Song et al., 2012; Xu and Song 2013; Titov et al, 2016]. Here we will report recent progress on two fronts: 1) Examples of using GNSS in detecting the tsunami energy scales for the 2004 Sumatra M9.1 earthquake, the 2005 Nias M8.7 earthquake, the 2010 M8.8 Chilean earthquake, the 2011 M9.0 Tohoku-Oki earthquake, and the 2015 M8.3 Illapel earthquake. 2) New results from recent state-of-the-art wave-maker experiments and comparisons with GNSS data will also be presented. Related reference: Titov, V., Y. T. Song, L. Tang, E. N. Bernard, Y. Bar-Sever, and Y. Wei (2016), Consistent estimates of tsunami energy show promise for improved early warning, Pur Appl. Geophs., DOI: 10.1007/s00024-016-1312-1. Xu, Z. and Y. T. Song (2013), Combining the all-source Green's functions and the GPS-derived source for fast tsunami prediction - illustrated by the March 2011 Japan tsunami, J. Atmos. Oceanic Tech., jtechD1200201. Song, Y. T., I. Fukumori, C. K. Shum, and Y. Yi (2012), Merging tsunamis of the 2011 Tohoku-Oki earthquake detected over the open ocean, Geophys. Res. Lett., doi:10.1029/2011GL050767. Song, Y. T., L.-L. Fu, V. Zlotnicki, C. Ji, V. Hjorleifsdottir, C.K. Shum, and Y. Yi, 2008: The role of horizontal impulses of the faulting continental slope in generating the 26 December 2004 Tsunami (2007), Ocean Modelling, doi:10.1016/j.ocemod.2007.10.007. Song, Y. T. (2007) Detecting tsunami genesis and scales directly from coastal GPS stations, Geophys. Res. Lett., 34, L19602, doi:10.1029/2007GL031681.

Flood Monitoring and Early Warning System Using Ultrasonic Sensor

NASA Astrophysics Data System (ADS)

Natividad, J. G.; Mendez, J. M.

2018-03-01

The purpose of this study is to develop a real-time flood monitoring and early warning system in the northern portion of the province of Isabela, particularly the municipalities near Cagayan River. Ultrasonic sensing techniques have become mature and are widely used in the various fields of engineering and basic science. One of advantage of ultrasonic sensing is its outstanding capability to probe inside objective non-destructively because ultrasound can propagate through any kinds of media including solids, liquids and gases. This study focuses only on the water level detection and early warning system (via website and/or SMS) that alerts concern agencies and individuals for a potential flood event. Furthermore, inquiry system is also included in this study to become more interactive wherein individuals in the community could inquire the actual water level and status of the desired area or location affected by flood thru SMS keyword. The study aims in helping citizens to be prepared and knowledgeable whenever there is a flood. The novelty of this work falls under the utilization of the Arduino, ultrasonic sensors, GSM module, web-monitoring and SMS early warning system in helping stakeholders to mitigate casualties related to flood. The paper envisions helping flood-prone areas which are common in the Philippines particularly to the local communities in the province. Indeed, it is relevant and important as per needs for safety and welfare of the community.

Assessing earthquake early warning using sparse networks in developing countries: Case study of the Kyrgyz Republic

NASA Astrophysics Data System (ADS)

Parolai, Stefano; Boxberger, Tobias; Pilz, Marco; Fleming, Kevin; Haas, Michael; Pittore, Massimiliano; Petrovic, Bojana; Moldobekov, Bolot; Zubovich, Alexander; Lauterjung, Joern

2017-09-01

The first real-time digital strong-motion network in Central Asia has been installed in the Kyrgyz Republic since 2014. Although this network consists of only 19 strong-motion stations, they are located in near-optimal locations for earthquake early warning and rapid response purposes. In fact, it is expected that this network, which utilizes the GFZ-Sentry software, allowing decentralized event assessment calculations, not only will provide useful strong motion data useful for improving future seismic hazard and risk assessment, but will serve as the backbone for regional and on-site earthquake early warning operations. Based on the location of these stations, and travel-time estimates for P- and S-waves, we have determined potential lead times for several major urban areas in Kyrgyzstan (i.e., Bishkek, Osh, and Karakol) and Kazakhstan (Almaty), where we find the implementation of an efficient earthquake early warning system would provide lead times outside the blind zone ranging from several seconds up to several tens of seconds. This was confirmed by the simulation of the possible shaking (and intensity) that would arise considering a series of scenarios based on historical and expected events, and how they affect the major urban centres. Such lead times would allow the instigation of automatic mitigation procedures, while the system as a whole would support prompt and efficient actions to be undertaken over large areas.

Use of cyanopigment determination as an indicator of cyanotoxins in drinking water.

PubMed

Schmidt, Wido; Petzoldt, Heike; Bornmann, Katrin; Imhof, Lutz; Moldaenke, Christian

2009-01-01

The indicator function of the fluorescence signals of the cyanopigments phycocyanin and phycoerythrin as early warning parameters against the microcystins in drinking water was investigated by lab- and pilot-scale studies. The early warning function of the fluorescence signals was examined with regard to the signals' real-time character, their sensitivity and the behaviour of the cyanopigments in different treatment stages in comparison to microcystins. Fluorescence measurements confirmed the real-time character, since they can be carried out on-site without the pre-concentration of pigments. The limit of detection of phycoerythrin is determined at 0.7 microg/L and of phycocyanin at 5.3 microg/L respectively. If the pigment/microcystin ratio is known and calculated to be higher than 1, very low microcystin concentrations can be estimated by the fluorescence signals. The compared behaviour of both pigments and selected microcystins (MC-LR and MC-RR) during water treatment shows that pigments have an early warning function against microcystins in conventional treatment stages using pre-oxidation with permanganate, powdered-activated carbon and chlorination. In contrast, cyanopigments do not have an early warning function if chlorine dioxide is used as a pre-oxidant or final disinfection agent. In order to use pigment control measurements in drinking water treatment the initial pigment/toxin ratio of the raw water must be known.

Assessment of a drowsy driver warning system for heavy-vehicle drivers : final report

DOT National Transportation Integrated Search

2009-04-01

Drowsiness has a globally negative impact on performance, slowing reaction time, decreasing situational awareness, and impairing judgment. A field operational test of an early prototype Drowsy Driver Warning System was conducted as a result of 12 yea...

Role of MODIS Vegetation Phenology Products in the U.S. for Warn Early Warning System for Forest Threats

NASA Technical Reports Server (NTRS)

Spruce, Joseph; Hargrove, William; Norman, Steve; Gasser, Gerald; Smoot, James; Kuper, Philip

2012-01-01

U.S. forests occupy approx 751 million acres (approx 1/3 of total land). Several abiotic and biotic damage agents disturb, damage, kill, and/or threaten these forests. Regionally extensive forest disturbances can also threaten human life and property, bio-diversity and water supplies. timely regional forest disturbance monitoring products are needed to aid forest health management work at finer scales. daily MODIS data provide a means to monitor regional forest disturbances on a weekly basis, leveraging vegetation phenology. In response, the USFS and NASA began collaborating in 2006 to develop a Near Real Time (NRT) forest monitoring capability, based on MODIS NDVI data, as part of a national forest threat Early Warning System (EWS).

The GNSS-based component for the new Indonesian tsunami early warning centre provided by GITEWS

NASA Astrophysics Data System (ADS)

Falck, C.; Ramatschi, M.; Bartsch, M.; Merx, A.; Hoeberechts, J.; Rothacher, M.

2009-04-01

Introduction Nowadays GNSS technologies are used for a large variety of precise positioning applications. The accuracy can reach the mm level depending on the data analysis methods. GNSS technologies thus offer a high potential to support tsunami early warning systems, e.g., by detection of ground motions due to earthquakes and of tsunami waves on the ocean by GNSS instruments on a buoy. Although GNSS-based precise positioning is a standard method, it is not yet common to apply this technique under tight time constraints and, hence, in the absence of precise satellite orbits and clocks. The new developed GNSS-based component utilises on- and offshore measured GNSS data and is the first system of its kind that was integrated into an operational early warning system. (Indonesian Tsunami Early Warning Centre INATEWS, inaugurated at BMKG, Jakarta on November, 11th 2008) Motivation After the Tsunami event of 26th December 2004 the German government initiated the GITEWS project (German Indonesian Tsunami Early Warning System) to develop a tsunami early warning system for Indonesia. The GFZ Potsdam (German Research Centre for Geosciences) as the consortial leader of GITEWS also covers several work packages, most of them related to sensor systems. The geodetic branch (Department 1) of the GFZ was assigned to develop a GNSS-based component. Brief system description The system covers all aspects from sensor stations with new developed hard- and software designs, manufacturing and installation of stations, real-time data transfer issues, a new developed automatic near real-time data processing and a graphical user interface for early warning centre operators including training on the system. GNSS sensors are installed on buoys, at tide gauges and as real-time reference stations (RTR stations), either stand-alone or co-located with seismic sensors. The GNSS data are transmitted to the warning centre where they are processed in a near real-time data processing chain. For sensors on land the processing system delivers deviations from their normal, mean coordinates. The deviations or so called displacements are indicators for land mass movements which can occur, e.g., due to strong earthquakes. The ground motion information is a valuable source for a fast understanding of an earthquake's mechanism with possible relevance for a potentially following tsunami. By this means the GNSS system supports the decision finding process whether most probably a tsunami has been generated or not. For buoy based GNSS data the processing (differential, with GNSS reference station on land) delivers coordinates as well. Only the vertical component is of interest as it corresponds to the instant sea level height. Deviations to the mean sea level height are an indicator for a possibly passing tsunami wave. The graphical user interface (GUI) of the GNSS system supports both, a quick view for all staff members at the warning centre (24h/7d shifts) and deeper analysis by GNSS experts. The GNSS GUI system is implemented as a web-based application and allows all views to be displayed on different screens at the same time, even at remote locations. This is part of the concept, as it can support the dialogue between warning centre staff on duty or on standby and sensor station maintenance staff. Acknowledgements The GITEWS project (German Indonesian Tsunami Early Warning System) is carried out by a large group of scientists and engineers from (GFZ) German Research Centre for Geosciences and its partners from the German Aerospace Centre (DLR), the Alfred Wegener Institute for Polar and Marine Research (AWI), the GKSS Research Centre, the Konsortium Deutsche Meeresforschung (KDM), the Leibniz Institute for Marine Sciences (IFM-GEOMAR), the United Nations University (UNU), the Federal Institute for Geosciences and Natural Resources (BGR), the German Agency for Technical Cooperation (GTZ) and other international partners. Most relevant partners in Indonesia with respect to the GNSS component of GITEWS are the National Coordinating Agency for Surveys and Mapping (BAKOSURTANAL), the National Metereology and Geophysics Agency (BMKG) and the National Agency for the Assessment and Application of Technology (BPPT). Funding is provided by the German Federal Ministry for Education and Research (BMBF), Grant 03TSU01.

[An early warning method of cucumber downy mildew in solar greenhouse based on canopy temperature and humidity modeling].

PubMed

Wang, Hui; Li, Mei-lan; Xu, Jian-ping; Chen, Mei-xiang; Li, Wen-yong; Li, Ming

2015-10-01

The greenhouse environmental parameters can be used to establish greenhouse nirco-climate model, which can combine with disease model for early warning, with aim of ecological controlling diseases to reduce pesticide usage, and protecting greenhouse ecological environment to ensure the agricultural product quality safety. Greenhouse canopy leaf temperature and air relative humidity, models were established using energy balance and moisture balance principle inside the greenhouse. The leaf temperature model considered radiation heat transfer between the greenhouse crops, wall, soil and cover, plus the heat exchange caused by indoor net radiation and crop transpiration. Furthermore, the water dynamic balance in the greenhouse including leaf transpiration, soil evaporation, cover and leaf water vapor condensation, was considered to develop a relative humidity model. The primary infection and latent period warning models for cucumber downy mildew (Pseudoperonospora cubensis) were validated using the results of the leaf temperature and relative humidity model, and then the estimated disease occurrence date of cucumber downy mildew was compared with actual disease occurrence date of field observation. Finally, the results were verified by the measured temperature and humidity data of September and October, 2014. The results showed that the root mean square deviations (RMSDs) of the measured and estimated leaf temperature were 0.016 and 0.024 °C, and the RMSDs of the measured and estimated air relative humidity were 0.15% and 0.13%, respectively. Combining the result of estimated temperature and humidity models, a cucumber disease early warning system was established to forecast the date of disease occurrence, which met with the real date. Thus, this work could provide the micro-environment data for the early warning system of cucumber diseases in solar greenhouses.

Development of a Low Cost Earthquake Early Warning System in Taiwan

NASA Astrophysics Data System (ADS)

Wu, Y. M.

2017-12-01

The National Taiwan University (NTU) developed an earthquake early warning (EEW) system for research purposes using low-cost accelerometers (P-Alert) since 2010. As of 2017, a total of 650 stations have been deployed and configured. The NTU system can provide earthquake information within 15 s of an earthquake occurrence. Thus, this system may provide early warnings for cities located more than 50 km from the epicenter. Additionally, the NTU system also has an onsite alert function that triggers a warning for incoming P-waves greater than a certain magnitude threshold, thus providing a 2-3 s lead time before peak ground acceleration (PGA) for regions close to an epicenter. Detailed shaking maps are produced by the NTU system within one or two minutes after an earthquake. Recently, a new module named ShakeAlarm has been developed. Equipped with real-time acceleration signals and the time-dependent anisotropic attenuation relationship of the PGA, ShakingAlarm can provide an accurate PGA estimation immediately before the arrival of the observed PGA. This unique advantage produces sufficient lead time for hazard assessment and emergency response, which is unavailable for traditional shakemap, which are based on only the PGA observed in real time. The performance of ShakingAlarm was tested with six M > 5.5 inland earthquakes from 2013 to 2016. Taking the 2016 M6.4 Meinong earthquake simulation as an example, the predicted PGA converges to a stable value and produces a predicted shake map and an isocontour map of the predicted PGA within 16 seconds of earthquake occurrence. Compared with traditional regional EEW system, ShakingAlarm can effectively identify possible damage regions and provide valuable early warning information (magnitude and PGA) for risk mitigation.

