Journal articles on the topic 'Integrated Early Warning Systems'
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Hürster, Walter, Thomas Wilbois, and Fernando Chaves. "An Integrated Systems Approach for Early Warning and Risk Management Systems." International Journal of Information Technologies and Systems Approach 3, no. 2 (July 2010): 46–56. http://dx.doi.org/10.4018/jitsa.2010070104.
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An integrated and interdisciplinary approach to Early Warning and Risk Management is described in this paper as well as the general technical implementation of Early Warning and Risk Management Systems. Based on this systems approach, a concept has been developed for the design of an Integrated System for Coastal Protection. In addition to this, as a prototype implementation of a modern environmental monitoring and surveillance system, a system for the Remote Monitoring of Nuclear Power Plants is presented here in more detail, including a Web Portal to allow for public access. The concept, the architectural design and the user interface of Early Warning and Risk Management Systems have to meet high demands. It is shown that only a close cooperation of all related disciplines and an integrated systems approach is able to fulfil the catalogue of requirements and to provide a suitable solution for environmental monitoring and surveillance, for early warning and for emergency management.
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Barla, M., and F. Antolini. "An integrated methodology for landslides’ early warning systems." Landslides 13, no. 2 (February 24, 2015): 215–28. http://dx.doi.org/10.1007/s10346-015-0563-8.
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Waidyanatha, Nuwan. "Towards a typology of integrated functional early warning systems." International Journal of Critical Infrastructures 6, no. 1 (2010): 31. http://dx.doi.org/10.1504/ijcis.2010.029575.
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Amato, Laura, Maria Dente, Paolo Calistri, and Silvia Declich. "Integrated Early Warning Surveillance: Achilles′ Heel of One Health?" Microorganisms 8, no. 1 (January 8, 2020): 84. http://dx.doi.org/10.3390/microorganisms8010084.
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Emerging and re-emerging infectious diseases and zoonoses indicate the importance of the One Health (OH) approach for early warning. At present, even when surveillance data are available, they are infrequently timeously shared between the health sectors. In the context of the MediLabSecure (MLS) Project, we investigated the collection of a set of surveillance indicators able to provide data for the implementation of integrated early warning systems in the 22 MLS countries of the Mediterranean, Black Sea and Sahel regions. We used an online questionnaire (covering vector, human, and animal sectors), focusing on seven relevant arboviruses, that was submitted to 110 officially appointed experts. Results showed that West Nile virus was perceived as the most relevant zoonotic pathogen, while Dengue virus was the most relevant non-zoonotic pathogen in the study area. Data collection of early warning indicators is in place at a different level for all the investigated pathogens and in almost all the MLS Countries. Further assessments on the reliability of the collection in place and on the feasibility of piloting an integrated early warning system for arbovirus could verify if integrated early warning really represents the Achilles’ heel of OH.
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Sirenko, Anatolii P. "The early warning systems about landslide hazards in Ukraine." Environmental safety and natural resources 37, no. 1 (April 2, 2021): 83–94. http://dx.doi.org/10.32347/2411-4049.2021.1.83-94.
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Early warning systems are an effective tool for preventing and mitigating the risks associated with the occurrences of various types of threats (including landslides). The paper presents and describes the concept and practical implementation of the new integrated methodology for early warning systems based on the integration of modern monitoring technologies and comprehensive numerical modeling of an object under study. Designing, testing and operation of monitoring systems of complex and unique construction objects have a lot of difficulties, need system knowledge in several spheres of science and engineering: construction, informational technologies, measuring instruments, systems and algorithms of data processing, programming etc. This information is known only to narrow range of highly qualified specialists that directly participated in designing and installing of the particular monitoring system at the particular construction object. The basic concept of Early Warning System installed on landslides is that the elements at risk, especially people being close from the dangerous area, must have sufficient time to evacuate, if an imminent collapse is expected. Therefore, an effective Early Warning System shall include such four main sets of actions: monitoring of the activity of the observed object, i.e. the data collection and transmission, as well as the equipment maintenance; the analysis and modeling of the observed and studied object; warning, i.e. the dissemination of simple and clear information about the observed object; the effective response of risk exposed elements; full understanding of risks. The examples of the practical application of the proposed integrated methodology to various construction projects and natural and technological systems are given, including 1) Central Livadia Landslide System and Livadia Palace; 2) a system for landslide hazard areas monitoring in the Kharkiv region; and 3) landslides Early Warning System using unmanned aerial vehicles as a specialized monitoring system for shearing deformations.
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Fathani, Teuku Faisal, Dwikorita Karnawati, and Wahyu Wilopo. "An integrated methodology to develop a standard for landslide early warning systems." Natural Hazards and Earth System Sciences 16, no. 9 (September 14, 2016): 2123–35. http://dx.doi.org/10.5194/nhess-16-2123-2016.
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Abstract. Landslides are one of the most widespread and commonly occurring natural hazards. In regions of high vulnerability, these complex hazards can cause significant negative social and economic impacts. Considering the worldwide susceptibility to landslides, it is necessary to establish a standard for early warning systems specific to landslide disaster risk reduction. This standard would provide guidance in conducting landslide detection, prediction, interpretation, and response. This paper proposes a new standard consisting of seven sub-systems for landslide early warning. These include risk assessment and mapping, dissemination and communication, establishment of the disaster preparedness and response team, development of an evacuation map, standardized operating procedures, installation of monitoring and warning services, and the building of local commitment to the operation and maintenance of the entire program. This paper details the global standard with an example of its application from Central Java, one of 20 landslide-prone provinces in Indonesia that have used this standard since 2012.
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Bhimjee, Diptes C. P. "Adaptive Early Warning Systems: An Axiomatic Approach." Journal of Central Banking Theory and Practice 11, no. 2 (April 30, 2022): 145–64. http://dx.doi.org/10.2478/jcbtp-2022-0017.
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Abstract The U.S. Subprime Crisis and the subsequent Great Recession have highlighted a renewed interest in the proper design and implementation of Early Warning Systems (E.W.S.), in order to help deter the onset of subsequent extreme financial events, through the implementation of adequate crisis detection mechanisms. The present article describes the Adaptive Early Warning Systems (A.E.W.S.) axiomatic approach, as a natural operational extension to E.W.S. testing. This novel protocol upholds the operational dimension of implementing an efficient holistic crisis detection mechanism, a domain which has been hitherto overlooked by the E.W.S. literature. The paper first describes the major axiomatic principles sustaining the A.E.W.S. protocol, which seek to establish universal principles in support of the said protocol. Second, the article also describes a basic universal template for an A.E.W.S. surveillance platform, which duly describes how multiple testing procedures can be integrated into a single crisis detection framework, while targeting multiple segments of the financial markets (such as the conventional and non-conventional segments of the financial markets). Third, the paper also describes the major advantages and disadvantages associated with the implementation of this novel protocol. It is hoped that the effective implementation of the A.E.W.S. protocol as a novel operational framework in the global macroprudential toolkit might help deter the onset of future extreme financial events, by enabling a greater cohesiveness in E.W.S.-related central banking procedures, as well as promoting a greater international central banking cooperation prior to and during financial distress episodes.
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Lendholt, Matthias. "Towards an Integrated Information Logistics for Multi Hazard Early Warning Systems." Open Environmental Engineering Journal 5, no. 1 (May 29, 2012): 27–43. http://dx.doi.org/10.2174/1874829501205010027.
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PL, Ortiz Bulto. "Cuban Approaches to Climate and Health Studies in Tropics Early Warning System and Learned Lessons." Virology & Immunology Journal 5, no. 3 (August 2, 2021): 1–7. http://dx.doi.org/10.23880/vij-16000282.
