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Reliability and effectiveness of early warning systems for natural hazards: Concept and application to debris flow warning

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  • Sättele, Martina
  • Bründl, Michael
  • Straub, Daniel

Abstract

Early Warning Systems (EWS) are increasingly applied to mitigate the risks posed by natural hazards. To compare the effect of EWS with alternative risk reduction measures and to optimize their design and operation, their reliability and effectiveness must be quantified. In the present contribution, a framework approach to the evaluation of threshold-based EWS for natural hazards is presented. The system reliability is classically represented by the Probability of Detection (POD) and Probability of False Alarms (PFA). We demonstrate how the EWS effectiveness, which is a measure of risk reduction, can be formulated as a function of POD and PFA. To model the EWS and compute the reliability, we develop a framework based on Bayesian Networks, which is further extended to a decision graph, facilitating the optimization of the warning system. In a case study, the framework is applied to the assessment of an existing debris flow EWS. The application demonstrates the potential of the framework for identifying the important factors influencing the effectiveness of the EWS and determining optimal warning strategies and system configurations.

Suggested Citation

  • Sättele, Martina & Bründl, Michael & Straub, Daniel, 2015. "Reliability and effectiveness of early warning systems for natural hazards: Concept and application to debris flow warning," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 192-202.
    • Handle: RePEc:eee:reensy:v:142:y:2015:i:c:p:192-202
    DOI: 10.1016/j.ress.2015.05.003
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    References listed on IDEAS

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    1. Christoph Rheinberger, 2013. "Learning from the past: statistical performance measures for avalanche warning services," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 65(3), pages 1519-1533, February.
    2. Langseth, Helge & Portinale, Luigi, 2007. "Bayesian networks in reliability," Reliability Engineering and System Safety, Elsevier, vol. 92(1), pages 92-108.
    3. Alexandre Badoux & Christoph Graf & Jakob Rhyner & Richard Kuntner & Brian McArdell, 2009. "A debris-flow alarm system for the Alpine Illgraben catchment: design and performance," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 49(3), pages 517-539, June.
    4. Ross D. Shachter, 1986. "Evaluating Influence Diagrams," Operations Research, INFORMS, vol. 34(6), pages 871-882, December.
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    Cited by:

    1. Lan, Meng & Zhu, Jiping & Lo, Siuming, 2021. "Hybrid Bayesian network-based landslide risk assessment method for modeling risk for industrial facilities subjected to landslides," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    2. Levitin, Gregory & Xing, Liudong & Xiang, Yanping, 2020. "Optimal early warning defense of N-version programming service against co-resident attacks in cloud system," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    3. Panagiotis Partsinevelos & George Kritikakis & Nikos Economou & Zach Agioutantis & Achilleas Tripolitsiotis & Stelios Mertikas & Antonis Vafidis, 2016. "Integration of seismic and image data processing for rockfall monitoring and early warning along transportation networks," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 83(1), pages 133-153, October.
    4. Khakzad, Nima & Van Gelder, Pieter, 2018. "Vulnerability of industrial plants to flood-induced natechs: A Bayesian network approach," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 403-411.

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