A spatiotemporal dengue fever early warning model accounting for nonlinear associations with meteorological factors: a Bayesian maximum entropy approach

NASA Astrophysics Data System (ADS)

Lee, Chieh-Han; Yu, Hwa-Lung; Chien, Lung-Chang

2014-05-01

Dengue fever has been identified as one of the most widespread vector-borne diseases in tropical and sub-tropical. In the last decade, dengue is an emerging infectious disease epidemic in Taiwan especially in the southern area where have annually high incidences. For the purpose of disease prevention and control, an early warning system is urgently needed. Previous studies have showed significant relationships between climate variables, in particular, rainfall and temperature, and the temporal epidemic patterns of dengue cases. However, the transmission of the dengue fever is a complex interactive process that mostly understated the composite space-time effects of dengue fever. This study proposes developing a one-week ahead warning system of dengue fever epidemics in the southern Taiwan that considered nonlinear associations between weekly dengue cases and meteorological factors across space and time. The early warning system based on an integration of distributed lag nonlinear model (DLNM) and stochastic Bayesian Maximum Entropy (BME) analysis. The study identified the most significant meteorological measures including weekly minimum temperature and maximum 24-hour rainfall with continuous 15-week lagged time to dengue cases variation under condition of uncertainty. Subsequently, the combination of nonlinear lagged effects of climate variables and space-time dependence function is implemented via a Bayesian framework to predict dengue fever occurrences in the southern Taiwan during 2012. The result shows the early warning system is useful for providing potential outbreak spatio-temporal prediction of dengue fever distribution. In conclusion, the proposed approach can provide a practical disease control tool for environmental regulators seeking more effective strategies for dengue fever prevention.

Experiences integrating autonomous components and legacy systems into tsunami early warning systems

NASA Astrophysics Data System (ADS)

Reißland, S.; Herrnkind, S.; Guenther, M.; Babeyko, A.; Comoglu, M.; Hammitzsch, M.

2012-04-01

Fostered by and embedded in the general development of Information and Communication Technology (ICT) the evolution of Tsunami Early Warning Systems (TEWS) shows a significant development from seismic-centred to multi-sensor system architectures using additional sensors, e.g. sea level stations for the detection of tsunami waves and GPS stations for the detection of ground displacements. Furthermore, the design and implementation of a robust and scalable service infrastructure supporting the integration and utilisation of existing resources serving near real-time data not only includes sensors but also other components and systems offering services such as the delivery of feasible simulations used for forecasting in an imminent tsunami threat. In the context of the development of the German Indonesian Tsunami Early Warning System (GITEWS) and the project Distant Early Warning System (DEWS) a service platform for both sensor integration and warning dissemination has been newly developed and demonstrated. In particular, standards of the Open Geospatial Consortium (OGC) and the Organization for the Advancement of Structured Information Standards (OASIS) have been successfully incorporated. In the project Collaborative, Complex, and Critical Decision-Support in Evolving Crises (TRIDEC) new developments are used to extend the existing platform to realise a component-based technology framework for building distributed TEWS. This talk will describe experiences made in GITEWS, DEWS and TRIDEC while integrating legacy stand-alone systems and newly developed special-purpose software components into TEWS using different software adapters and communication strategies to make the systems work together in a corporate infrastructure. The talk will also cover task management and data conversion between the different systems. Practical approaches and software solutions for the integration of sensors, e.g. providing seismic and sea level data, and utilisation of special-purpose components, such as simulation systems, in TEWS will be presented.

Vital signs and other observations used to detect deterioration in pregnant women: an analysis of vital sign charts in consultant-led UK maternity units.

PubMed

Smith, G B; Isaacs, R; Andrews, L; Wee, M Y K; van Teijlingen, E; Bick, D E; Hundley, V

2017-05-01

Obstetric early warning systems are recommended for monitoring hospitalised pregnant and postnatal women. We decided to compare: (i) vital sign values used to define physiological normality; (ii) symptoms and signs used to escalate care; (iii) type of chart used; and (iv) presence of explicit instructions for escalating care. One-hundred-and-twenty obstetric early warning charts and escalation protocols were obtained from consultant-led maternity units in the UK and Channel Islands. These data were extracted: values used to determine normality for each maternal vital sign; chart colour-coding; instructions following early warning system triggering; other criteria used as triggers. There was considerable variation in the charts, warning systems and escalation protocols. Of 120 charts, 89.2% used colour; 69.2% used colour-coded escalation systems. Forty-one (34.2%) systems required the calculation of weighted scores. Seventy-five discrete combinations of 'normal' vital sign ranges were found, the most common being: heart rate=50-99beats/min; respiratory rate=11-20breaths/min; blood pressure, systolic=100-149mmHg, diastolic ≤89mmHg; SpO 2 =95-100%; temperature=36.0-37.9°C; and Alert-Voice-Pain-Unresponsive assessment=Alert. Most charts (90.8%) provided instructions about who to contact following triggering, but only 41.7% gave instructions about subsequent observation frequency. The wide range of 'normal' vital sign values in different systems suggests a lack of equity in the processes for detecting deterioration and escalating care in hospitalised pregnant and postnatal women. Agreement regarding 'normal' vital sign ranges is urgently required and would assist the development of a standardised obstetric early warning system and chart. Copyright © 2017 Elsevier Ltd. All rights reserved.

101. View of transmitter building no. 102, missile warning operation ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

101. View of transmitter building no. 102, missile warning operation center, close up view of DRED (detection radar environmental display) console in operation showing target. Official photograph BMEWS Project by Hansen, 14 March 1963, clear as negative no. A-8803. - Clear Air Force Station, Ballistic Missile Early Warning System Site II, One mile west of mile marker 293.5 on Parks Highway, 5 miles southwest of Anderson, Anderson, Denali Borough, AK

Implementation of a Global Navigation Satellite System (GNSS) Augmentation to Tsunami Early Warning Systems

NASA Astrophysics Data System (ADS)

LaBrecque, John

2016-04-01

The Global Geodetic Observing System has issued a Call for Participation to research scientists, geodetic research groups and national agencies in support of the implementation of the IUGG recommendation for a Global Navigation Satellite System (GNSS) Augmentation to Tsunami Early Warning Systems. The call seeks to establish a working group to be a catalyst and motivating force for the definition of requirements, identification of resources, and for the encouragement of international cooperation in the establishment, advancement, and utilization of GNSS for Tsunami Early Warning. During the past fifteen years the populations of the Indo-Pacific region experienced a series of mega-thrust earthquakes followed by devastating tsunamis that claimed nearly 300,000 lives. The future resiliency of the region will depend upon improvements to infrastructure and emergency response that will require very significant investments from the Indo-Pacific economies. The estimation of earthquake moment magnitude, source mechanism and the distribution of crustal deformation are critical to rapid tsunami warning. Geodetic research groups have demonstrated the use of GNSS data to estimate earthquake moment magnitude, source mechanism and the distribution of crustal deformation sufficient for the accurate and timely prediction of tsunamis generated by mega-thrust earthquakes. GNSS data have also been used to measure the formation and propagation of tsunamis via ionospheric disturbances acoustically coupled to the propagating surface waves; thereby providing a new technique to track tsunami propagation across ocean basins, opening the way for improving tsunami propagation models, and providing accurate warning to communities in the far field. These two new advancements can deliver timely and accurate tsunami warnings to coastal communities in the near and far field of mega-thrust earthquakes. This presentation will present the justification for and the details of the GGOS Call for Participation.

Time difference of arrival to blast localization of potential chemical/biological event on the move

NASA Astrophysics Data System (ADS)

Morcos, Amir; Desai, Sachi; Peltzer, Brian; Hohil, Myron E.

2007-10-01

Integrating a sensor suite with ability to discriminate potential Chemical/Biological (CB) events from high-explosive (HE) events employing a standalone acoustic sensor with a Time Difference of Arrival (TDOA) algorithm we developed a cueing mechanism for more power intensive and range limited sensing techniques. Enabling the event detection algorithm to locate to a blast event using TDOA we then provide further information of the event as either Launch/Impact and if CB/HE. The added information is provided to a range limited chemical sensing system that exploits spectroscopy to determine the contents of the chemical event. The main innovation within this sensor suite is the system will provide this information on the move while the chemical sensor will have adequate time to determine the contents of the event from a safe stand-off distance. The CB/HE discrimination algorithm exploits acoustic sensors to provide early detection and identification of CB attacks. Distinct characteristics arise within the different airburst signatures because HE warheads emphasize concussive and shrapnel effects, while CB warheads are designed to disperse their contents over large areas, therefore employing a slower burning, less intense explosive to mix and spread their contents. Differences characterized by variations in the corresponding peak pressure and rise time of the blast, differences in the ratio of positive pressure amplitude to the negative amplitude, and variations in the overall duration of the resulting waveform. The discrete wavelet transform (DWT) is used to extract the predominant components of these characteristics from air burst signatures at ranges exceeding 3km. Highly reliable discrimination is achieved with a feed-forward neural network classifier trained on a feature space derived from the distribution of wavelet coefficients and higher frequency details found within different levels of the multiresolution decomposition. The development of an adaptive noise floor to provide early event detection assists in minimizing the false alarm rate and increasing the confidence whether the event is blast event or back ground noise. The integration of these algorithms with the TDOA algorithm provides a complex suite of algorithms that can give early warning detection and highly reliable look direction from a great stand-off distance for a moving vehicle to determine if a candidate blast event is CB and if CB what is the composition of the resulting cloud.

Performance of Early Warning Systems on Landslides in Central America

NASA Astrophysics Data System (ADS)

Strauch, W.; Devoli, G.

2012-04-01

We performed a reconnaissance about Early Warning Systems (EWS) on Landslides (EWSL) in the countries of Central America. The advance of the EWSL began in the 1990-ies and accelerated dramatically after the regional disaster provoked by Hurricane Mitch in 1998. In the last decade, Early Warning Systems were intensely promoted by national and international development programs aimed on disaster prevention. Early Warning on landslides is more complicated than for other geological phenomena. But, we found information on more than 30 EWSL in the region. In practice, for example in planning, implementation and evaluation of development projects, it is often not clearly defined what exactly is an Early Warning System. Only few of the systems can be classified as true EWSL that means 1) being directly and solely aimed at persons living in the well-defined areas of greatest risk and 2) focusing their work on saving lives before the phenomenon impacts. There is little written information about the work of the EWSL after the initial phase. Even, there are no statistics whether they issued warnings, if the warnings were successful, how many people were evacuated, if there were few false alerts, etc.. Actually, we did not find a single report on a successful landslide warning issued by an EWSL. The lack of information is often due to the fact that communitarian EWSL are considered local structures and do not have a clearly defined position in the governmental hierarchy; there is little oversight and no qualified support and long-term support. The EWSL suffer from severe problems as lack of funding on the long term, low technical level, and insufficient support from central institutions. Often the EWSL are implemented by NGÓs with funding from international agencies, but leave the project alone after the initial phase. In many cases, the hope of the local people to get some protection against the landslide hazard is not really fulfilled. There is one case, where an EWSL with a good technical base was installed in 2001 in an area with risk of lahars. The system was too complicated to be managed by the municipality or there was not sufficient training, and soon the system stopped working. In 2009, lahars were triggered by extreme rains and around 100 people died in the area previously covered by this EWSL. We discuss the reasons for the poor performance of the projects developing EWSL in Central America and present proposals to make the more efficient and sustainable. This work was carried out in the frame of a project of UNESCO (Office San José, Costa Rica) in association with CEPREDENAC-SICA within the 7-th Plan of ECHO for Central America.

Smokers' and E-Cigarette Users' Perceptions about E-Cigarette Warning Statements.

PubMed

Wackowski, Olivia A; Hammond, David; O'Connor, Richard J; Strasser, Andrew A; Delnevo, Cristine D

2016-06-30

Cigarette warning labels are important sources of risk information, but warning research for other tobacco products is limited. This study aimed to gauge perceptions about warnings that may be used for e-cigarettes. We conducted six small focus groups in late 2014/early 2015 with adult current e-cigarette users and cigarette-only smokers. Participants rated and discussed their perceptions of six e-cigarette warning statements, and warnings in two existing Vuse and MarkTen e-cigarette ads. Participants were open to e-cigarette warnings and provided the strongest reactions to statements warning that e-liquid/e-vapor or e-cigarettes can be poisonous, contain toxins, or are "not a safe alternative to smoking". However, many also noted that these statements were exaggerated, potentially misleading, and could scare smokers away from reducing their harm by switching to e-cigarettes. Opinions on the Food and Drug Administration's proposed nicotine addiction warning and warnings that e-cigarettes had not been approved for smoking cessation or had unknown health effects were mixed. Participants perceived MarkTen's advertisement warning to be stronger and more noticeable than Vuse's. Care should be taken in developing e-cigarette warnings given their relative recentness and potential for harm reduction compared to other tobacco products. Additional research, including with varied audiences, would be instructive.