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Infectious diseases are sensitive to variations and climate change, causing impacts on health systems in the countries. Current climatic conditions favor the appearance of outbreaks and the circulation of new viruses such as SARS COV2. To alert such dangers, the creation of specialized warning systems for the health sector from climatic conditions is currently a global priority. This requires intense collaboration between medical and environmental communication, with new work approaches and methods for forecasting, using integrated climatic and epidemiological information. The objective of this publication is to show the advances and experience of Cuban research and projections, in terms of approaches and methodological procedures for the studies of the relationships between climate and health for forecasting purposes. An Early Warning System for infectious diseases and their causative agents was created. This warning system strengthens the health sector surveillance system for decision-making
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Lin, Pei-Yang. "A successful approach to earthquake early warning systems." Impact 2019, no. 9 (December 20, 2019): 18–20. http://dx.doi.org/10.21820/23987073.2019.9.18.
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The goal of an earthquake early warning system (EEW) is to identify where and when an earthquake has occurred and then warn those in danger. We think of earthquakes as happening instantaneously but from the detection of the initial event there is time until the effects are noticed. An early warning of even 6 to 19 seconds could allow sensitive infrastructure like factories or power plants to enact automated precautions, reducing damage and allowing for quicker recovery. Warnings of 30 seconds could prevent 95 per cent of potential mortalities in some large cities. It is exactly these crucial seconds that Lin is trying to provide. His team's work centres on developing an integrated EEW for Taiwan which, once proven effective, to help bring this system to other countries who live with the threat of seismic activity.<br/> The EEW developed by Lin works through detecting the seismic waves that earthquakes produce. When an earthquake strikes the ground shakes, this creates seismic waves that move through the earth. These waves, exactly like a ripple in a pond, move out from the epicentre. The first wave, known as the primary wave is low intensity and will not affect structures significantly. The secondary wave however arrives a bit later and causes the intense shaking, damage and casualties. "The goal of the system is to deploy onsite Earthquake Early Waring System(EEWS) to detect the seismic waves and provide warning for the neighborhoods," explains Lin. The regional detectors and EEWS can be deployed to monitor a whole country.<br/> For traditional regional EEWS,like Japan,once a wave is detected the information is sent to a central server which determines the epicentre of the quake, the magnitude of the earthquake and the peak ground acceleration, an important measure of earthquake intensity. "With these measurements the server can then predict where peak ground acceleration will exceed thresholds likely to cause damage and send the alarm," says Lin. The whole process takes only about 15 seconds, which is extremely valuable time for those further away from the epicentre, but because the wave can move up to 90km within these 15 seconds there is a blind spot of about 90km in the regional Earthquake Early Waring System. Lin points out that a blind spot of this size may be fine for some regions. "In Japan the epicentre of the typical hazardous earthquake is in the east sea bed about 100km from the shore but for Taiwan the epicentres are typically below a city. Therefore Lin has developed an integrated approach combining the regional EEW with onsite EEW. The onsite EEWS are installed in cities and monitor local seismic activity. They can predict peak ground acceleration for local area from the primary wave within 1 to 3 seconds and reduce the blind spot to 20 or 30km. Each system has its advantages and disadvantages but integration provides maximum coverage.
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Esposito, Marco, Lorenzo Palma, Alberto Belli, Luisiana Sabbatini, and Paola Pierleoni. "Recent Advances in Internet of Things Solutions for Early Warning Systems: A Review." Sensors 22, no. 6 (March 9, 2022): 2124. http://dx.doi.org/10.3390/s22062124.
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Natural disasters cause enormous damage and losses every year, both economic and in terms of human lives. It is essential to develop systems to predict disasters and to generate and disseminate timely warnings. Recently, technologies such as the Internet of Things solutions have been integrated into alert systems to provide an effective method to gather environmental data and produce alerts. This work reviews the literature regarding Internet of Things solutions in the field of Early Warning for different natural disasters: floods, earthquakes, tsunamis, and landslides. The aim of the paper is to describe the adopted IoT architectures, define the constraints and the requirements of an Early Warning system, and systematically determine which are the most used solutions in the four use cases examined. This review also highlights the main gaps in literature and provides suggestions to satisfy the requirements for each use case based on the articles and solutions reviewed, particularly stressing the advantages of integrating a Fog/Edge layer in the developed IoT architectures.
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Pumo, D., A. Francipane, F. Lo Conti, E. Arnone, P. Bitonto, F. Viola, G. La Loggia, and L. V. Noto. "The SESAMO early warning system for rainfall-triggered landslides." Journal of Hydroinformatics 18, no. 2 (November 17, 2015): 256–76. http://dx.doi.org/10.2166/hydro.2015.060.
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The development of Web-based information systems coupled with advanced monitoring systems could prove to be extremely useful in landslide risk management and mitigation. A new frontier in the field of rainfall-triggered landslides (RTLs) lies in the real-time modelling of the relationship between rainfall and slope stability; this requires an intensive monitoring of some key parameters that could be achieved through the use of modern and often low-cost technologies. This work describes an integrated information system for early warning of RTLs that has been deployed and tested, in a prototypal form, for an Italian pilot site. The core of the proposed system is a wireless sensor network collecting meteorological, hydrological and geotechnical data. Data provided by different sensors and transmitted to a Web-based platform are used by an opportunely designed artificial neural network performing a stability analysis in near real-time or in forecast modality. The system is able to predict whether and when landslides could occur, providing early warnings of potential slope failures. System infrastructure, designed on three interacting levels, encompasses a sensing level, integrating different Web-based sensors, a processing level, using Web standard interoperability services and specifically implemented algorithms, and, finally, a warning level, providing warning information through Web technologies.
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Zhu, Jiang. "Research on Integrated Management of Logistics Economy Based on Internet of Things." Wireless Communications and Mobile Computing 2022 (April 8, 2022): 1–7. http://dx.doi.org/10.1155/2022/5441954.
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In order to study the management and control ability of the government affairs 4.0 system on the logistics economic park and improve the data prediction and data early warning sensitivity of the economic management information system in the logistics economic park, this study recombs the basic model of the logistics economic park and puts forward the development model of the inward logistics economic park. Based on this model, a logistics economic big data system is built, and a data early warning model based on machine learning is designed. In the simulation verification, it is found that the original data demand of machine learning data early warning mode is smaller, the data sensitivity early warning cycle is longer, and the data early warning sensitivity is higher under the same conditions. Finally, it is considered that the machine learning linear programming algorithm model has high applicability in the government economic management process of inward logistics economic park.
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Sun, Fuyu, Hua Wang, and Jianping Zhou. "Research and development techniques for early-warning satellite systems using concurrent engineering." Concurrent Engineering 26, no. 3 (April 23, 2018): 215–30. http://dx.doi.org/10.1177/1063293x18768668.
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An early-warning satellite system is a complex project that requires the participation of many aerospace academies and scientific institutions. In terms of software programming, this study proposes a new simulation integrated management platform for the analysis of parallel and distributed systems. The platform facilitates the design and testing of both applications and architectures. To improve the efficiency of project development, new early-warning satellite systems are designed based on the simulation integrated management platform. In terms of project management, this study applies concurrent engineering theory to aerospace engineering and presents a method of collaborative project management. Finally, through a series of experiments, this study validates the simulation integrated management platform, models, and project management method. Furthermore, the causes of deviation and prevention methods are explained in detail. The proposed simulation platform, models, and project management method provide a foundation for further validations of autonomous technology in space attack–defense architecture research.
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Intrieri, Emanuele, Federica Bardi, Riccardo Fanti, Giovanni Gigli, Francesco Fidolini, Nicola Casagli, Sandra Costanzo, et al. "Big data managing in a landslide early warning system: experience from a ground-based interferometric radar application." Natural Hazards and Earth System Sciences 17, no. 10 (October 6, 2017): 1713–23. http://dx.doi.org/10.5194/nhess-17-1713-2017.
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Abstract. A big challenge in terms or landslide risk mitigation is represented by increasing the resiliency of society exposed to the risk. Among the possible strategies with which to reach this goal, there is the implementation of early warning systems. This paper describes a procedure to improve early warning activities in areas affected by high landslide risk, such as those classified as critical infrastructures for their central role in society. This research is part of the project LEWIS (Landslides Early Warning Integrated System): An Integrated System for Landslide Monitoring, Early Warning and Risk Mitigation along Lifelines. LEWIS is composed of a susceptibility assessment methodology providing information for single points and areal monitoring systems, a data transmission network and a data collecting and processing center (DCPC), where readings from all monitoring systems and mathematical models converge and which sets the basis for warning and intervention activities. The aim of this paper is to show how logistic issues linked to advanced monitoring techniques, such as big data transfer and storing, can be dealt with compatibly with an early warning system. Therefore, we focus on the interaction between an areal monitoring tool (a ground-based interferometric radar) and the DCPC. By converting complex data into ASCII strings and through appropriate data cropping and average, and by implementing an algorithm for line-of-sight correction, we managed to reduce the data daily output without compromising the capability for performing.
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Sättele, M., M. Bründl, and D. Straub. "Quantifying the effectiveness of early warning systems for natural hazards." Natural Hazards and Earth System Sciences 16, no. 1 (January 19, 2016): 149–66. http://dx.doi.org/10.5194/nhess-16-149-2016.
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Abstract. Early warning systems (EWSs) are increasingly applied as preventive measures within an integrated risk management approach for natural hazards. At present, common standards and detailed guidelines for the evaluation of their effectiveness are lacking. To support decision-makers in the identification of optimal risk mitigation measures, a three-step framework approach for the evaluation of EWSs is presented. The effectiveness is calculated in function of the technical and the inherent reliability of the EWS. The framework is applicable to automated and non-automated EWSs and combinations thereof. To address the specifics and needs of a wide variety of EWS designs, a classification of EWSs is provided, which focuses on the degree of automations encountered in varying EWSs. The framework and its implementation are illustrated through a series of example applications of EWS in an alpine environment.
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Sättele, M., M. Bründl, and D. Straub. "Quantifying the effectiveness of early warning systems for natural hazards." Natural Hazards and Earth System Sciences Discussions 3, no. 7 (July 30, 2015): 4479–526. http://dx.doi.org/10.5194/nhessd-3-4479-2015.
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Abstract. Early warning systems (EWS) are increasingly applied as preventive measures within an integrated risk management approach for natural hazards. At present, common standards and detailed guidelines for the evaluation of their effectiveness are lacking. To support decision-makers in the identification of optimal risk mitigation measures, a three-step framework approach for the evaluation of EWS is presented. The effectiveness is calculated in function of the technical and the inherent reliability of the EWS. The framework is applicable to automated and non-automated EWS and combinations thereof. To address the specifics and needs of a wide variety of EWS designs, a classification of EWS is provided, which focuses on the degree of automations encountered in varying EWS. The framework and its implementation are illustrated through a series of example applications of EWS in an alpine environment.
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TKALICH, PAVEL, MY HA DAO, and ENG SOON CHAN. "TSUNAMI PROPAGATION MODELING AND FORECASTING FOR EARLY WARNING SYSTEM." Journal of Earthquake and Tsunami 01, no. 01 (March 2007): 87–98. http://dx.doi.org/10.1142/s1793431107000067.
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After the devastating Indian Ocean 2004 Tsunami, coastal economies around the Indian Ocean have been reminded of the necessity to make well-coordinated efforts to deal with the tsunami problem. An integrated socio-technological infrastructure has to be built, with key tasks including advanced sensors, reliable communication networks, fast predictive algorithms, early warning systems, and educational outreach. This paper highlights the key features of a prediction system under development in Singapore in support of the early warning system being developed in the region.
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Scalici, Massimiliano, Monica Colamartino, Federica Spani, Lorenzo Traversetti, Tiziana Persichini, Maria Maisano, Salvatore Fasulo, and Marco Colasanti. "Integrated early warning systems in marine bivalves reveal detrimental alterations of coastal habitats." Hydrobiologia 847, no. 11 (April 30, 2020): 2573–85. http://dx.doi.org/10.1007/s10750-020-04275-1.
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Yu, Ran, Jiao Jiao Zhang, Hong Fei Xu, and Shen Jin. "An Integrated Electricity Information Security Monitoring Platform." Applied Mechanics and Materials 701-702 (December 2014): 947–51. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.947.
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An integrated electricity information security monitoring platform is presented, electrical information security integrated real-time monitoring of the state power applied information security systems, security equipment, network equipment, network boundaries, desktop computers, to improve information security monitoring, early warning, emergency response and defense capabilities to protect IT hardware, software and network security, which can guarantee continuous, reliable and efficient operation.
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Iordache, Mihai. "Systems on Board the NATO E-3A Platform." Scientific Bulletin 25, no. 2 (December 1, 2020): 103–6. http://dx.doi.org/10.2478/bsaft-2020-0014.
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AbstractThe article highlights the importance of the systems on board the NATO E-3A platform and how they influence the mission and work of the aircrew. The E-3A is an Airborne Early Warning & Control (AEW&C) system designed to provide early warning information to battle managers through surveillance of airborne and maritime objects, and to provide aircraft control capabilities. Even though the system is integrated on the old airframe of Boeing 707, in the past 35 years there have been numerous upgrades. The biggest upgrade of them all is the conversion of the entire fleet of aircraft to the NATO Mid-Term (NMT) configuration and then to “glass cockpit”. Now, the fleet is embarking upon a final modernization effort to extend its service life to 2035.
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Guo, Jie, and Wenyan Wang. "The Application of the Data Mining Method Based on Ensemble Learning to the Research on Early Warning of Financial Crisis in Small- and Medium-Sized Enterprises." Wireless Communications and Mobile Computing 2022 (July 19, 2022): 1–11. http://dx.doi.org/10.1155/2022/8443988.
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In the big data environment, the factors affecting the operation of small- and medium-sized enterprises are becoming more and more complex. In order to more accurately measure the financial crisis of small- and medium-sized enterprises, from the perspective of multisource information fusion, based on the traditional financial data reflecting the private information of enterprises, the public information data for measuring the macroeconomic and market environment are integrated and analyzed. Considering the multidimensional heterogeneity of multisource information data, BPNN, SVM, KNN, LOG, and MDA are introduced to build models, and ensemble learning is used to integrate the results of different early warning models to reduce the risk of inconsistent results from different models. The empirical results show that the early warning model integrating multisource information can improve the early warning accuracy. The research results show that the financial crisis early warning of small- and medium-sized enterprises should not only pay attention to financial indicators but also pay attention to the impact of corporate governance, industry, and the overall economic environment of the country; the accuracy of the model in this paper is significantly higher than that of the other models in terms of the accuracy of financial crisis early warning. It shows that the model in this paper can better predict the real financial status of small- and medium-sized enterprises.