Development of web-based services for an ensemble flood forecasting and risk assessment system

NASA Astrophysics Data System (ADS)

Yaw Manful, Desmond; He, Yi; Cloke, Hannah; Pappenberger, Florian; Li, Zhijia; Wetterhall, Fredrik; Huang, Yingchun; Hu, Yuzhong

2010-05-01

Flooding is a wide spread and devastating natural disaster worldwide. Floods that took place in the last decade in China were ranked the worst amongst recorded floods worldwide in terms of the number of human fatalities and economic losses (Munich Re-Insurance). Rapid economic development and population expansion into low lying flood plains has worsened the situation. Current conventional flood prediction systems in China are neither suited to the perceptible climate variability nor the rapid pace of urbanization sweeping the country. Flood prediction, from short-term (a few hours) to medium-term (a few days), needs to be revisited and adapted to changing socio-economic and hydro-climatic realities. The latest technology requires implementation of multiple numerical weather prediction systems. The availability of twelve global ensemble weather prediction systems through the ‘THORPEX Interactive Grand Global Ensemble' (TIGGE) offers a good opportunity for an effective state-of-the-art early forecasting system. A prototype of a Novel Flood Early Warning System (NEWS) using the TIGGE database is tested in the Huai River basin in east-central China. It is the first early flood warning system in China that uses the massive TIGGE database cascaded with river catchment models, the Xinanjiang hydrologic model and a 1-D hydraulic model, to predict river discharge and flood inundation. The NEWS algorithm is also designed to provide web-based services to a broad spectrum of end-users. The latter presents challenges as both databases and proprietary codes reside in different locations and converge at dissimilar times. NEWS will thus make use of a ready-to-run grid system that makes distributed computing and data resources available in a seamless and secure way. An ability to run or function on different operating systems and provide an interface or front that is accessible to broad spectrum of end-users is additional requirement. The aim is to achieve robust interoperability through strong security and workflow capabilities. A physical network diagram and a work flow scheme of all the models, codes and databases used to achieve the NEWS algorithm are presented. They constitute a first step in the development of a platform for providing real time flood forecasting services on the web to mitigate 21st century weather phenomena.

No evidence of critical slowing down in two endangered Hawaiian honeycreepers

PubMed Central

Camp, Richard J.; Reed, J. Michael

2017-01-01

There is debate about the current population trends and predicted short-term fates of the endangered forest birds, Hawai`i Creeper (Loxops mana) and Hawai`i `Ākepa (L. coccineus). Using long-term population size estimates, some studies report forest bird populations as stable or increasing, while other studies report signs of population decline or impending extinction associated with introduced Japanese White-eye (Zosterops japonicus) increase. Reliable predictors of impending population collapse, well before the collapse begins, have been reported in simulations and microcosm experiments. In these studies, statistical indicators of critical slowing down, a phenomenon characterized by longer recovery rates after population size perturbation, are reported to be early warning signals of an impending regime shift observable prior to the tipping point. While the conservation applications of these metrics are commonly discussed, early warning signal detection methods are rarely applied to population size data from natural populations, so their efficacy and utility in species management remain unclear. We evaluated two time series of state-space abundance estimates (1987–2012) from Hakalau Forest National Wildlife Refuge, Hawai`i to test for evidence of early warning signals of impending population collapse for the Hawai`i Creeper and Hawai`i `Ākepa. We looked for signals throughout the time series, and prior to 2000, when white-eye abundance began increasing. We found no evidence for either species of increasing variance, autocorrelation, or skewness, which are commonly reported early warning signals. We calculated linear rather than ordinary skewness because the latter is biased, particularly for small sample sizes. Furthermore, we identified break-points in trends over time for both endangered species, indicating shifts in slopes away from strongly increasing trends, but they were only weakly supported by Bayesian change-point analyses (i.e., no step-wise changes in abundance). The break-point and change-point test results, in addition to the early warning signal analyses, support that the two populations do not appear to show signs of critical slowing down or decline. PMID:29131835

An online operational rainfall-monitoring resource for epidemic malaria early warning systems in Africa

USGS Publications Warehouse

Grover-Kopec, Emily; Kawano, Mika; Klaver, Robert W.; Blumenthal, Benno; Ceccato, Pietro; Connor, Stephen J.

2005-01-01

Periodic epidemics of malaria are a major public health problem for many sub-Saharan African countries. Populations in epidemic prone areas have a poorly developed immunity to malaria and the disease remains life threatening to all age groups. The impact of epidemics could be minimized by prediction and improved prevention through timely vector control and deployment of appropriate drugs. Malaria Early Warning Systems are advocated as a means of improving the opportunity for preparedness and timely response.Rainfall is one of the major factors triggering epidemics in warm semi-arid and desert-fringe areas. Explosive epidemics often occur in these regions after excessive rains and, where these follow periods of drought and poor food security, can be especially severe. Consequently, rainfall monitoring forms one of the essential elements for the development of integrated Malaria Early Warning Systems for sub-Saharan Africa, as outlined by the World Health Organization.The Roll Back Malaria Technical Resource Network on Prevention and Control of Epidemics recommended that a simple indicator of changes in epidemic risk in regions of marginal transmission, consisting primarily of rainfall anomaly maps, could provide immediate benefit to early warning efforts. In response to these recommendations, the Famine Early Warning Systems Network produced maps that combine information about dekadal rainfall anomalies, and epidemic malaria risk, available via their Africa Data Dissemination Service. These maps were later made available in a format that is directly compatible with HealthMapper, the mapping and surveillance software developed by the WHO's Communicable Disease Surveillance and Response Department. A new monitoring interface has recently been developed at the International Research Institute for Climate Prediction (IRI) that enables the user to gain a more contextual perspective of the current rainfall estimates by comparing them to previous seasons and climatological averages. These resources are available at no cost to the user and are updated on a routine basis.

Monitoring of unstable slopes by MEMS tilting sensors and its application to early warning

NASA Astrophysics Data System (ADS)

Towhata, I.; Uchimura, T.; Seko, I.; Wang, L.

2015-09-01

The present paper addresses the newly developed early warning technology that can help mitigate the slope failure disasters during heavy rains. Many studies have been carried out in the recent times on early warning that is based on rainfall records. Although those rainfall criteria of slope failure tells the probability of disaster on a regional scale, it is difficult for them to judge the risk of particular slopes. This is because the rainfall intensity is spatially too variable to forecast and the early warning based on rainfall alone cannot take into account the effects of local geology, hydrology and topography that vary spatially as well. In this regard, the authors developed an alternative technology in which the slope displacement/deformation is monitored and early warning is issued when a new criterion is satisfied. The new MEMS-based sensor monitors the tilting angle of an instrument that is embedded at a very shallow depth and the record of the tilting angle corresponds to the lateral displacement at the slope surface. Thus, the rate of tilting angle that exceeds a new criterion value implies an imminent slope failure. This technology has been validated against several events of slope failures as well as against a field rainfall test. Those validations have made it possible to determine the criterion value of the rate of tilting angle to be 0.1 degree/hour. The advantage of the MEMS tilting sensor lies in its low cost. Hence, it is possible to install many low-cost sensors over a suspected slope in which the precise range of what is going to fall down during the next rainfall is unknown. In addition to the past validations, this paper also introduces a recent application to a failed slope in the Izu Oshima Island where a heavy rainfall-induced slope failure occurred in October, 2013.

No evidence of critical slowing down in two endangered Hawaiian honeycreepers

USGS Publications Warehouse

Rozek, Jessica C.; Camp, Richard J.; Reed, J. Michael

2017-01-01

There is debate about the current population trends and predicted short-term fates of the endangered forest birds, Hawai`i Creeper (Loxops mana) and Hawai`i `Ākepa (L. coccineus). Using long-term population size estimates, some studies report forest bird populations as stable or increasing, while other studies report signs of population decline or impending extinction associated with introduced Japanese White-eye (Zosterops japonicus) increase. Reliable predictors of impending population collapse, well before the collapse begins, have been reported in simulations and microcosm experiments. In these studies, statistical indicators of critical slowing down, a phenomenon characterized by longer recovery rates after population size perturbation, are reported to be early warning signals of an impending regime shift observable prior to the tipping point. While the conservation applications of these metrics are commonly discussed, early warning signal detection methods are rarely applied to population size data from natural populations, so their efficacy and utility in species management remain unclear. We evaluated two time series of state-space abundance estimates (1987–2012) from Hakalau Forest National Wildlife Refuge, Hawai`i to test for evidence of early warning signals of impending population collapse for the Hawai`i Creeper and Hawai`i `Ākepa. We looked for signals throughout the time series, and prior to 2000, when white-eye abundance began increasing. We found no evidence for either species of increasing variance, autocorrelation, or skewness, which are commonly reported early warning signals. We calculated linear rather than ordinary skewness because the latter is biased, particularly for small sample sizes. Furthermore, we identified break-points in trends over time for both endangered species, indicating shifts in slopes away from strongly increasing trends, but they were only weakly supported by Bayesian change-point analyses (i.e., no step-wise changes in abundance). The break-point and change-point test results, in addition to the early warning signal analyses, support that the two populations do not appear to show signs of critical slowing down or decline.

Rainfall threshold calculation for debris flow early warning in areas with scarcity of data

NASA Astrophysics Data System (ADS)

Pan, Hua-Li; Jiang, Yuan-Jun; Wang, Jun; Ou, Guo-Qiang

2018-05-01

Debris flows are natural disasters that frequently occur in mountainous areas, usually accompanied by serious loss of lives and properties. One of the most commonly used approaches to mitigate the risk associated with debris flows is the implementation of early warning systems based on well-calibrated rainfall thresholds. However, many mountainous areas have little data regarding rainfall and hazards, especially in debris-flow-forming regions. Therefore, the traditional statistical analysis method that determines the empirical relationship between rainstorms and debris flow events cannot be effectively used to calculate reliable rainfall thresholds in these areas. After the severe Wenchuan earthquake, there were plenty of deposits deposited in the gullies, which resulted in several debris flow events. The triggering rainfall threshold has decreased obviously. To get a reliable and accurate rainfall threshold and improve the accuracy of debris flow early warning, this paper developed a quantitative method, which is suitable for debris flow triggering mechanisms in meizoseismal areas, to identify rainfall threshold for debris flow early warning in areas with a scarcity of data based on the initiation mechanism of hydraulic-driven debris flow. First, we studied the characteristics of the study area, including meteorology, hydrology, topography and physical characteristics of the loose solid materials. Then, the rainfall threshold was calculated by the initiation mechanism of the hydraulic debris flow. The comparison with other models and with alternate configurations demonstrates that the proposed rainfall threshold curve is a function of the antecedent precipitation index (API) and 1 h rainfall. To test the proposed method, we selected the Guojuanyan gully, a typical debris flow valley that during the 2008-2013 period experienced several debris flow events, located in the meizoseismal areas of the Wenchuan earthquake, as a case study. The comparison with other threshold models and configurations shows that the selected approach is the most promising starting point for further studies on debris flow early warning systems in areas with a scarcity of data.

Global Drought Services: Collaborations Toward an Information System for Early Warning

NASA Astrophysics Data System (ADS)

Hayes, M. J.; Pulwarty, R. S.; Svoboda, M.

2014-12-01

Drought is a hazard that lends itself well to diligent, sustained monitoring and early warning. However, unlike most hazards, the fact that droughts typically evolve slowly, can last for months or years and cover vast areas spanning multiple political boundaries/jurisdictions and economic sectors can make it a daunting task to monitor, develop plans for, and identify appropriate, proactive mitigation strategies. The National Drought Mitigation Center (NDMC) and National Integrated Drought Information System (NIDIS) have been working together to reduce societal vulnerability to drought by helping decision makers at all levels to: 1) implement drought early warning/forecasting and decision support systems; 2) support and advocate for better collection of, and understanding of drought impacts; and 3) increase long-term resilience to drought through proactive planning. The NDMC and NIDIS risk management approach has been the basis from which many partners around the world are developing a collaboration and coordination nexus with an ultimate goal of building comprehensive global drought early warning information systems (GDEWIS). The core emphasis of this model is on developing and applying useful and usable information that can be integrated and transferred freely to other regions around the globe. The High-Level Ministerial Declaration on Drought, the Integrated Drought Management Programme (IDMP) co-led by the WMO and the Global Water Partnership (GWP), and the Global Framework for Climate Services are drawing extensively from the integrated NDMC-NIDIS risk management framework. This presentation will describe, in detail, the various drought resources, tools, services, and collaborations already being provided and undertaken at the national and regional scales by the NDMC, NIDIS, and their partners. The presentation will be forward-looking, identifying improvements in existing and proposed mechanisms to help strengthen national and international drought early warning information systems to support preparedness and adaptation decisions in a changing climate.

Study on visual detection method for wind turbine blade failure

NASA Astrophysics Data System (ADS)

Chen, Jianping; Shen, Zhenteng

2018-02-01

Start your abstract here…At present, the non-destructive testing methods of the wind turbine blades has fiber bragg grating, sound emission and vibration detection, but there are all kinds of defects, and the engineering application is difficult. In this regard, three-point slope deviation method, which is a kind of visual inspection method, is proposed for monitoring the running status of wind turbine blade based on the image processing technology. A better blade image can be got through calibration, image splicing, pretreatment and threshold segmentation algorithm. Design of the early warning system to monitor wind turbine blade running condition, recognition rate, stability and impact factors of the method were statistically analysed. The experimental results shown showed that it has highly accurate and good monitoring effect.

Ecosystems for Early Warning: Potential Use of Bioindicators

NASA Astrophysics Data System (ADS)

Zommers, Z. A.; Sitati, A. M.; Habilov, M.

2014-12-01

Bioindicators are biological processes, species or communities, which are used to assess changes in the environment or environmental quality. Theoretically, they could also be used to provide advanced warning of hazards. They are inexpensive, locally relevant, and can encourage stakeholder participation in early warning system development and maintenance. While bioindicators have been identified for environmental problems such as air pollution and water pollution, and have been used to assess health of ecosystems, little information is available on bioindicators for climate related hazards. This presentation reviews possible biodindicators for droughts, wildfires and tropical cyclones, based on the results of a literature review. It will also present results from a household survey of 36 communities in Kenya, Ghana and Burkina Faso. Indigenous knowledge offers a wealth of potential bioindicators; including animal and insect behavior, and plant phenology. Yet significant study is needed to verify these indicators and evaluate them against criteria such as specificity, variability, monotonicity, practicality and relevance. Bioindicators may not be specific to individual hazards and may provide limited advanced warning, as response often occurs after the actual onset of the hazard. Furthermore, indicators may become increasingly unreliable due to climate change itself. There is a need for a large-scale assessment of hazard bioindicators, which should also include forecasts of bioindicator change under global warming, and a cost-benefit analysis of the value of integrating bioindicators into early warning systems. Lessons can be drawn from ethnopharmacology. Coordinated research on this topic could contribute to the resilience of both ecosystems and human livelihoods.