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Martelloni, G., S. Segoni, D. Lagomarsino, R. Fanti, and F. Catani. "Snow accumulation/melting model (SAMM) for integrated use in regional scale landslide early warning systems." Hydrology and Earth System Sciences 17, no. 3 (March 20, 2013): 1229–40. http://dx.doi.org/10.5194/hess-17-1229-2013.
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Abstract. We propose a simple snow accumulation/melting model (SAMM) to be applied at regional scale in conjunction with landslide warning systems based on empirical rainfall thresholds. SAMM is based on two modules modelling the snow accumulation and the snowmelt processes. Each module is composed by two equations: a conservation of mass equation is solved to model snowpack thickness and an empirical equation for the snow density. The model depends on 13 empirical parameters, whose optimal values were defined with an optimisation algorithm (simplex flexible) using calibration measures of snowpack thickness. From an operational point of view, SAMM uses as input data only temperature and rainfall measurements, bringing about the additional benefit of a relatively easy implementation. After performing a cross validation and a comparison with two simpler temperature index models, we simulated an operational employment in a regional scale landslide early warning system (EWS) and we found that the EWS forecasting effectiveness was substantially improved when used in conjunction with SAMM.
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Amoah, Papa K., Christopher E. Sunday, Chukwudi Okoro, Jungjoon Ahn, Lin You, Dmitry Veksler, Joseph Kopanski, and Yaw Obeng. "(Invited) Towards the Physical Reliability of 3D-Integrated Systems: Broadband Dielectric Spectroscopic (BDS) Studies of Material Evolution and Reliability in Integrated Systems." ECS Transactions 109, no. 2 (September 30, 2022): 41–49. http://dx.doi.org/10.1149/10902.0041ecst.
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In this paper, we present an overview of our current research focus in developing non-destructive metrology for monitoring reliability issues in 3D- integrated electronic systems. We introduce a suite of non-destructive metrologies that can serve as early warning monitors for reliability issues. These Broadband Dielectric Spectroscopic (BDS) techniques are based on the application of high frequency microwaves (up to 40 GHz) to devices under various external stress, to probe impedance changes due to material and structural changes in integrated circuits. We illustrate the capabilities of the techniques with four case studies of potential reliability issues in 3D-IC interconnects.
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Wang, Wenqi, Yuhong Sun, and Jing Wu. "Environmental Warning System Based on the DPSIR Model: A Practical and Concise Method for Environmental Assessment." Sustainability 10, no. 6 (May 25, 2018): 1728. http://dx.doi.org/10.3390/su10061728.
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Though we are in urgent need of environmental warnings to slow environmental deterioration, currently, there is no internationally concise method for environmental warnings. In addition, the existing approaches do not combine the three aspects of ecology, resources, and environment. At the same time, the three elements of the environment (air, water, and soil) are separated in most environmental warning systems. Thus, the method this paper gives is an innovative attempt and aims to make environmental assessment more practical. This paper establishes the index system of an environmental early warning based on the Driving–Pressure–State–Influence–Response (DPSIR) model. The Analytic Hierarchy Process (AHP) method was used to determine the weights. Next, single and integrated index methods further assess the environmental warning state, in which the weighted summation method is used to summarize the data and results. The case of Tianjin is used to confirm the applicability of this method. In conclusion, the method in this paper is more well-behaved and, therefore, more suitable to assist cities in their environmental assessment.
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Sharma, Sudeep, S. Aruna, and K. Srinivasa Naik. "Design of Integrated Platform for Ventilation & Early Warning Systems using NI Lab VIEW." International Journal of Engineering Trends and Technology 40, no. 6 (October 25, 2016): 316–42. http://dx.doi.org/10.14445/22315381/ijett-v40p251.
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Martelloni, G., S. Segoni, D. Lagomarsino, R. Fanti, and F. Catani. "Snow Accumulation-Melting Model (SAMM) for integrated use in regional scale landslide early warning systems." Hydrology and Earth System Sciences Discussions 9, no. 8 (August 10, 2012): 9391–423. http://dx.doi.org/10.5194/hessd-9-9391-2012.
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Abstract. We propose a simple snow accumulation-melting model (SAMM) to be applied at the regional scale in conjunction with landslide warning systems based on empirical rainfall thresholds. SAMM follows an intermediate approach between physically based models and empirical temperature index models. It is based on two modules modelling the snow accumulation and the snowmelt processes. Each module is composed by two equations: a conservation of mass equation is solved to model snowpack thickness and an empirical equation for the snow density. The model depends on 13 empirical parameters, whose optimal values were defined with an optimization algorithm (simplex flexible) using calibration measures of snowpack thickness. From an operational point of view, SAMM uses as input data only temperature and rainfall measurements, bringing the additional advantage of a relatively easy implementation. The snow model validation gave satisfactory results; moreover we simulated an operational employment in a regional scale landslide early warning system (EWS) and found that the EWS forecasting effectiveness was substantially improved when used in conjunction with SAMM.
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Cera, Alessandra, Simona Ceschin, Floriano Del Grosso, Lorenzo Traversetti, and Massimiliano Scalici. "Correlating ecotoxicological early-warning systems to biotic indices to assess riverine teratogenic contamination." Marine and Freshwater Research 71, no. 8 (2020): 1033. http://dx.doi.org/10.1071/mf18471.
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We constructed a new ecotoxicological tool to test for the presence of teratogens in rivers by exploiting the regenerative capabilities of Hydra vulgaris because using living organisms is considered good practice in environmental monitoring for detecting detrimental pressures. Outputs on the Hydra assay were integrated with two biotic indices based on benthic invertebrates and macrophytes to evaluate whether the assay acts as a tool for detecting generalised freshwater conditions or as a biological test model revealing only the effects of dissolved teratogens. Several hypostomae of H. vulgaris were reared in waters collected in diverse southern central Latium rivers, where physiochemical descriptors were surveyed for environmental characterisation. The regeneration rate and amount of aberrations were evaluated to obtain a teratogenic risk index (TRI) score and then compared with the control. TRI calculations showed 4 sites (18.18%) in Class I (no risk), 15 sites (68.18%) in Class II (low risk), 2 sites (9.09%) in Class III (moderate risk) and 1 site (4.55%) in Class IV (high risk). No Class V sites (very high risk) were found. No significant correlations emerged among physicochemical parameters (except for PO43–), environmental descriptors, Simpson diversity indices and biotic indices and the TRI. These results promote an integrated approach to improving links between biological and ecological responses to anthropogenic impacts.
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Kohler, Monica D., Deborah E. Smith, Jennifer Andrews, Angela I. Chung, Renate Hartog, Ivan Henson, Douglas D. Given, Robert de Groot, and Stephen Guiwits. "Earthquake Early Warning ShakeAlert 2.0: Public Rollout." Seismological Research Letters 91, no. 3 (April 8, 2020): 1763–75. http://dx.doi.org/10.1785/0220190245.