Early warning model based on correlated networks in global crude oil markets

NASA Astrophysics Data System (ADS)

Yu, Jia-Wei; Xie, Wen-Jie; Jiang, Zhi-Qiang

2018-01-01

Applying network tools on predicting and warning the systemic risks provides a novel avenue to manage risks in financial markets. Here, we construct a series of global crude oil correlated networks based on the historical 57 oil prices covering a period from 1993 to 2012. Two systemic risk indicators are constructed based on the density and modularity of correlated networks. The local maximums of the risk indicators are found to have the ability to predict the trends of oil prices. In our sample periods, the indicator based on the network density sends five signals and the indicator based on the modularity index sends four signals. The four signals sent by both indicators are able to warn the drop of future oil prices and the signal only sent by the network density is followed by a huge rise of oil prices. Our results deepen the application of network measures on building early warning models of systemic risks and can be applied to predict the trends of future prices in financial markets.

Development of structural health monitoring and early warning system for reinforced concrete system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Iranata, Data, E-mail: iranata-data@yahoo.com, E-mail: data@ce.its.ac.id; Wahyuni, Endah; Murtiadi, Suryawan

Many buildings have been damaged due to earthquakes that occurred recently in Indonesia. The main cause of the damage is the large deformation of the building structural component cannot accommodate properly. Therefore, it is necessary to develop the Structural Health Monitoring System (SHMS) to measure precisely the deformation of the building structural component in the real time conditions. This paper presents the development of SHMS for reinforced concrete structural system. This monitoring system is based on deformation component such as strain of reinforcement bar, concrete strain, and displacement of reinforced concrete component. Since the deformation component has exceeded the limitmore » value, the warning message can be sent to the building occupies. This warning message has also can be performed as early warning system of the reinforced concrete structural system. The warning message can also be sent via Short Message Service (SMS) through the Global System for Mobile Communications (GSM) network. Hence, the SHMS should be integrated with internet modem to connect with GSM network. Additionally, the SHMS program is verified with experimental study of simply supported reinforced concrete beam. Verification results show that the SHMS has good agreement with experimental results.« less

Integration of the Total Lightning Jump Algorithm into Current Operational Warning Environment Conceptual Models

NASA Technical Reports Server (NTRS)

Schultz, Christopher J.; Carey, Lawrence D.; Schultz, Elise V.; Stano, Geoffrey T.; Gatlin, Patrick N.

2013-01-01

The presence and rates of total lightning are both correlated to and physically dependent upon storm updraft strength, mixed phase precipitation volume and the size of the charging zone. The updraft modulates the ingredients necessary for electrification within a thunderstorm, while the updraft also plays a critical role in the development of severe and hazardous weather. Therefore utilizing this relationship, the monitoring of lightning rates and jumps provides an additional piece of information on the evolution of a thunderstorm, more often than not, at higher temporal resolution than current operational radar systems. This correlation is the basis for the total lightning jump algorithm that has been developed in recent years. In order to become a viable option for operational forecasters to incorporate into their severe storm monitoring process, the total lightning jump must be placed into the framework of several severe storm conceptual models (e.g., radar evolution, storm morphology) which forecasters have built through training and experience. Thus, one of the goals of this study is to examine and relate the lightning jump concept to often used radar parameters (e.g., dBZ vertical structure, VIL, MESH, MESO/shear) in the warning environment. Tying lightning trends and lightning jump occurrences to these radar based parameters will provide forecasters with an additional tool that they can use to build an accurate realtime depiction as to what is going on in a given environment. Furthermore, relating the lightning jump concept to these parameters could also increase confidence in a warning decision they have already made, help tip the scales on whether or not to warn on a given storm, or to draw the forecaster s attention to a particular storm that is rapidly developing. Furthermore the lightning information will add vital storm scale information in regions that are not well covered by radar, or when radar failures occur. The physical basis for the lightning jump algorithm in relation to severe storm dynamics and microphysics is a key component that must be further explored. Many radar studies have examined flash rates and their relation to updraft strength, updraft volume, precipitation -sized ice mass, etc.; however, very few have related the concept of the lightning jump and manifestation of severe weather to storm dynamics and microphysics using multi -Doppler and polarimetric radar techniques. Therefore, the second half of this study will combine the lightning jump algorithm and these radar techniques in order to place the lightning jump concept into a physical and dynamical framework. This analysis includes examining such parameters as mixed phase precipitation volume, charging zone, updraft strength and updraft volume. Such a study should provide increased understanding of and confidence in the strengths and limitations of the lightning jump algorithm in the storm warning process.

Current NASA Earth Remote Sensing Observations

NASA Technical Reports Server (NTRS)

Luvall, Jeffrey C.; Sprigg, William A.; Huete, Alfredo; Pejanovic, Goran; Nickovic, Slobodan; Ponce-Campos, Guillermo; Krapfl, Heide; Budge, Amy; Zelicoff, Alan; Myers, Orrin;

How to Decide? Multi-Objective Early-Warning Monitoring Networks for Water Suppliers

NASA Astrophysics Data System (ADS)

Bode, Felix; Loschko, Matthias; Nowak, Wolfgang

2015-04-01

Groundwater is a resource for drinking water and hence needs to be protected from contaminations. However, many well catchments include an inventory of known and unknown risk sources, which cannot be eliminated, especially in urban regions. As a matter of risk control, all these risk sources should be monitored. A one-to-one monitoring situation for each risk source would lead to a cost explosion and is even impossible for unknown risk sources. However, smart optimization concepts could help to find promising low-cost monitoring network designs. In this work we develop a concept to plan monitoring networks using multi-objective optimization. Our considered objectives are to maximize the probability of detecting all contaminations, to enhance the early warning time before detected contaminations reach the drinking water well, and to minimize the installation and operating costs of the monitoring network. Using multi-objectives optimization, we avoid the problem of having to weight these objectives to a single objective-function. These objectives are clearly competing, and it is impossible to know their mutual trade-offs beforehand - each catchment differs in many points and it is hardly possible to transfer knowledge between geological formations and risk inventories. To make our optimization results more specific to the type of risk inventory in different catchments we do risk prioritization of all known risk sources. Due to the lack of the required data, quantitative risk ranking is impossible. Instead, we use a qualitative risk ranking to prioritize the known risk sources for monitoring. Additionally, we allow for the existence of unknown risk sources that are totally uncertain in location and in their inherent risk. Therefore, they can neither be located nor ranked. Instead, we represent them by a virtual line of risk sources surrounding the production well. We classify risk sources into four different categories: severe, medium and tolerable for known risk sources and an extra category for the unknown ones. With that, early warning time and detection probability become individual objectives for each risk class. Thus, decision makers can identify monitoring networks valid for controlling the top risk sources, and evaluate the capabilities (or search for least-cost upgrades) to also cover moderate, tolerable and unknown risk sources. Monitoring networks, which are valid for the remaining risk also cover all other risk sources, but only with a relatively poor early-warning time. The data provided for the optimization algorithm are calculated in a preprocessing step by a flow and transport model. It simulates, which potential contaminant plumes from the risk sources would be detectable where and when by all possible candidate positions for monitoring wells. Uncertainties due to hydro(geo)logical phenomena are taken into account by Monte-Carlo simulations. These include uncertainty in ambient flow direction of the groundwater, uncertainty of the conductivity field, and different scenarios for the pumping rates of the production wells. To avoid numerical dispersion during the transport simulations, we use particle-tracking random walk methods when simulating transport.

Using Quality Improvement to Introduce and Standardize the National Early Warning Score (NEWS) for Adult Inpatients at a Children's Hospital.

PubMed

Conway-Habes, Erin E; Herbst, Brian F; Herbst, Lori A; Kinnear, Benjamin; Timmons, Kristen; Horewitz, Deborah; Falgout, Rachel; O'Toole, Jennifer K; Vossmeyer, Michael

2017-03-01

The population of adults with childhood-onset chronic illness is growing across children's hospitals and constitutes a high risk population. National Early Warning Score (NEWS) is among the most recently validated adult early warning scores (EWSs) for early recognition of and response to clinical deterioration. Our aim was to implement and standardize NEWS scoring in 80% of patients age 21 and older admitted to a children's hospital. Our intervention was tested on a single unit of our children's hospital. The primary process measure was the percentage of NEWS documented within 1 hour of routine nursing assessments, and was tracked using a run chart. Improvement activities focused on effective training, key stakeholder buy-in, increased awareness, real-time mitigation of failures, accountability for adherence, and action-oriented response. We also tracked the distribution of NEWS values and medical emergency team calls. The percentage of NEWS documented with routine nursing assessments for patients age 21 and over increased from 0% to 90% within 15 weeks and remained at 77% or greater for 17 weeks. Our distribution of NEWS values was similar to previously reported NEWS distribution. A nurse-driven adult early warning system for inpatients age 21 and older at a children's hospital can be achieved through a standardized EWS assessment process, incorporation into the electronic health record, and charge nurse and key stakeholder oversight. Furthermore, implementation of an adult EWS being used at a pediatric institution and our distribution of NEWS values were comparable to distribution published from adult hospitals. Copyright © 2017 by the American Academy of Pediatrics.

Early warning systems and rapid response to the deteriorating patient in hospital: A realist evaluation.

PubMed

McGaughey, Jennifer; O'Halloran, Peter; Porter, Sam; Trinder, John; Blackwood, Bronagh

2017-12-01

To test the Rapid Response Systems programme theory against actual practice components of the Rapid Response Systems implemented to identify those contexts and mechanisms which have an impact on the successful achievement of desired outcomes in practice. Rapid Response Systems allow deteriorating patients to be recognized using Early Warning Systems, referred early via escalation protocols and managed at the bedside by competent staff. Realist evaluation. The research design was an embedded multiple case study approach of four wards in two hospitals in Northern Ireland which followed the principles of Realist Evaluation. We used various mixed methods including individual and focus group interviews, observation of nursing practice between June-November 2010 and document analysis of Early Warning Systems audit data between May-October 2010 and hospital acute care training records over 4.5 years from 2003-2008. Data were analysed using NiVivo8 and SPPS. A cross-case analysis highlighted similar patterns of factors which enabled or constrained successful recognition, referral and response to deteriorating patients in practice. Key enabling factors were the use of clinical judgement by experienced nurses and the empowerment of nurses as a result of organizational change associated with implementation of Early Warning System protocols. Key constraining factors were low staffing and inappropriate skill mix levels, rigid implementation of protocols and culturally embedded suboptimal communication processes. Successful implementation of Rapid Response Systems was dependent on adopting organizational and cultural changes that facilitated staff empowerment, flexible implementation of protocols and ongoing experiential learning. © 2017 John Wiley & Sons Ltd.

Successful ShakeAlert Performance for the Napa Quake

NASA Astrophysics Data System (ADS)

Allen, R. M.; Given, D. D.; Heaton, T. H.; Vidale, J. E.

2014-12-01

ShakeAlert, the demonstration earthquake early warning system, developed by the USGS, UC Berkeley, Caltech, ETH, and the University of Washington, functioned as expected for the August 24, 2014, M6.0 Napa earthquake. The first ShakeAlert was generated by the ElarmS algorithm 5.1 sec after the origin time of the earthquake, and 3.3 sec after the P-wave arrived at the closest station 6.5 km from the epicenter. This initial alert, based on P-wave triggers from four stations, estimated the magnitude to be 5.7. The warning was received at the UC Berkeley Seismological Laboratory 5 seconds before the S-wave and about 10 sec prior to the onset of the strongest shaking. ShakeAlert beta-testers across the San Francisco Bay Area simultaneously received the alert, including the San Francisco 911 center with 8 sec warning, and the BART train system. BART has implemented an automated train-stopping system that was activated (although no trains were running at 3:20 am). With the available network geometry and communications, the blind zone of the first alert had a radius of 16 km. The four stations that contributed to the first alert all encapsulate data into 1-second packets, but the latency in transmitting data to the processing center ranged from 0.27 to 2.62 seconds. If all the stations were to deliver data in 0.27 seconds, then the alert would have been available 2.3 sec sooner and the blind zone would be reduced to about 8 km. This would also mean that the city of Napa would have received about 1 second of warning. The magnitude estimate and event location were accurate from the initial alert onwards. The magnitude estimate did first increase to 5.8 and then dip to 5.4 2.6 sec after the initial alert, stayed at that level for 2 sec, and then returned to 5.7. The final magnitude estimate was 6.0, consistent with the ANSS catalog.

Progress in Development of an Airborne Turbulence Detection System

NASA Technical Reports Server (NTRS)

Hamilton, David W.; Proctor, Fred H.

2006-01-01

Aircraft encounters with turbulence are the leading cause of in-flight injuries (Tyrvanas 2003) and have occasionally resulted in passenger and crew fatalities. Most of these injuries are caused by sudden and unexpected encounters with severe turbulence in and around convective activity (Kaplan et al 2005). To alleviate this problem, the Turbulence Prediction and Warning Systems (TPAWS) element of NASA s Aviation Safety program has investigated technologies to detect and warn of hazardous in-flight turbulence. This effort has required the numerical modeling of atmospheric convection: 1) for characterizing convectively induced turbulence (CIT) environments, 2) for defining turbulence hazard metrics, and 3) as a means of providing realistic three-dimensional data sets that can be used to test and evaluate turbulence detection sensors. The data sets are being made available to industry and the FAA for certification of future airborne turbulence-detection systems (ATDS) with warning capability. Early in the TPAWS project, a radar-based ATDS was installed and flight tested on NASA s research aircraft, a B-757. This ATDS utilized new algorithms and hazard metrics that were developed for use with existing airborne predictive windshear radars, thus avoiding the installation of new hardware. This system was designed to detect and warn of hazardous CIT even in regions with weak radar reflectivity (i.e. 5-15 dBz). Results from an initial flight test of the ATDS were discussed in Hamilton and Proctor (2002a; 2002b). In companion papers (Proctor et al 2002a; 2002b), a numerical simulation of the most significant encounter from that flight test was presented. Since the presentation of these papers a second flight test has been conducted providing additional cases for examination. In this paper, we will present results from NASA s flight test and a numerical model simulation of a turbulence environment encountered on 30 April 2002. Progress leading towards FAA certification of industry built ATDS will also be discussed.

Elucidation and short-term forecasting of microcystin concentrations in Lake Suwa (Japan) by means of artificial neural networks and evolutionary algorithms.

PubMed

Chan, Wai Sum; Recknagel, Friedrich; Cao, Hongqing; Park, Ho-Dong

2007-05-01

Non-supervised artificial neural networks (ANN) and hybrid evolutionary algorithms (EA) were applied to analyse and model 12 years of limnological time-series data of the shallow hypertrophic Lake Suwa in Japan. The results have improved understanding of relationships between changing microcystin concentrations, Microcystis species abundances and annual rainfall intensity. The data analysis by non-supervised ANN revealed that total Microcystis abundance and extra-cellular microcystin concentrations in typical dry years are much higher than those in typical wet years. It also showed that high microcystin concentrations in dry years coincided with the dominance of the toxic Microcystis viridis whilst in typical wet years non-toxic Microcystis ichthyoblabe were dominant. Hybrid EA were used to discover rule sets to explain and forecast the occurrence of high microcystin concentrations in relation to water quality and climate conditions. The results facilitated early warning by 3-days-ahead forecasting of microcystin concentrations based on limnological and meteorological input data, achieving an r(2)=0.74 for testing.

Dust Storm Feature Identification and Tracking from 4D Simulation Data

NASA Astrophysics Data System (ADS)

Yu, M.; Yang, C. P.

2016-12-01

Dust storms cause significant damage to health, property and the environment worldwide every year. To help mitigate the damage, dust forecasting models simulate and predict upcoming dust events, providing valuable information to scientists, decision makers, and the public. Normally, the model simulations are conducted in four-dimensions (i.e., latitude, longitude, elevation and time) and represent three-dimensional (3D), spatial heterogeneous features of the storm and its evolution over space and time. This research investigates and proposes an automatic multi-threshold, region-growing based identification algorithm to identify critical dust storm features, and track the evolution process of dust storm events through space and time. In addition, a spatiotemporal data model is proposed, which can support the characterization and representation of dust storm events and their dynamic patterns. Quantitative and qualitative evaluations for the algorithm are conducted to test the sensitivity, and capability of identify and track dust storm events. This study has the potential to assist a better early warning system for decision-makers and the public, thus making hazard mitigation plans more effective.

Classification Model for Forest Fire Hotspot Occurrences Prediction Using ANFIS Algorithm

NASA Astrophysics Data System (ADS)

Wijayanto, A. K.; Sani, O.; Kartika, N. D.; Herdiyeni, Y.

2017-01-01

This study proposed the application of data mining technique namely Adaptive Neuro-Fuzzy inference system (ANFIS) on forest fires hotspot data to develop classification models for hotspots occurrence in Central Kalimantan. Hotspot is a point that is indicated as the location of fires. In this study, hotspot distribution is categorized as true alarm and false alarm. ANFIS is a soft computing method in which a given inputoutput data set is expressed in a fuzzy inference system (FIS). The FIS implements a nonlinear mapping from its input space to the output space. The method of this study classified hotspots as target objects by correlating spatial attributes data using three folds in ANFIS algorithm to obtain the best model. The best result obtained from the 3rd fold provided low error for training (error = 0.0093676) and also low error testing result (error = 0.0093676). Attribute of distance to road is the most determining factor that influences the probability of true and false alarm where the level of human activities in this attribute is higher. This classification model can be used to develop early warning system of forest fire.

Research and Application of an Air Quality Early Warning System Based on a Modified Least Squares Support Vector Machine and a Cloud Model.

PubMed

Wang, Jianzhou; Niu, Tong; Wang, Rui

2017-03-02

The worsening atmospheric pollution increases the necessity of air quality early warning systems (EWSs). Despite the fact that a massive amount of investigation about EWS in theory and practicality has been conducted by numerous researchers, studies concerning the quantification of uncertain information and comprehensive evaluation are still lacking, which impedes further development in the area. In this paper, firstly a comprehensive warning system is proposed, which consists of two vital indispensable modules, namely effective forecasting and scientific evaluation, respectively. For the forecasting module, a novel hybrid model combining the theory of data preprocessing and numerical optimization is first developed to implement effective forecasting for air pollutant concentration. Especially, in order to further enhance the accuracy and robustness of the warning system, interval forecasting is implemented to quantify the uncertainties generated by forecasts, which can provide significant risk signals by using point forecasting for decision-makers. For the evaluation module, a cloud model, based on probability and fuzzy set theory, is developed to perform comprehensive evaluations of air quality, which can realize the transformation between qualitative concept and quantitative data. To verify the effectiveness and efficiency of the warning system, extensive simulations based on air pollutants data from Dalian in China were effectively implemented, which illustrate that the warning system is not only remarkably high-performance, but also widely applicable.

Research and Application of an Air Quality Early Warning System Based on a Modified Least Squares Support Vector Machine and a Cloud Model

PubMed Central

Wang, Jianzhou; Niu, Tong; Wang, Rui

2017-01-01

The worsening atmospheric pollution increases the necessity of air quality early warning systems (EWSs). Despite the fact that a massive amount of investigation about EWS in theory and practicality has been conducted by numerous researchers, studies concerning the quantification of uncertain information and comprehensive evaluation are still lacking, which impedes further development in the area. In this paper, firstly a comprehensive warning system is proposed, which consists of two vital indispensable modules, namely effective forecasting and scientific evaluation, respectively. For the forecasting module, a novel hybrid model combining the theory of data preprocessing and numerical optimization is first developed to implement effective forecasting for air pollutant concentration. Especially, in order to further enhance the accuracy and robustness of the warning system, interval forecasting is implemented to quantify the uncertainties generated by forecasts, which can provide significant risk signals by using point forecasting for decision-makers. For the evaluation module, a cloud model, based on probability and fuzzy set theory, is developed to perform comprehensive evaluations of air quality, which can realize the transformation between qualitative concept and quantitative data. To verify the effectiveness and efficiency of the warning system, extensive simulations based on air pollutants data from Dalian in China were effectively implemented, which illustrate that the warning system is not only remarkably high-performance, but also widely applicable. PMID:28257122

Establishment and Practical Application of Flood Warning Stage in Taiwan's River

NASA Astrophysics Data System (ADS)

Yang, Sheng-Hsueh; Chia Yeh, Keh-

2017-04-01

In the face of extreme flood events or the possible impact of climate change, non-engineering disaster prevention and early warning work is particularly important. Taiwan is an island topography with more than 3,900 meters of high mountains. The length of the river is less than 100 kilometers. Most of the watershed catchment time is less than 24 hours, which belongs to the river with steep slope and rapid flood. Every year in summer and autumn, several typhoon events invade Taiwan. Typhoons often result in rainfall events in excess of 100 mm/hr or 250 mm/3hr. In the face of Taiwan's terrain and extreme rainfall events, flooding is difficult to avoid. Therefore, most of the river has embankment protection, so that people do not have to face every year flooding caused by economic and life and property losses. However, the river embankment protection is limited. With the increase of the hydrological data, the design criteria for the embankment protection standards in the past was 100 year of flood return period and is now gradually reduced to 25 or 50 year of flood return period. The river authorities are not easy to rise the existing embankment height. The safety of the river embankment in Taiwan is determined by the establishment of the flood warning stage to cope with the possible increase in annual floods and the impact of extreme hydrological events. The flood warning stage is equal to the flood control elevation minus the flood rise rate multiply by the flood early warning time. The control elevation can be the top of the embankment, the design flood level of the river, the embankment gap of the river section, the height of the bridge beam bottom, etc. The flood rise rate is consider the factors such as hydrological stochastic and uncertain rainfall and the effect of flood discharge operation on the flood in the watershed catchment area. The maximum value of the water level difference between the two hours or five hours before the peak value of the analysis result is decided by this rate. The flood early warning time is divided into two levels, the first level is 2 hours, evacuation time for the public, the second level is 5 hours for the implementation of unit preparation time. Finally, The flood warning stages are practical application in 20 water level stations have been incorporated into the flood early warning system of the Danshuei river basin in Taiwan.

The journalist's role in bioethics.

PubMed

Rosenfeld, A

1999-04-01

In the late 1950s and early 1960s, emerging advances in the biomedical sciences raised insufficiently noticed ethical issues, prompting science reporters to serve as a sort of Early Warning System. As awareness of bioethical issues increased rapidly everywhere, and bioethics itself arrived as a recognized discipline, the need for this early-warning press role has clearly diminished. A secondary but important role for the science journalist is that of investigative reporter/whistleblower, as in the Tuskegee syphilis trials and the government's secret plutonium experiments. Because the general public gets most of its information from the popular media, ways are suggested for journalists and bioethicists to work together.

Famine Early Warning Systems and Their Use of Satellite Remote Sensing Data

NASA Technical Reports Server (NTRS)

Brown, Molly E.; Essam, Timothy; Leonard, Kenneth

2011-01-01

Famine early warning organizations have experience that has much to contribute to efforts to incorporate climate and weather information into economic and political systems. Food security crises are now caused almost exclusively by problems of food access, not absolute food availability, but the role of monitoring agricultural production both locally and globally remains central. The price of food important to the understanding of food security in any region, but it needs to be understood in the context of local production. Thus remote sensing is still at the center of much food security analysis, along with an examination of markets, trade and economic policies during food security analyses. Technology including satellite remote sensing, earth science models, databases of food production and yield, and modem telecommunication systems contributed to improved food production information. Here we present an econometric approach focused on bringing together satellite remote sensing and market analysis into food security assessment in the context of early warning.

Early-warning signals for an outbreak of the influenza pandemic

NASA Astrophysics Data System (ADS)

Ren, Di; Gao, Jie

2011-12-01

Over the course of human history, influenza pandemics have been seen as major disasters, so studies on the influenza virus have become an important issue for many experts and scholars. Comprehensive research has been performed over the years on the biological properties, chemical characteristics, external environmental factors and other aspects of the virus, and some results have been achieved. Based on the chaos game representation walk model, this paper uses the time series analysis method to study the DNA sequences of the influenza virus from 1913 to 2010, and works out the early-warning signals indicator value for the outbreak of an influenza pandemic. The variances in the CGR walk sequences for the pandemic years (or + -1 to 2 years) are significantly higher than those for the adjacent years, while those in the non-pandemic years are usually smaller. In this way we can provide an influenza early-warning mechanism so that people can take precautions and be well prepared prior to a pandemic.

Evaluating probabilistic dengue risk forecasts from a prototype early warning system for Brazil

PubMed Central

Lowe, Rachel; Coelho, Caio AS; Barcellos, Christovam; Carvalho, Marilia Sá; Catão, Rafael De Castro; Coelho, Giovanini E; Ramalho, Walter Massa; Bailey, Trevor C; Stephenson, David B; Rodó, Xavier

2016-01-01

Recently, a prototype dengue early warning system was developed to produce probabilistic forecasts of dengue risk three months ahead of the 2014 World Cup in Brazil. Here, we evaluate the categorical dengue forecasts across all microregions in Brazil, using dengue cases reported in June 2014 to validate the model. We also compare the forecast model framework to a null model, based on seasonal averages of previously observed dengue incidence. When considering the ability of the two models to predict high dengue risk across Brazil, the forecast model produced more hits and fewer missed events than the null model, with a hit rate of 57% for the forecast model compared to 33% for the null model. This early warning model framework may be useful to public health services, not only ahead of mass gatherings, but also before the peak dengue season each year, to control potentially explosive dengue epidemics. DOI: http://dx.doi.org/10.7554/eLife.11285.001 PMID:26910315

Heatwave early warning systems and adaptation advice to reduce human health consequences of heatwaves.

PubMed

Lowe, Dianne; Ebi, Kristie L; Forsberg, Bertil

2011-12-01

With climate change, there has been an increase in the frequency, intensity and duration of heatwave events. In response to the devastating mortality and morbidity of recent heatwave events, many countries have introduced heatwave early warning systems (HEWS). HEWS are designed to reduce the avoidable human health consequences of heatwaves through timely notification of prevention measures to vulnerable populations. To identify the key characteristics of HEWS in European countries to help inform modification of current, and development of, new systems and plans. We searched the internet to identify HEWS policy or government documents for 33 European countries and requested information from relevant organizations. We translated the HEWS documents and extracted details on the trigger indicators, thresholds for action, notification strategies, message intermediaries, communication and dissemination strategies, prevention strategies recommended and specified target audiences. Twelve European countries have HEWS. Although there are many similarities among the HEWS, there also are differences in key characteristics that could inform improvements in heatwave early warning plans.

Instability mechanisms and early warning indicators for mesophilic anaerobic digestion of vegetable waste.