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Abstract The ShakeAlert earthquake early warning system is designed to automatically identify and characterize the initiation and rupture evolution of large earthquakes, estimate the intensity of ground shaking that will result, and deliver alerts to people and systems that may experience shaking, prior to the occurrence of shaking at their location. It is configured to issue alerts to locations within the West Coast of the United States. In 2018, ShakeAlert 2.0 went live in a regional public test in the first phase of a general public rollout. The ShakeAlert system is now providing alerts to more than 60 institutional partners in the three states of the western United States where most of the nation’s earthquake risk is concentrated: California, Oregon, and Washington. The ShakeAlert 2.0 product for public alerting is a message containing a polygon enclosing a region predicted to experience modified Mercalli intensity (MMI) threshold levels that depend on the delivery method. Wireless Emergency Alerts are delivered for M 5+ earthquakes with expected shaking of MMI≥IV. For cell phone apps, the thresholds are M 4.5+ and MMI≥III. A polygon format alert is the easiest description for selective rebroadcasting mechanisms (e.g., cell towers) and is a requirement for some mass notification systems such as the Federal Emergency Management Agency’s Integrated Public Alert and Warning System. ShakeAlert 2.0 was tested using historic waveform data consisting of 60 M 3.5+ and 25 M 5.0+ earthquakes, in addition to other anomalous waveforms such as calibration signals. For the historic event test, the average M 5+ false alert and missed event rates for ShakeAlert 2.0 are 8% and 16%. The M 3.5+ false alert and missed event rates are 10% and 36.7%. Real-time performance metrics are also presented to assess how the system behaves in regions that are well-instrumented, sparsely instrumented, and for offshore earthquakes.
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Dong, Gui Hong, Jian Bo Xiong, Sheng Nian Wang, and Zhi Hong Fan. "Implementation of Corrosion Intelligent Monitoring and Durability early Warning Expert System for the Large Marine Environmental Engineering Concrete Structures." Advanced Materials Research 610-613 (December 2012): 1155–61. http://dx.doi.org/10.4028/www.scientific.net/amr.610-613.1155.
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To ensure 120 years design life of the large marine environmental engineering concrete structures in South China, and assess the status of the development of durability of concrete structure in real time w a research has been performed on embedded-non-destructive corrosion monitoring sensor systems, data acquisition and software integration, combined exposure test of concrete under environment in South China more than 30 years and laboratory test. It is a new type of technology for corrosion monitoring, assessment and early warning, which based on electrochemical principle, network transmission, expert software system, integrated the durability of the project full set of intelligent real-time monitoring and early warning expert systems.
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Alcántara-Ayala, Irasema, and Anthony Oliver-Smith. "Early Warning Systems: Lost in Translation or Late by Definition? A FORIN Approach." International Journal of Disaster Risk Science 10, no. 3 (September 2019): 317–31. http://dx.doi.org/10.1007/s13753-019-00231-3.
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Abstract Early warning systems (EWSs) are widely considered to be one of the most important mechanisms to prevent disasters around the globe. But as disasters continue to affect countries where EWSs have already been implemented, the striking disaster consequences have led us to reflect on the focus, architecture, and function of the warning systems. Since the 2004 Indian Ocean tsunami there has been a rapid rise in the promotion and use of EWSs to minimize disaster losses and damage. However, few researchers have addressed the question of their acceptability as an adaptive measure to the existing exposure conditions. EWSs are far more linked to emergency response and humanitarian crises and accepted technological interventions as solutions than they are to explicitly advance integrated analysis, disaster risk reduction, and policy making. A major flaw of EWSs is that the term “early” has been essentially used in reference to the speed of hazard onset, founded on a physicalist perspective that has encouraged a considerable dependence on technology. In this article we address the need for a clear understanding of the root causes and risk drivers of disaster risk creation, as advanced in the FORIN (forensic investigation of disasters) approach, as a prerequisite for the development of more articulated EWSs that could contribute to disaster risk reduction through policy making and practice, based on integrated and transdisciplinary management, in the interest of sustainable development, and human welfare and well-being.
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Fernández-Nóvoa, Diego, Orlando García-Feal, José González-Cao, Carlos de Gonzalo, José Antonio Rodríguez-Suárez, Carlos Ruiz del Portal, and Moncho Gómez-Gesteira. "MIDAS: A New Integrated Flood Early Warning System for the Miño River." Water 12, no. 9 (August 19, 2020): 2319. http://dx.doi.org/10.3390/w12092319.
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Early warning systems have become an essential tool to mitigate the impact of river floods, whose frequency and magnitude have increased during the last few decades as a consequence of climate change. In this context, the Miño River Flood Alert System (MIDAS) early warning system has been developed for the Miño River (Galicia, NW Spain), whose flood events have historically caused severe damage in urban areas and are expected to increase in intensity in the next decades. MIDAS is integrated by a hydrologic (HEC-HMS) and a hydraulic (Iber+) model using precipitation forecast as input data. The system runs automatically and is governed by a set of Python scripts. When any hazard is detected, an alert is issued by the system, including detailed hazards maps, to help decision makers to take precise and effective mitigation measures. Statistical analysis supports the accuracy of hydrologic and hydraulic modules implemented to forecast river flow and flooded critical areas during the analyzed period of time, including some of the most extreme events registered in the Miño River. In fact, MIDAS has proven to be capable of predicting most of the alert situations occurred during the study period, showing its capability to anticipate risk situations.
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Ferrarin, Christian, Andrea Valentini, Martin Vodopivec, Dijana Klaric, Giovanni Massaro, Marco Bajo, Francesca De Pascalis, et al. "Integrated sea storm management strategy: the 29 October 2018 event in the Adriatic Sea." Natural Hazards and Earth System Sciences 20, no. 1 (January 13, 2020): 73–93. http://dx.doi.org/10.5194/nhess-20-73-2020.
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Abstract. Addressing coastal risks related to sea storms requires an integrative approach which combines monitoring stations, forecasting models, early warning systems, and coastal management and planning. Such great effort is sometimes possible only through transnational cooperation, which becomes thus vital to face, effectively and promptly, the marine events which are responsible for damage impacting the environment and citizens' life. Here we present a shared and interoperable system to allow a better exchange of and elaboration on information related to sea storms among countries. The proposed integrated web system (IWS) is a combination of a common data system for sharing ocean observations and forecasts, a multi-model ensemble system, a geoportal, and interactive geo-visualisation tools to make results available to the general public. The multi-model ensemble mean and spread for sea level height and wave characteristics are used to describe three different sea condition scenarios. The IWS is designed to provide sea state information required for issuing coastal risk alerts over the analysed region as well as for being easily integrated into existing local early warning systems. This study describes the application of the developed system to the exceptional storm event of 29 October 2018 that caused severe flooding and damage to coastal infrastructure in the Adriatic Sea. The forecasted ensemble products were successfully compared with in situ observations. The hazards estimated by integrating IWS results in existing early warning systems were confirmed by documented storm impacts along the coast of Slovenia, Emilia-Romagna and the city of Venice. For the investigated event, the most severe simulated scenario results provide a realistic and conservative estimation of the peak storm conditions to be used in coastal risk management.
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Nikonovas, Tadas, Allan Spessa, Stefan H. Doerr, Gareth D. Clay, and Symon Mezbahuddin. "ProbFire: a probabilistic fire early warning system for Indonesia." Natural Hazards and Earth System Sciences 22, no. 2 (February 4, 2022): 303–22. http://dx.doi.org/10.5194/nhess-22-303-2022.
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Abstract. Recurrent extreme landscape fire episodes associated with drought events in Indonesia pose severe environmental, societal and economic threats. The ability to predict severe fire episodes months in advance would enable relevant agencies and communities to more effectively initiate fire-preventative measures and mitigate fire impacts. While dynamic seasonal climate predictions are increasingly skilful at predicting fire-favourable conditions months in advance in Indonesia, there is little evidence that such information is widely used yet by decision makers. In this study, we move beyond forecasting fire risk based on drought predictions at seasonal timescales and (i) develop a probabilistic early fire warning system for Indonesia (ProbFire) based on a multilayer perceptron model using ECMWF SEAS5 (fifth-generation seasonal forecasting system) dynamic climate forecasts together with forest cover, peatland extent and active-fire datasets that can be operated on a standard computer; (ii) benchmark the performance of this new system for the 2002–2019 period; and (iii) evaluate the potential economic benefit of such integrated forecasts for Indonesia. ProbFire's event probability predictions outperformed climatology-only based fire predictions at 2- to 4-month lead times in south Kalimantan, south Sumatra and south Papua. In central Sumatra, an improvement was observed only at a 0-month lead time, while in west Kalimantan seasonal predictions did not offer any additional benefit over climatology-only-based predictions. We (i) find that seasonal climate forecasts coupled with the fire probability prediction model confer substantial benefits to a wide range of stakeholders involved in fire management in Indonesia and (ii) provide a blueprint for future operational fire warning systems that integrate climate predictions with non-climate features.