PubMed

Li, Dong; Chen, Lin; Liu, Xiaofeng; Mei, Zili; Ren, Haiwei; Cao, Qin; Yan, Zhiying

2017-12-01

In order to elucidate the instability mechanism, screen early warning indicators, and propose control measures, the mesophilic digestion of vegetable waste (VW) was carried out at organic loading rates (OLR) of 0.5, 1.0, and 1.5g volatile solid (VS)/(Ld). The process parameters, including biogas components, volatile fatty acids (VFA), ammonia, pH, total alkalinity (TA), bicarbonate alkalinity (BA), and intermediate alkalinity (IA), were monitored every day. Digestion was inhibited at OLR of 1.5gVS/(Ld). The primary causes of instability are a high sugar and negligible ammonia content, in addition to the feed without effluent recirculation, which led to BA loss. The ratios of CH 4 /CO 2 , VFA/BA, propionate, n-butyrate and iso-valerate were selected as early warning indicators. In order to maintain the digestion of VW at a high OLR, control measures including effluent recirculation and trace element addition are recommended. Copyright © 2017 Elsevier Ltd. All rights reserved.

Potentiation of the early visual response to learned danger signals in adults and adolescents

PubMed Central

Howsley, Philippa; Jordan, Jeff; Johnston, Pat

2015-01-01

The reinforcing effects of aversive outcomes on avoidance behaviour are well established. However, their influence on perceptual processes is less well explored, especially during the transition from adolescence to adulthood. Using electroencephalography, we examined whether learning to actively or passively avoid harm can modulate early visual responses in adolescents and adults. The task included two avoidance conditions, active and passive, where two different warning stimuli predicted the imminent, but avoidable, presentation of an aversive tone. To avoid the aversive outcome, participants had to learn to emit an action (active avoidance) for one of the warning stimuli and omit an action for the other (passive avoidance). Both adults and adolescents performed the task with a high degree of accuracy. For both adolescents and adults, increased N170 event-related potential amplitudes were found for both the active and the passive warning stimuli compared with control conditions. Moreover, the potentiation of the N170 to the warning stimuli was stable and long lasting. Developmental differences were also observed; adolescents showed greater potentiation of the N170 component to danger signals. These findings demonstrate, for the first time, that learned danger signals in an instrumental avoidance task can influence early visual sensory processes in both adults and adolescents. PMID:24652856

A spatial Bayesian network model to assess the benefits of early warning for urban flood risk to people

NASA Astrophysics Data System (ADS)

Balbi, S.; Villa, F.; Mojtahed, V.; Hegetschweiler, K. T.; Giupponi, C.

2015-10-01

This article presents a novel methodology to assess flood risk to people by integrating people's vulnerability and ability to cushion hazards through coping and adapting. The proposed approach extends traditional risk assessments beyond material damages; complements quantitative and semi-quantitative data with subjective and local knowledge, improving the use of commonly available information; produces estimates of model uncertainty by providing probability distributions for all of its outputs. Flood risk to people is modeled using a spatially explicit Bayesian network model calibrated on expert opinion. Risk is assessed in terms of: (1) likelihood of non-fatal physical injury; (2) likelihood of post-traumatic stress disorder; (3) likelihood of death. The study area covers the lower part of the Sihl valley (Switzerland) including the city of Zurich. The model is used to estimate the benefits of improving an existing Early Warning System, taking into account the reliability, lead-time and scope (i.e. coverage of people reached by the warning). Model results indicate that the potential benefits of an improved early warning in terms of avoided human impacts are particularly relevant in case of a major flood event: about 75 % of fatalities, 25 % of injuries and 18 % of post-traumatic stress disorders could be avoided.

Operational Tsunami Modelling with TsunAWI for the German-Indonesian Tsunami Early Warning System: Recent Developments

NASA Astrophysics Data System (ADS)

Rakowsky, N.; Harig, S.; Androsov, A.; Fuchs, A.; Immerz, A.; Schröter, J.; Hiller, W.

2012-04-01

Starting in 2005, the GITEWS project (German-Indonesian Tsunami Early Warning System) established from scratch a fully operational tsunami warning system at BMKG in Jakarta. Numerical simulations of prototypic tsunami scenarios play a decisive role in a priori risk assessment for coastal regions and in the early warning process itself. Repositories with currently 3470 regional tsunami scenarios for GITEWS and 1780 Indian Ocean wide scenarios in support of Indonesia as a Regional Tsunami Service Provider (RTSP) were computed with the non-linear shallow water modell TsunAWI. It is based on a finite element discretisation, employs unstructured grids with high resolution along the coast and includes inundation. This contribution gives an overview on the model itself, the enhancement of the model physics, and the experiences gained during the process of establishing an operational code suited for thousands of model runs. Technical aspects like computation time, disk space needed for each scenario in the repository, or post processing techniques have a much larger impact than they had in the beginning when TsunAWI started as a research code. Of course, careful testing on artificial benchmarks and real events remains essential, but furthermore, quality control for the large number of scenarios becomes an important issue.

Developing and testing an Early Warning System for Non Indigenous Species and Ballast Water Management

NASA Astrophysics Data System (ADS)

Magaletti, Erika; Garaventa, Francesca; David, Matej; Castriota, Luca; Kraus, Romina; Luna, Gian Marco; Silvestri, Cecilia; Forte, Cosmo; Bastianini, Mauro; Falautano, Manuela; Maggio, Teresa; Rak, Giulietta; Gollasch, Stephan

2018-03-01

This paper describes the methodological approach used for the development of an Early Warning System (EWS) for Non Indigenous Species (NIS) and ballast water management and summarizes the results obtained. The specific goals of the EWS are firstly to warn vessels to prevent loading of ballast water when critical biological conditions occur in ports and surrounding areas i.e. mass development or blooms of Harmful Aquatic Organisms and Pathogens (HAOP). Secondly, to warn environmental and health authorities when NIS or pathogens are present in ports or surrounding areas to enable an early response and an implementation of remediation measures. The EWS is designed to be used for implementing various parallel obligations, by taking into consideration different legal scopes, associated information and decision-making needs. The EWS was elaborated, tested in the Adriatic Sea and illustrated by two case studies. Although the EWS was developed with an Adriatic Sea focus, it is presented in a format so that it may be used as a model when establishing similar systems in other locations. The role of the various actors is discussed and recommendations on further developments of the EWS are presented. It was concluded that the EWS is a suitable tool to reduce the spread of potentially harmful and ballast water mediated species.

Early Warning Signals for Abrupt Change Raise False Alarm During Sea Ice Loss

NASA Astrophysics Data System (ADS)

Wagner, T. J. W.; Eisenman, I.

2015-12-01

Uncovering universal early warning signals for critical transitions has become a coveted goal in diverse scientific disciplines, ranging from climate science to financial mathematics. There has been a flurry of recent research proposing such signals, with increasing autocorrelation and increasing variance being among the most widely discussed candidates. A number of studies have suggested that increasing autocorrelation alone may suffice to signal an impending transition, although some others have questioned this. Here, we consider variance and autocorrelation in the context of sea ice loss in an idealized model of the global climate system. The model features no bifurcation, nor increased rate of retreat, as the ice disappears. Nonetheless, the autocorrelation of summer sea ice area is found to increase with diminishing sea ice cover in a global warming scenario. The variance, by contrast, decreases. A simple physical mechanism is proposed to explain the occurrence of increasing autocorrelation but not variance in the model when there is no approaching bifurcation. Additionally, a similar mechanism is shown to allow an increase in both indicators with no physically attainable bifurcation. This implies that relying on autocorrelation and variance as early warning signals can raise false alarms in the climate system, warning of "tipping points" that are not actually there.

[Application of EARS in early-warning of influenza pandemic in Beijing].

PubMed

Zhang, Dai-tao; Yang, Peng; Zhang, Yi; Zhang, Li; Peng, Xiao-min; Shi, Wei-xian; Lu, Gui-lan; Liang, Hui-jie; Liu, Yi-meng; Liu, Min; Wang, Quan-yi

2012-06-18

To illustrate the efficiency of cumulative sum (CUSUM) in pre-warning of the influenza peak in Beijing. CUSUM was used to analyze the data of influenza like illness (ILI), and the results of the influenza laboratory surveillance was regarded as the gold standard to judge the approaching of the influenza peak. The surveillance was launched in 421 hospitals in Beijing during the 2009 to 2010 influenza season, while the influenza laboratory surveillance was launched by 7 collaborative laboratories. From Jun. 2009 to Apr. 2010, the average ILI percentage in the 421 hospitals was 2.56%. In the study, 19 262 pharyngeal swab samples were collected from the ILI cases in 11 hospitals and 5 045 of them were tested positive for the influenza virus, with the novel swine-origin influenza A H1N1 virus dominating. After analyzing of the ILI surveillance data with CUSUM, it was found that the ILI surveillance in Beijing could make a satisfactory early warning for the approaching of the influenza peak referring to the gold standard based on the influenza laboratory results. It could give the prediction and early warning for the influenza peak efficiently and precisely, by using CUSUM to analyze the influenza surveillance data of Beijing.

Analyzing a 35-Year Hourly Data Record: Why So Difficult?

NASA Technical Reports Server (NTRS)

Lynnes, Chris

2014-01-01

At the Goddard Distributed Active Archive Center, we have recently added a 35-Year record of output data from the North American Land Assimilation System (NLDAS) to the Giovanni web-based analysis and visualization tool. Giovanni (Geospatial Interactive Online Visualization ANd aNalysis Infrastructure) offers a variety of data summarization and visualization to users that operate at the data center, obviating the need for users to download and read the data themselves for exploratory data analysis. However, the NLDAS data has proven surprisingly resistant to application of the summarization algorithms. Algorithms that were perfectly happy analyzing 15 years of daily satellite data encountered limitations both at the algorithm and system level for 35 years of hourly data. Failures arose, sometimes unexpectedly, from command line overflows, memory overflows, internal buffer overflows, and time-outs, among others. These serve as an early warning sign for the problems likely to be encountered by the general user community as they try to scale up to Big Data analytics. Indeed, it is likely that more users will seek to perform remote web-based analysis precisely to avoid the issues, or the need to reprogram around them. We will discuss approaches to mitigating the limitations and the implications for data systems serving the user communities that try to scale up their current techniques to analyze Big Data.

A novel method for landslide displacement prediction by integrating advanced computational intelligence algorithms.

PubMed

Zhou, Chao; Yin, Kunlong; Cao, Ying; Ahmed, Bayes; Fu, Xiaolin

2018-05-08

Landslide displacement prediction is considered as an essential component for developing early warning systems. The modelling of conventional forecast methods requires enormous monitoring data that limit its application. To conduct accurate displacement prediction with limited data, a novel method is proposed and applied by integrating three computational intelligence algorithms namely: the wavelet transform (WT), the artificial bees colony (ABC), and the kernel-based extreme learning machine (KELM). At first, the total displacement was decomposed into several sub-sequences with different frequencies using the WT. Next each sub-sequence was predicted separately by the KELM whose parameters were optimized by the ABC. Finally the predicted total displacement was obtained by adding all the predicted sub-sequences. The Shuping landslide in the Three Gorges Reservoir area in China was taken as a case study. The performance of the new method was compared with the WT-ELM, ABC-KELM, ELM, and the support vector machine (SVM) methods. Results show that the prediction accuracy can be improved by decomposing the total displacement into sub-sequences with various frequencies and by predicting them separately. The ABC-KELM algorithm shows the highest prediction capacity followed by the ELM and SVM. Overall, the proposed method achieved excellent performance both in terms of accuracy and stability.

Building regional early flood warning systems by AI techniques

NASA Astrophysics Data System (ADS)

Chang, F. J.; Chang, L. C.; Amin, M. Z. B. M.

2017-12-01

Building early flood warning system is essential for the protection of the residents against flood hazards and make actions to mitigate the losses. This study implements AI technology for forecasting multi-step-ahead regional flood inundation maps during storm events. The methodology includes three major schemes: (1) configuring the self-organizing map (SOM) to categorize a large number of regional inundation maps into a meaningful topology; (2) building dynamic neural networks to forecast multi-step-ahead average inundated depths (AID); and (3) adjusting the weights of the selected neuron in the constructed SOM based on the forecasted AID to obtain real-time regional inundation maps. The proposed models are trained, and tested based on a large number of inundation data sets collected in regions with the most frequent and serious flooding in the river basin. The results appear that the SOM topological relationships between individual neurons and their neighbouring neurons are visible and clearly distinguishable, and the hybrid model can continuously provide multistep-ahead visible regional inundation maps with high resolution during storm events, which have relatively small RMSE values and high R2 as compared with numerical simulation data sets. The computing time is only few seconds, and thereby leads to real-time regional flood inundation forecasting and make early flood inundation warning system. We demonstrate that the proposed hybrid ANN-based model has a robust and reliable predictive ability and can be used for early warning to mitigate flood disasters.

Seasonal Water Balance Forecasts for Drought Early Warning in Ethiopia

NASA Astrophysics Data System (ADS)

Spirig, Christoph; Bhend, Jonas; Liniger, Mark

2016-04-01

Droughts severely impact Ethiopian agricultural production. Successful early warning for drought conditions in the upcoming harvest season therefore contributes to better managing food shortages arising from adverse climatic conditions. So far, however, meteorological seasonal forecasts have not been used in Ethiopia's national food security early warning system (i.e. the LEAP platform). Here we analyse the forecast quality of seasonal forecasts of total rainfall and of the meteorological water balance as a proxy for plant available water. We analyse forecast skill of June to September rainfall and water balance from dynamical seasonal forecast systems, the ECMWF System4 and EC-EARTH global forecasting systems. Rainfall forecasts outperform forecasts assuming a stationary climate mainly in north-eastern Ethiopia - an area that is particularly vulnerable to droughts. Forecasts of the water balance index seem to be even more skilful and thus more useful than pure rainfall forecasts. The results vary though for different lead times and skill measures employed. We further explore the potential added value of dynamically downscaling the forecasts through several dynamical regional climate models made available through the EU FP7 project EUPORIAS. Preliminary results suggest that dynamically downscaled seasonal forecasts are not significantly better compared with seasonal forecasts from the global models. We conclude that seasonal forecasts of a simple climate index such as the water balance have the potential to benefit drought early warning in Ethiopia, both due to its positive predictive skill and higher usefulness than seasonal mean quantities.

A Mathematical Framework for Critical Transitions: Normal Forms, Variance and Applications

NASA Astrophysics Data System (ADS)

Kuehn, Christian

2013-06-01

Critical transitions occur in a wide variety of applications including mathematical biology, climate change, human physiology and economics. Therefore it is highly desirable to find early-warning signs. We show that it is possible to classify critical transitions by using bifurcation theory and normal forms in the singular limit. Based on this elementary classification, we analyze stochastic fluctuations and calculate scaling laws of the variance of stochastic sample paths near critical transitions for fast-subsystem bifurcations up to codimension two. The theory is applied to several models: the Stommel-Cessi box model for the thermohaline circulation from geoscience, an epidemic-spreading model on an adaptive network, an activator-inhibitor switch from systems biology, a predator-prey system from ecology and to the Euler buckling problem from classical mechanics. For the Stommel-Cessi model we compare different detrending techniques to calculate early-warning signs. In the epidemics model we show that link densities could be better variables for prediction than population densities. The activator-inhibitor switch demonstrates effects in three time-scale systems and points out that excitable cells and molecular units have information for subthreshold prediction. In the predator-prey model explosive population growth near a codimension-two bifurcation is investigated and we show that early-warnings from normal forms can be misleading in this context. In the biomechanical model we demonstrate that early-warning signs for buckling depend crucially on the control strategy near the instability which illustrates the effect of multiplicative noise.

Early Warning Signs of Suicide in Service Members Who Engage in Unauthorized Acts of Violence

DTIC Science & Technology

2016-06-01

observable to military law enforcement personnel. Statistical analyses tested for differences in warning signs between cases of suicide, violence, or...indicators, (2) Behavioral Change indicators, (3) Social indicators, and (4) Occupational indicators. Statistical analyses were conducted to test for...6 Coding _________________________________________________________________ 7 Statistical

UNAVCO Real-Time GNSS Positioning: High-Precision Static and Kinematic Testing of the Next Generation GNSS network.

NASA Astrophysics Data System (ADS)

Berglund, H. T.; Hodgkinson, K. M.; Blume, F.; Mencin, D.; Phillips, D. A.; Meertens, C. M.; Mattioli, G. S.

2014-12-01

The GAGE Facility, managed by UNAVCO, operates a real-time GNSS (RT-GNSS) network of ~450 stations. The majority of the streaming stations are part of the EarthScope Plate Boundary Observatory (PBO). Following community input from a real-time GNSS data products and formats meeting hosted by UNAVCO in Spring of 2011, UNAVCO now provides real-time PPP positions, and network solutions where practical, for all available stations using Trimble's PIVOT RTX server software and TrackRT. The UNAVCO real-time system has the potential to enhance our understanding of earthquakes, seismic wave propagation, volcanic eruptions, magmatic intrusions, movement of ice, landslides, and the dynamics of the atmosphere. Beyond the ever increasing applications in science and engineering, RT-GNSS has the potential to provide early warning of hazards to emergency managers, utilities, other infrastructure managers, first responders and others. Upgrades to the network include eight Trimble NetR9 GNSS receivers with GLONASS and receiver-based RTX capabilities and sixteen new co-located MEMS based accelerometers. These new capabilities will allow integration of GNSS and strong motion data to produce broad-spectrum waveforms improving Earthquake Early Warning systems. Controlled outdoor kinematic and static experiments provide a useful method for evaluating and comparing real-time systems. UNAVCO has developed a portable low-cost antenna actuator to characterize the kinematic performance of receiver- and server-based real-time positioning algorithms and identify system limitations. We have performed tests using controlled 1-d antenna motions and will present comparisons between these and other post-processed kinematic algorithms including GIPSY-OASIS and TRACK. In addition to kinematic testing, long-term static testing of Trimble's RTX service is ongoing at UNAVCO and will be used to characterize the stability of the position time-series produced by RTX. In addition, with the goal of characterizing stability and improving software and higher level products based on real-time and high frequency GNSS time series, we present an overview of the UNAVCO RT-GPS system, a comparison of the UNAVCO generated real-time, static and community data products, and an overview of available common data sets.

Text-based Analytics for Biosurveillance

DOE Office of Scientific and Technical Information (OSTI.GOV)

Charles, Lauren E.; Smith, William P.; Rounds, Jeremiah

The ability to prevent, mitigate, or control a biological threat depends on how quickly the threat is identified and characterized. Ensuring the timely delivery of data and analytics is an essential aspect of providing adequate situational awareness in the face of a disease outbreak. This chapter outlines an analytic pipeline for supporting an advanced early warning system that can integrate multiple data sources and provide situational awareness of potential and occurring disease situations. The pipeline, includes real-time automated data analysis founded on natural language processing (NLP), semantic concept matching, and machine learning techniques, to enrich content with metadata related tomore » biosurveillance. Online news articles are presented as an example use case for the pipeline, but the processes can be generalized to any textual data. In this chapter, the mechanics of a streaming pipeline are briefly discussed as well as the major steps required to provide targeted situational awareness. The text-based analytic pipeline includes various processing steps as well as identifying article relevance to biosurveillance (e.g., relevance algorithm) and article feature extraction (who, what, where, why, how, and when). The ability to prevent, mitigate, or control a biological threat depends on how quickly the threat is identified and characterized. Ensuring the timely delivery of data and analytics is an essential aspect of providing adequate situational awareness in the face of a disease outbreak. This chapter outlines an analytic pipeline for supporting an advanced early warning system that can integrate multiple data sources and provide situational awareness of potential and occurring disease situations. The pipeline, includes real-time automated data analysis founded on natural language processing (NLP), semantic concept matching, and machine learning techniques, to enrich content with metadata related to biosurveillance. Online news articles are presented as an example use case for the pipeline, but the processes can be generalized to any textual data. In this chapter, the mechanics of a streaming pipeline are briefly discussed as well as the major steps required to provide targeted situational awareness. The text-based analytic pipeline includes various processing steps as well as identifying article relevance to biosurveillance (e.g., relevance algorithm) and article feature extraction (who, what, where, why, how, and when).« less

Smokers’ and E-Cigarette Users’ Perceptions about E-Cigarette Warning Statements

PubMed Central

Wackowski, Olivia A.; Hammond, David; O’Connor, Richard J.; Strasser, Andrew A.; Delnevo, Cristine D.

2016-01-01

Cigarette warning labels are important sources of risk information, but warning research for other tobacco products is limited. This study aimed to gauge perceptions about warnings that may be used for e-cigarettes. We conducted six small focus groups in late 2014/early 2015 with adult current e-cigarette users and cigarette-only smokers. Participants rated and discussed their perceptions of six e-cigarette warning statements, and warnings in two existing Vuse and MarkTen e-cigarette ads. Participants were open to e-cigarette warnings and provided the strongest reactions to statements warning that e-liquid/e-vapor or e-cigarettes can be poisonous, contain toxins, or are “not a safe alternative to smoking”. However, many also noted that these statements were exaggerated, potentially misleading, and could scare smokers away from reducing their harm by switching to e-cigarettes. Opinions on the Food and Drug Administration’s proposed nicotine addiction warning and warnings that e-cigarettes had not been approved for smoking cessation or had unknown health effects were mixed. Participants perceived MarkTen’s advertisement warning to be stronger and more noticeable than Vuse’s. Care should be taken in developing e-cigarette warnings given their relative recentness and potential for harm reduction compared to other tobacco products. Additional research, including with varied audiences, would be instructive. PMID:27376310

Smart Roadside System for Driver Assistance and Safety Warnings: Framework and Applications

PubMed Central

Jang, Jeong Ah; Kim, Hyun Suk; Cho, Han Byeog

2011-01-01

The use of newly emerging sensor technologies in traditional roadway systems can provide real-time traffic services to drivers through Telematics and Intelligent Transport Systems (ITSs). This paper introduces a smart roadside system that utilizes various sensors for driver assistance and traffic safety warnings. This paper shows two road application models for a smart roadside system and sensors: a red-light violation warning system for signalized intersections, and a speed advisory system for highways. Evaluation results for the two services are then shown using a micro-simulation method. In the given real-time applications for drivers, the framework and certain algorithms produce a very efficient solution with respect to the roadway type features and sensor type use. PMID:22164025

Evaluation of the effects of implementing an electronic early warning score system: protocol for a stepped wedge study.

PubMed

Bonnici, Timothy; Gerry, Stephen; Wong, David; Knight, Julia; Watkinson, Peter

2016-02-09

An Early Warning Score is a clinical risk score based upon vital signs intended to aid recognition of patients in need of urgent medical attention. The use of an escalation of care policy based upon an Early Warning Score is mandated as the standard of practice in British hospitals. Electronic systems for recording vital sign observations and Early Warning Score calculation offer theoretical benefits over paper-based systems. However, the evidence for their clinical benefit is limited. Previous studies have shown inconsistent results. The majority have employed a "before and after" study design, which may be strongly confounded by simultaneously occurring events. This study aims to examine how the implementation of an electronic early warning score system, System for Notification and Documentation (SEND), affects the recognition of clinical deterioration occurring in hospitalised adult patients. This study is a non-randomised stepped wedge evaluation carried out across the four hospitals of the Oxford University Hospitals NHS Trust, comparing charting on paper and charting using SEND. We assume that more frequent monitoring of acutely ill patients is associated with better recognition of patient deterioration. The primary outcome measure is the time between a patient's first observations set with an Early Warning Score above the alerting threshold and their subsequent set of observations. Secondary outcome measures are in-hospital mortality, cardiac arrest and Intensive Care admission rates, hospital length of stay and system usability measured using the System Usability Scale. We will also measure Intensive Care length of stay, Intensive Care mortality, Acute Physiology and Chronic Health Evaluation (APACHE) II acute physiology score on admission, to examine whether the introduction of SEND has any effect on Intensive Care-related outcomes. The development of this protocol has been informed by guidance from the Agency for Healthcare Research and Quality (AHRQ) Health Information Technology Evaluation Toolkit and Delone and McLeans's Model of Information System Success. Our chosen trial design, a stepped wedge study, is well suited to the study of a phased roll out. The choice of primary endpoint is challenging. We have selected the time from the first triggering observation set to the subsequent observation set. This has the benefit of being easy to measure on both paper and electronic charting and having a straightforward interpretation. We have collected qualitative measures of system quality via a user questionnaire and organisational descriptors to help readers understand the context in which SEND has been implemented.

Enhancing Community Based Early Warning Systems in Nepal with Flood Forecasting Using Local and Global Models

NASA Astrophysics Data System (ADS)

Dugar, Sumit; Smith, Paul; Parajuli, Binod; Khanal, Sonu; Brown, Sarah; Gautam, Dilip; Bhandari, Dinanath; Gurung, Gehendra; Shakya, Puja; Kharbuja, RamGopal; Uprety, Madhab

2017-04-01

Operationalising effective Flood Early Warning Systems (EWS) in developing countries like Nepal poses numerous challenges, with complex topography and geology, sparse network of river and rainfall gauging stations and diverse socio-economic conditions. Despite these challenges, simple real-time monitoring based EWSs have been in place for the past decade. A key constraint of these simple systems is the very limited lead time for response - as little as 2-3 hours, especially for rivers originating from steep mountainous catchments. Efforts to increase lead time for early warning are focusing on imbedding forecasts into the existing early warning systems. In 2016, the Nepal Department of Hydrology and Meteorology (DHM) piloted an operational Probabilistic Flood Forecasting Model in major river basins across Nepal. This comprised a low data approach to forecast water levels, developed jointly through a research/practitioner partnership with Lancaster University and WaterNumbers (UK) and the International NGO Practical Action. Using Data-Based Mechanistic Modelling (DBM) techniques, the model assimilated rainfall and water levels to generate localised hourly flood predictions, which are presented as probabilistic forecasts, increasing lead times from 2-3 hours to 7-8 hours. The Nepal DHM has simultaneously started utilizing forecasts from the Global Flood Awareness System (GLoFAS) that provides streamflow predictions at the global scale based upon distributed hydrological simulations using numerical ensemble weather forecasts from the ECMWF (European Centre for Medium-Range Weather Forecasts). The aforementioned global and local models have already affected the approach to early warning in Nepal, being operational during the 2016 monsoon in the West Rapti basin in Western Nepal. On 24 July 2016, GLoFAS hydrological forecasts for the West Rapti indicated a sharp rise in river discharge above 1500 m3/sec (equivalent to the river warning level at 5 meters) with 53% probability of exceeding the Medium Level Alert in two days. Rainfall stations upstream of the West Rapti catchment recorded heavy rainfall on 26 July, and localized forecasts from the probabilistic model at 8 am suggested that the water level would cross a pre-determined warning level in the next 3 hours. The Flood Forecasting Section at DHM issued a flood advisory, and disseminated SMS flood alerts to more than 13,000 at-risk people residing along the floodplains. Water levels crossed the danger threshold (5.4 meters) at 11 am, peaking at 8.15 meters at 10 pm. Extension of the warning lead time from probabilistic forecasts was significant in minimising the risk to lives and livelihoods as communities gained extra time to prepare, evacuate and respond. Likewise, longer timescale forecasts from GLoFAS could be potentially linked with no-regret early actions leading to improved preparedness and emergency response. These forecasting tools have contributed to enhance the effectiveness and efficiency of existing community based systems, increasing the lead time for response. Nevertheless, extensive work is required on appropriate ways to interpret and disseminate probabilistic forecasts having longer (2-14 days) and shorter (3-5 hours) time horizon for operational deployment as there are numerous uncertainties associated with predictions.