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Wang, Jun, Yongfeng Fu, and Xiaokang Yang. "An integrated system for building structural health monitoring and early warning based on an Internet of things approach." International Journal of Distributed Sensor Networks 13, no. 1 (January 2017): 155014771668910. http://dx.doi.org/10.1177/1550147716689101.
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The intelligent security monitoring of buildings and their surroundings has become increasingly crucial as the number of high-rise buildings increases. Building structural health monitoring and early warning technology are key components of building safety, the implementation of which remains challenging, and the Internet of things approach provides a new technical measure for addressing this challenge. This article presents a novel integrated information system that combines Internet of things, building information management, early warning system, and cloud services. Specifically, the system involves an intelligent data box with enhanced connectivity and exchangeability for accessing and integrating the data obtained from distributed heterogeneous sensing devices. An extensible markup language (XML)–based uniform data parsing model is proposed to abstract the various message formats of heterogeneous devices to ensure data integration. The proposed Internet of things–based integrated information system structure was applied for monitoring an actual pit excavation engineering site. Three early warning levels were implemented according to rules based on the threshold value, which determined the specific safety personnel to be notified. The proposed Internet of things–based integrated information system is demonstrated to improve the effectiveness of monitoring processes and decision making in construction informatics applications. Our work highlights the crucial importance of a systematic approach toward integrated information systems for effective information collection and structural health monitoring.
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Feng, Lin, Jian Wang, Ye Chen, and Chao Ding. "Detection and Early Warning of Toxic Gases Based on Semiconductor Wireless Sensors." Journal of Sensors 2021 (November 26, 2021): 1–11. http://dx.doi.org/10.1155/2021/6988676.
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This paper studies a semiconductor wireless sensor system, which is composed of a semiconductor wireless sensor sampling circuit, gas-sensitive signal alarm and wireless transmitting circuit, and wireless radio frequency signal receiving circuit. The system is suitable for wireless monitoring of hydrogen fluoride gas in chemical plants. The hydrogen fluoride gas sensor is designed, integrated, and classified according to the polarity and size of the sensor output signal. The signal processing circuit of the sensor output signal is made with an integrated design. This paper developed a simulation experimental system for the wireless monitoring network characteristics of toxic hydrogen fluoride gas and completed the monitoring system’s sensor characteristic calibration and accuracy comparison simulation experiment, the communication distance test experiment of the communication system, and the research experiment on the influence of environmental humidity on the sensor characteristics of the monitoring system. In terms of software, the workflow of network nodes has been optimized. Since the structure of the wireless sensor network is not exactly the same in different application fields, the toxic gas monitoring system based on wireless sensor networks must focus on extending the network’s life cycle. Without affecting the normal operation of the system, distributed compressed sensing can greatly extend the service life of the system. Therefore, this subject combines the compressed sensing technology developed in recent years with the air monitoring system for the processing of transmission data, in order to achieve the purpose of further reducing the energy consumption of the system. The simulation experiment demonstrated that the lmF neural network combined with gas sensor array technology can realize qualitative identification, quantitative analysis of single gas, and quantitative analysis of mixed combustible gas. The research work in this area also provides a new way to further combine the miniature hydrogen fluoride gas sensor unit with sensor technology, integrate the hydrogen fluoride gas sensor unit and the electronic tag, and expand the wireless application of the gas sensor.
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Ali, Abdullah, Iddam Hairully Umam, Hesti Heningtyas, Rindita Charolidya, Bertha Sanditya, Azhari Putri Cempaka, Teguh Setyawan, and Kiki Ali. "Pengembangan Sistem Peringatan Dini Cuaca Ekstrem Terintegrasi Berbasis Y-Model Webgis Development Methodology (Y-WDM)." Jurnal Geografi, Edukasi dan Lingkungan (JGEL) 6, no. 2 (July 26, 2022): 87–100. http://dx.doi.org/10.22236/jgel.v6i2.8222.
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The incidence of hydrometeorological disasters in Indonesia is in the largest percentage for the last 10 years. The development of an integrated extreme weather early warning system is one of the initial processes in a series of hydrometeorological disaster mitigation efforts. Data models, observation and remote sensing become the database in early warning of extreme weather, the officers who carry out early detection of hazards so that they have sufficient waiting time to prepare an effective emergency response plan. In this study, an early warning system was built by implementing the Y-Model WebGIS Development Methodology (YWDM) which is a combination of geographic information systems and web-based application development context. Spatial data processing is carried out using the python programming language, including radar and weather satellite data, surface observation, lightning detection, storm detection and identification, and nowcasting models. Web GIS visualization is built by using JavaScript and php programming language, database management managed using PostgreSQL. The results of the implementation are in the form of a SPACE (Spatial Based Extreme Weather Warning System) framework which Sidarma-Nowcast is a system within the framework. The development of this system shows an increase and the level of improvement in the process of producing and disseminating early warning information.
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Hofmann, Julian, and Holger Schüttrumpf. "Risk-Based and Hydrodynamic Pluvial Flood Forecasts in Real Time." Water 12, no. 7 (July 2, 2020): 1895. http://dx.doi.org/10.3390/w12071895.
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The effective forecast and warning of pluvial flooding in real time is one of the key elements and remaining challenges of an integrated urban flood risk management. This paper presents a new methodology for integrating risk-based solutions and 2D hydrodynamic models into the early warning process. Whereas existing hydrodynamic forecasting methods are based on rigid systems with extremely high computational demands, the proposed framework builds on a multi-model concept allowing the use of standard computer systems. As a key component, a pluvial flood alarm operator (PFA-Operator) is developed for selecting and controlling affected urban subcatchment models. By distributed computing of hydrologic independent models, the framework overcomes the issue of high computational times of hydrodynamic simulations. The PFA-Operator issues warnings and flood forecasts based on a two-step process: (1) impact-based rainfall thresholds for flood hotspots and (2) hydrodynamic real-time simulations of affected urban subcatchments models. Based on the open-source development software Qt, the system can be equipped with interchangeable modules and hydrodynamic software while building on the preliminary results of flood risk analysis. The framework was tested using a historic pluvial flood event in the city of Aachen, Germany. Results indicate the high efficiency and adaptability of the proposed system for operational warning systems in terms of both accuracy and computation time.
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Pozzi, Will, Justin Sheffield, Robert Stefanski, Douglas Cripe, Roger Pulwarty, Jürgen V. Vogt, Richard R. Heim, et al. "Toward Global Drought Early Warning Capability: Expanding International Cooperation for the Development of a Framework for Monitoring and Forecasting." Bulletin of the American Meteorological Society 94, no. 6 (June 1, 2013): 776–85. http://dx.doi.org/10.1175/bams-d-11-00176.1.