An exploration of public knowledge of warning signs for cancer.

PubMed

Keeney, Sinead; McKenna, Hugh; Fleming, Paul; McIlfatrick, Sonja

2011-02-01

Warning signs of cancer have long been used as an effective way to summarise and communicate early indications of cancer to the public. Given the increasing global burden of cancer, the communication of these warning signs to the public is more important than ever before. This paper presents part of a larger study which explored the attitudes, knowledge and behaviours of people in mid-life towards cancer prevention. The focus of this paper is on the assessment of the knowledge of members of the public aged between 35 and 54 years of age. A questionnaire was administered to a representative sample of the population listing 17 warning signs of cancer. These included the correct warning signs and distracter signs. Respondents were asked to correctly identify the seven warning signs. Findings show that respondents could identify 4.8 cancer warning signs correctly. Analysis by demographics shows that being female, being older, having a higher level of educational attainment and being in a higher socio-economic group are predictors of better level of knowledge of cancer warning signs. Recommendations are proffered with regard to better targeting, clarification and communication of cancer warning signs. Copyright © 2010 Elsevier Ltd. All rights reserved.

Real-time earthquake source imaging: An offline test for the 2011 Tohoku earthquake

NASA Astrophysics Data System (ADS)

Zhang, Yong; Wang, Rongjiang; Zschau, Jochen; Parolai, Stefano; Dahm, Torsten

2014-05-01

In recent decades, great efforts have been expended in real-time seismology aiming at earthquake and tsunami early warning. One of the most important issues is the real-time assessment of earthquake rupture processes using near-field seismogeodetic networks. Currently, earthquake early warning systems are mostly based on the rapid estimate of P-wave magnitude, which contains generally large uncertainties and the known saturation problem. In the case of the 2011 Mw9.0 Tohoku earthquake, JMA (Japan Meteorological Agency) released the first warning of the event with M7.2 after 25 s. The following updates of the magnitude even decreased to M6.3-6.6. Finally, the magnitude estimate stabilized at M8.1 after about two minutes. This led consequently to the underestimated tsunami heights. By using the newly developed Iterative Deconvolution and Stacking (IDS) method for automatic source imaging, we demonstrate an offline test for the real-time analysis of the strong-motion and GPS seismograms of the 2011 Tohoku earthquake. The results show that we had been theoretically able to image the complex rupture process of the 2011 Tohoku earthquake automatically soon after or even during the rupture process. In general, what had happened on the fault could be robustly imaged with a time delay of about 30 s by using either the strong-motion (KiK-net) or the GPS (GEONET) real-time data. This implies that the new real-time source imaging technique is helpful to reduce false and missing warnings, and therefore should play an important role in future tsunami early warning and earthquake rapid response systems.

The Earthquake Early Warning System in Japan (Invited)

NASA Astrophysics Data System (ADS)

Mori, J. J.; Yamada, M.

2010-12-01

In Japan, the earthquake early warning system (Kinkyu Jishin Sokuhou in Japanese) maintained by the Japan Meterological Agency (JMA) has been in operation and sending pubic information since October 1, 2007. Messages have been broadcast on television and radio to warn of strong shaking to the public. The threshold for broadcasting a message is an estimated intensity of JMA 5 lower, which is approximately equivalent to MM VII to VIII. During the period from October 2007 through August 2010, messages have been sent 9 times for earthquakes of magnitude 5.2 to 7.0. There have been a few instances of significantly over-estimating or under-estimating the predicted shaking, but in general the performance of the system has been quite good. The quality of the detection system depends on the dense network of high-quality seismometers that cover the Japanese Islands. Consequently, the system works very well for events on or close to the 4 main islands, but there is more uncertainty for events near the smaller and more distant islands where the density of instrumentation is much less The Early Warning System is also tied to an extensive education program so that the public can react appropriately in the short amount of time given by the warning. There appears to be good public support in Japan, where people have become accustomed to a high level of fast information on a daily basis. There has also been development of a number of specific safety applications in schools and industry that work off the backbone information provided in the national system.

Awareness levels about breast cancer risk factors, early warning signs, and screening and therapeutic approaches among Iranian adult women: a large population based study using latent class analysis.

PubMed

Tazhibi, Mahdi; Feizi, Awat

2014-01-01

Breast cancer (BC) continues to be a major cause of morbidity and mortality among women throughout the world and in Iran. Lack of awareness and early detection program in developing country is a main reason for escalating the mortality. The present research was conducted to assess the Iranian women's level of knowledge about breast cancer risk factors, early warning signs, and therapeutic and screening approaches, and their correlated determinants. In a cross-sectional study, 2250 women before participating at a community based screening and public educational program in an institute of cancer research in Isfahan, Iran, in 2012 were investigated using a self-administered questionnaire about risk factors, early warning signs, and therapeutic and screening approaches of BC. Latent class regression as a comprehensive statistical method was used for evaluating the level of knowledge and its correlated determinants. Only 33.2%, 31.9%, 26.7%, and 35.8% of study participants had high awareness levels about screening approaches, risk factors, early warning signs and therapeutic modalities of breast cancer, respectively, and majority had poor to moderate knowledge levels. Most effective predictors of high level of awareness were higher educational qualifications, attending in screening and public educational programs, personal problem, and family history of BC, respectively. Results of current study indicated that the levels of awareness among study population about key elements of BC are low. These findings reenforce the continuing need for more BC education through conducting public and professional programs that are intended to raise awareness among younger, single women and those with low educational attainments and without family history.

Crowdsourced earthquake early warning.

PubMed

Minson, Sarah E; Brooks, Benjamin A; Glennie, Craig L; Murray, Jessica R; Langbein, John O; Owen, Susan E; Heaton, Thomas H; Iannucci, Robert A; Hauser, Darren L

2015-04-01

Earthquake early warning (EEW) can reduce harm to people and infrastructure from earthquakes and tsunamis, but it has not been implemented in most high earthquake-risk regions because of prohibitive cost. Common consumer devices such as smartphones contain low-cost versions of the sensors used in EEW. Although less accurate than scientific-grade instruments, these sensors are globally ubiquitous. Through controlled tests of consumer devices, simulation of an M w (moment magnitude) 7 earthquake on California's Hayward fault, and real data from the M w 9 Tohoku-oki earthquake, we demonstrate that EEW could be achieved via crowdsourcing.

Crowdsourced earthquake early warning

PubMed Central

Minson, Sarah E.; Brooks, Benjamin A.; Glennie, Craig L.; Murray, Jessica R.; Langbein, John O.; Owen, Susan E.; Heaton, Thomas H.; Iannucci, Robert A.; Hauser, Darren L.

2015-01-01

Earthquake early warning (EEW) can reduce harm to people and infrastructure from earthquakes and tsunamis, but it has not been implemented in most high earthquake-risk regions because of prohibitive cost. Common consumer devices such as smartphones contain low-cost versions of the sensors used in EEW. Although less accurate than scientific-grade instruments, these sensors are globally ubiquitous. Through controlled tests of consumer devices, simulation of an Mw (moment magnitude) 7 earthquake on California’s Hayward fault, and real data from the Mw 9 Tohoku-oki earthquake, we demonstrate that EEW could be achieved via crowdsourcing. PMID:26601167

Recruitment Early Warning System. Phase II. Volume 1. Research and Development of the Recruitment EWS (Early Warning System).

DTIC Science & Technology

1985-09-30

DRIVE. RESTON, VA 22091 NATO (703) 476-5500 +’ h, .*Je/_ft, MEND " " E I: v zRm MV WK= I 8 6D.’"-. by L Peter Greenston, Lawrence Goldberg, Sigurd... Lawrence Goldberg, Sigurd Hermansen, and Sherry Andrews September 1985 .’. This report was prepared under the Navy Manpower R&D Program of the Office of...February for seniors, and in the summer for graduates. The seasonal pattern is apparently even more differentiated in the Marine Corps. - 5.~ &,.p

An early warning system to forecast the close of the spring burning window from satellite-observed greenness.

PubMed

Pickell, Paul D; Coops, Nicholas C; Ferster, Colin J; Bater, Christopher W; Blouin, Karen D; Flannigan, Mike D; Zhang, Jinkai

2017-10-27

Spring represents the peak of human-caused wildfire events in populated boreal forests, resulting in catastrophic loss of property and human life. Human-caused wildfire risk is anticipated to increase in northern forests as fuels become drier, on average, under warming climate scenarios and as population density increases within formerly remote regions. We investigated springtime human-caused wildfire risk derived from satellite-observed vegetation greenness in the early part of the growing season, a period of increased ignition and wildfire spread potential from snow melt to vegetation green-up with the aim of developing an early warning wildfire risk system. The initial system was developed for 392,856 km 2 of forested lands with satellite observations available prior to the start of the official wildfire season and predicted peak human-caused wildfire activity with 10-day accuracy for 76% of wildfire-protected lands by March 22. The early warning system could have significant utility as a cost-effective solution for wildfire managers to prioritize the deployment of wildfire protection resources in wildfire-prone landscapes across boreal-dominated ecosystems of North America, Europe, and Russia using open access Earth observations.

Developing the Framework for an Early Warning System for Ebola based on Environmental Conditions

NASA Astrophysics Data System (ADS)

Dartevelle, Sebastien; Nguy-Robertson, Anthony; Bell, Jesse; Chretien, Jean-Paul

2017-04-01

The 2014-2016 Ebola outbreak in West Africa indicated that this lethal disease can become a National Security issue as it crossed boarders and taxed regional health care systems. Ebola symptoms are also similar to other endemic diseases. Thus, forewarning of its possible presence can alert local public health facilities and populations, and may thereby reduce response time. Early work by our group has identified local climate (e.g. temperature, precipitation) and vegetation health (e.g. remote sensing using normalized difference vegetation index, NDVI) variables as leading indicators to known historical Ebola outbreaks. The environmental stress placed on the system as it reaches a climatic tipping point provides optimal conditions for spillover of Ebola virus from the reservoir host (which is unknown but suspected to be bats) to humans. This work outlines a framework for an approach to provide early warning maps based on the present state of the environment. Time series data from Climate Forecast System ver. 2 and AVHRR and MODIS satellite sensors are the basis for the early warning models used. These maps can provide policy makers and local health care professionals timely information for disease surveillance and preparation for future Ebola outbreaks.

Birth, growth and progresses through the last twelve years of a regional scale landslide warning system

NASA Astrophysics Data System (ADS)

Fanti, Riccardo; Segoni, Samuele; Rosi, Ascanio; Lagomarsino, Daniela; Catani, Filippo

2017-04-01

SIGMA is a regional landslide warning system that operates in the Emilia Romagna region (Italy). In this work, we depict its birth and the continuous development process, still ongoing, after over a decade of operational employ. Traditionally, landslide rainfall thresholds are defined by the empirical correspondence between a rainfall database and a landslide database. However, in the early stages of the research, a complete catalogue of dated landslides was not available. Therefore, the prototypal version of SIGMA was based on rainfall thresholds defined by means of a statistical analysis performed over the rainfall time series. SIGMA was purposely designed to take into account both shallow and deep seated landslides and it was based on the hypothesis that anomalous or extreme values of accumulated rainfall are responsible for landslide triggering. The statistical distribution of the rainfall series was analyzed, and multiples of the standard deviation (σ) were used as thresholds to discriminate between ordinary and extraordinary rainfall events. In the warning system, the measured and the forecasted rainfall are compared with these thresholds. Since the response of slope stability to rainfall may be complex, SIGMA is based on a decision algorithm aimed at identifying short but exceptionally intense rainfalls and mild but exceptionally prolonged rains: while the former are commonly associated with shallow landslides, the latter are mainly associated with deep-seated landslides. In the first case, the rainfall threshold is defined by high σ values and short durations (i.e. a few days); in the second case, σ values are lower but the decision algorithm checks long durations (i.e. some months). The exact definition of "high" and "low" σ values and of "short" and "long" duration varied through time according as it was adjusted during the evolution of the model. Indeed, since 2005, a constant work was carried out to gather and organize newly available data (rainfall recordings and landslides occurred) and to use them to define more robust relationships between rainfalls and landslide triggering, with the final aim to increase the forecasting effectiveness of the warning system. The updated rainfall and landslide database were used to periodically perform a quantitative validation and to analyze the errors affecting the system forecasts. The errors characterization was used to implement a continuous process of updating and modification of SIGMA, that included: - Main model upgrades (generalization from a pilot test site to the whole Emilia Romagna region; calibration against well documented landslide events to define specific σ levels for each territorial units; definition of different alert levels according to the number of expected - Ordinary updates (periodically, the new landslide and rainfall data were used to re-calibrate the thresholds, taking into account a more robust sample). - Model tuning (set up of the optimal version of the decisional algorithm, including different definitions of "long" and "short" periods; selection of the optimal reference rain gauge for each Territorial Unit; modification of the boundaries of some territorial - Additional features (definition of a module that takes into account the effect of snow melt and snow accumulation; coupling with a landslide susceptibility model to improve the spatial accuracy of the model). - Various performance tests (including the comparison with alternate versions of SIGMA or with thresholds based on rainfall intensity and duration). This process has led to an evolution of the warning system and to a documented improvement of its forecasting effectiveness. Landslide forecasting at regional scale is a very complex task, but as time passes by and with the systematic gathering of new substantial data and the continuous progresses of research, uncertainties can be progressively reduced and a warning system can be set that increases its performances and reliability with time.

Inappropriate Alarm Rates and Driver Annoyance

DOT National Transportation Integrated Search

1996-02-01

Future in-vehicle crash avoidance warning systems will inevitably deliver : inappropriate alarms from time to time, caused for example, by situations where : algorithms have correctly identified an object but pose no threat or danger to : the driver....