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Drought is a global problem that has far-reaching impacts, especially on vulnerable populations in developing regions. This paper highlights the need for a Global Drought Early Warning System (GDEWS), the elements that constitute its underlying framework (GDEWF), and the recent progress made toward its development. Many countries lack drought monitoring systems, as well as the capacity to respond via appropriate political, institutional, and technological frameworks, and these have inhibited the development of integrated drought management plans or early warning systems. The GDEWS will provide a source of drought tools and products via the GDEWF for countries and regions to develop tailored drought early warning systems for their own users. A key goal of a GDEWS is to maximize the lead time for early warning, allowing drought managers and disaster coordinators more time to put mitigation measures in place to reduce the vulnerability to drought. To address this, the GDEWF will take both a top-down approach to provide global realtime drought monitoring and seasonal forecasting, and a bottom-up approach that builds upon existing national and regional systems to provide continental-to-global coverage. A number of challenges must be overcome, however, before a GDEWS can become a reality, including the lack of in situ measurement networks and modest seasonal forecast skill in many regions, and the lack of infrastructure to translate data into useable information. A set of international partners, through a series of recent workshops and evolving collaborations, has made progress toward meeting these challenges and developing a global system.
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Capan, Muge, Stephen Hoover, Kristen E. Miller, Carmen Pal, Justin M. Glasgow, Eric V. Jackson, and Ryan C. Arnold. "Data-driven approach to Early Warning Score-based alert management." BMJ Open Quality 7, no. 3 (August 2018): e000088. http://dx.doi.org/10.1136/bmjoq-2017-000088.
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BackgroundIncreasing adoption of electronic health records (EHRs) with integrated alerting systems is a key initiative for improving patient safety. Considering the variety of dynamically changing clinical information, it remains a challenge to design EHR-driven alerting systems that notify the right providers for the right patient at the right time while managing alert burden. The objective of this study is to proactively develop and evaluate a systematic alert-generating approach as part of the implementation of an Early Warning Score (EWS) at the study hospitals.MethodsWe quantified the impact of an EWS-based clinical alert system on quantity and frequency of alerts using three different alert algorithms consisting of a set of criteria for triggering and muting alerts when certain criteria are satisfied. We used retrospectively collected EHRs data from December 2015 to July 2016 in three units at the study hospitals including general medical, acute care for the elderly and patients with heart failure.ResultsWe compared the alert-generating algorithms by opportunity of early recognition of clinical deterioration while proactively estimating alert burden at a unit and patient level. Results highlighted the dependency of the number and frequency of alerts generated on the care location severity and patient characteristics.ConclusionEWS-based alert algorithms have the potential to facilitate appropriate alert management prior to integration into clinical practice. By comparing different algorithms with regard to the alert frequency and potential early detection of physiological deterioration as key patient safety opportunities, findings from this study highlight the need for alert systems tailored to patient and care location needs, and inform alternative EWS-based alert deployment strategies to enhance patient safety.
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Li, Fang Yuan, and Pei Feng Wu. "Accidental Injury Detecting and Warning System for the Maglev Bridge." Advanced Materials Research 446-449 (January 2012): 3517–22. http://dx.doi.org/10.4028/www.scientific.net/amr.446-449.3517.
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There are many roads crossed the bridge of the Maglev train. Due to overheight or irregular vehicles may bump or scratch the bridge, damage the electronic lines, or even damage the bridge, it is necessary to monitor these vehicles for the particularity of the Maglev transport. According to the possibility of different injury, this paper developed corresponding recognition modes and methods, discussed several early-warning systems and signal transmission mode. After compared some plans, feasible method was tested. After finished the system design and equipment processing and test, the final testing program was verified using the integrated early-warning system according to the field situation, proved the system reliability, sensitivity. The next step research was suggested.
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Moisès, Deolfa Josè, and Olivia Kunguma. "Strengthening Namibia’s Flood Early Warning System through a Critical Gap Analysis." Sustainability 15, no. 1 (December 28, 2022): 524. http://dx.doi.org/10.3390/su15010524.
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Floods are considered leading hydrometeorological disasters, which are increasing in frequency, intensity and complexity with the evolution of climate change. Their associated impacts have detrimental and often prolonged implications for humankind, especially communities heavily reliant on the natural environment. The development and implementation of effective flood early warning systems (FEWSs) can serve to enhance coping strategies and strengthen the adaptive capacities of target communities while simultaneously minimising flood risks. However, shortcomings related to the lack of information on the operationalisation of these systems, the technical and financial requirements, the challenges faced and the directives related to their implementation have persisted, subverting risk reduction efforts at the grassroots level. Using data from key informant interviews and focus group discussions, this study employed a systematic analysis of the official Namibian flood early warning system based on the system’s implementation in Kabbe, Namibia. The study results revealed a need for significant changes across all system components as the FEWS follows a top-down, disintegrated and response-driven approach. Roles are undefined among institutions; funding is inadequate; and community risk perceptions, coping capacities and participation are disregarded. Based on the study findings, the researchers recommend significant changes in the design and application of the system, urging practitioners to recognise FEWSs as the continuous and integrated tools that they are.
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Nof, Ran N., and Ittai Kurzon. "TRUAA—Earthquake Early Warning System for Israel: Implementation and Current Status." Seismological Research Letters 92, no. 1 (November 4, 2020): 325–41. http://dx.doi.org/10.1785/0220200176.
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Abstract Following a governmental decision to build a national earthquake early warning system (EEWS) named TRUAA, the Geological Survey of Israel has upgraded the national Israeli Seismic Network with more than 100 stations countrywide. The stations are spread mainly along the main hazardous fault systems of the Dead Sea and Carmel-Zfira, which potentially may produce Mw 7.5 earthquakes. Currently the system is shifting from the deployment phase into a testing phase in which the earthquake point-source integrated code (EPIC) EEW algorithm is used. During the deployment phase, real-time performance of the EEW algorithm has steadily improved, with alert delays (span between origin time and EEW alert time) reduced down to 3 s in some cases. We present an overview of TRUAA, the performance of EPIC during the deployment phase and during playbacks of historic events, as well as our suggested alert approach for Israel.
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Peng, Chaoyong, Xiaoyi Zhu, Jiansi Yang, Bing Xue, and Yang Chen. "Development of an integrated onsite earthquake early warning system and test deployment in Zhaotong, China." Computers & Geosciences 56 (July 2013): 170–77. http://dx.doi.org/10.1016/j.cageo.2013.03.018.
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Nasher, Samar, Rima Alusfi, Rula Taher, Abdualqawi Qasira, Abdualwakeel Alsameay, and Yasser Ghaleb. "Electronic Integrated Disease Early Warning System Surveillance System Evaluation, Sana'a Capital, Yemen, 2021." Iproceedings 8, no. 1 (February 7, 2022): e36554. http://dx.doi.org/10.2196/36554.
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Background The Electronic Integrated Disease Early Warning System (eIDEWS) is an essential system; it contributes to the better prevention and management of epidemics. Through the collection of complete, accurate, and timely data, countries are able to determine the priorities for suitable interventions that save the lives of communities. Regardless of the conflict in Yemen, the system is still functioning and is expanding to be the most effective epidemiological surveillance program. Objective We aimed to determine the usefulness of the eIDEWS, assess its performance, and identify the strengths and weaknesses of its implementation. Methods The usefulness and performance attributes of the eIDEWS were evaluated using the Centers for Disease Control and Prevention’s updated guidelines for evaluating public health surveillance systems. The evaluation was carried out in Sana’a capital from January to March 2021 by interviewing 25 stakeholders at 3 levels—the central, governorate, and health district levels—and using a semistructured questionnaire. Attributes of the system were ranked as poor (<60%), average (60% to <80%), good (80% to <90%), and excellent (≥90%) on the basis of indicators to calculate the final scores. Results The eIDEWS’ overall usefulness and performance score was 90%—an excellent rank. The mean score of system attributes was 100% for acceptability, completeness, and timeliness. The flexibility was good (83%), since the change in reporting method was applied difficultly. The system depends completely on foreign funds; thus, the system’s stability was average (75%). However, the eIDEWS was expanded recently to add new health facilities; its representativeness was average (76%). Conclusions The system is working effectively at evaluated sites. The overall system performance was excellent; however, flexibility and stability were good due to the negative adaptation of the system with regard to the reporting method and the absence of other fund resources. Therefore, evaluating the newly upgraded system, strengthening its stability by finding other supporting resources, and further expanding coverage to include all public and private health care facilities are recommended.
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Fang, Shifeng, Lida Xu, Yunqiang Zhu, Yongqiang Liu, Zhihui Liu, Huan Pei, Jianwu Yan, and Huifang Zhang. "An integrated information system for snowmelt flood early-warning based on internet of things." Information Systems Frontiers 17, no. 2 (October 9, 2013): 321–35. http://dx.doi.org/10.1007/s10796-013-9466-1.
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Hofmann, Julian, and Holger Schüttrumpf. "Risk-Based Early Warning System for Pluvial Flash Floods: Approaches and Foundations." Geosciences 9, no. 3 (March 14, 2019): 127. http://dx.doi.org/10.3390/geosciences9030127.
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In times of increasing weather extremes and expanding vulnerable cities, a significant risk to civilian security is posed by heavy rainfall induced flash floods. In contrast to river floods, pluvial flash floods can occur anytime, anywhere and vary enormously due to both terrain and climate factors. Current early warning systems (EWS) are based largely on measuring rainfall intensity or monitoring water levels, whereby the real danger due to urban torrential floods is just as insufficiently considered as the vulnerability of the physical infrastructure. For this reason, this article presents a concept for a risk-based EWS as one integral component of a multi-functional pluvial flood information system (MPFIS). Taking both the pluvial flood hazard as well as the damage potential into account, the EWS identifies the urban areas particularly affected by a forecasted heavy rainfall event and issues object-precise warnings in real-time. Further, the MPFIS performs a georeferenced documentation of occurred events as well as a systematic risk analysis, which at the same time forms the foundation of the proposed EWS. Based on a case study in the German city of Aachen and the event of 29 May 2018, the operation principle of the integrated information system is illustrated.
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Tran Duc Tan. "DEVELOPMENT OF A SMART OCEAN RADIATION MONITORING SYSTEM." Journal of Military Science and Technology, no. 75A (November 11, 2021): 38–45. http://dx.doi.org/10.54939/1859-1043.j.mst.75a.2021.38-45.
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Ocean radiation monitoring systems (ORMSs) are an essential component in the radiation early warning network that monitors radiation exposure and estimates radioactive propagation induced by nuclear activities or nuclear accidents in the sea. Numerous systems have been developed and installed in the radiation warning network in different countries. However, there is not any similar product that has been studied and developed in Vietnam. This paper presents a complete process in designing and manufacturing a marine buoy integrated with a radiation sensor. The radiation detector can measure both dose rate and radiological spectrum. The ORMS also combines multimodal data transmission and various programmed software for data processing, signal transmission, and system control. Therefore, the proposed configuration system has potential application in terms of performance and maintenance.
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Krebs, Verena, Till Quadflieg, Christian Grimm, Max Schwab, and Holger Schüttrumpf. "DEVELOPMENT OF A SENSOR-BASED DIKE MONITORING SYSTEM FOR COASTAL DIKES." Coastal Engineering Proceedings, no. 35 (June 23, 2017): 35. http://dx.doi.org/10.9753/icce.v35.structures.35.
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Sea and estuarine dikes are one of the most important structures when it comes to coastal protection in Germany. Therefore, the implementation of sea dike monitoring systems is of great value to improve the reliability of flood protection. The interdisciplinary research project, EarlyDike, attempts to develop an innovative early warning system, which works not only based on water level forecasts, but also considers wave load, improved storm surge monitoring, and inner dike conditions. The implementation of a web-based Geo-Portal, which integrates the information generated by sensors and numerical simulations, enables decision makers to access reliable real-time data. Thereby, the intended sensor- and risk-based early warning system allows in time warnings and improves present disaster prevention and management. Objective of this paper is to depict the integration of sensor-based dike monitoring within the project.
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Zhang, Yiqi, and Changxu Wu. "Modeling the Effects of Warning Lead Time, Warning Reliability and Warning Style on Human Performance Under Connected Vehicle Settings." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (September 2018): 701. http://dx.doi.org/10.1177/1541931218621158.
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Deaths and injuries resulted from traffic accidents is still a major public health problem. Recent advances in connected vehicle technology support a connected driving environment in which vehicles are enabled to communicate with each other and with roadside infrastructures via Dedicated Short Range Communication (DSRC). Connected vehicle safety applications supported by this technology allow drivers to learn about the traffic situations out of their sight and ahead of time so that drivers are warned early enough to make proper responses. As the connected vehicle systems (CVS) are designed with an aim to improving driver safety, the effectiveness of the CVS can not be achieved without drivers making proper responses in responding to the wireless warnings. Therefore, it is essential to understand and model the mechanism for human processing and responding to warnings from connected vehicle systems, and apply the driver model to optimize the design the CVS at the interface level and the communication level. Queuing Network-Model Human Processor (QN-MHP) is a computational framework that integrates three discrete serial stages of human information processing (i.e., perceptual, cognitive, and motor processing) into three continuous subnetworks. Each subnetwork is constructed of multiple servers and links among these servers. Each individual server is an abstraction of a brain area with specific functions, and links among servers represent neural pathways among functional brain areas. The neurological processing of stimuli is illustrated in the transformation of entities passing through routes in QN-MHP. Since this architecture was established, QN-MHP has been applied to quantify various aspects of aspects of driver behavior and performance, including speed control (Bi & Liu, 2009; Zhao & Wu, 2013b), lateral control (Bi et al., 2012; Bi et al., 2013), driver distraction (Bi et al., 2012; Fuller, Reed & Liu, 2012; Liu, Feyen & Tsimhoni, 2006), and driver workload (Wu & Liu, 2007; Wu et al., 2008). Most of the driver model built upon QN-MHP focused on the modeling of driver performance in normal driving situation. In a previous work of authors, a mathematical model was developed to predict the effects of warning loudness, word choice, and lead time on drivers’ warning reaction time (Zhang, Wu, & Wan, 2016). The current research focused on the development of a mathematical model based on QN-MHP to quantify and predict driver performance in responding to warnings from connected vehicle systems, including warning response time and the selection of warning response type. The model also quantified the effects of important warning characteristics in connected vehicle systems, including warning reliability, warning lead time, and speech warning style. The model was validated via an experimental study indicating its good predictability of driver behavior and performance in connected vehicle systems. In particular, the model was able to explain 68.83% of the warning response type in the initial trial of the experiment with a root mean square error (RMSE) of 0.18. By adding the warning effect on the probability of a response type through trials, the model was able to explain 65.13% of the warning response type in the initial trial of the experiment with a root mean square error (RMSE) of 0.16. In terms of warning response time, the model prediction of warning response time under different warning reliability, style and lead time were very similar to the response time results from the experiments. The model was able to explain 88.30% of the experimental response time in average with a root mean square error (RMSE) of 0.16s. The developed driver model could be applied to optimize the design of the connected vehicle systems based on driver