IDEAS home Printed from https://ideas.repec.org/a/eee/reensy/v142y2015icp192-202.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0951832015001441
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ress.2015.05.003?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Langseth, Helge & Portinale, Luigi, 2007. "Bayesian networks in reliability," Reliability Engineering and System Safety, Elsevier, vol. 92(1), pages 92-108.
    4. Ross D. Shachter, 1986. "Evaluating Influence Diagrams," Operations Research, INFORMS, vol. 34(6), pages 871-882, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. 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.
    3. 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.
    4. 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).
    5. Feng, Xinhang & Jiang, Yanli & Gai, Wenmei, 2024. "Rural community response to accidental toxic gas release: An individual emergency response model during self-organized evacuations," Reliability Engineering and System Safety, Elsevier, vol. 248(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bielza, Concha & Gómez, Manuel & Shenoy, Prakash P., 2011. "A review of representation issues and modeling challenges with influence diagrams," Omega, Elsevier, vol. 39(3), pages 227-241, June.
    2. Zitrou, Athena & Bedford, Tim & Walls, Lesley, 2010. "Bayes geometric scaling model for common cause failure rates," Reliability Engineering and System Safety, Elsevier, vol. 95(2), pages 70-76.
    3. Tan, Kim Hua & Zhan, YuanZhu & Ji, Guojun & Ye, Fei & Chang, Chingter, 2015. "Harvesting big data to enhance supply chain innovation capabilities: An analytic infrastructure based on deduction graph," International Journal of Production Economics, Elsevier, vol. 165(C), pages 223-233.
    4. Luca Schilirò & Gian Marco Marmoni & Matteo Fiorucci & Massimo Pecci & Gabriele Scarascia Mugnozza, 2023. "Preliminary insights from hydrological field monitoring for the evaluation of landslide triggering conditions over large areas," 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. 118(2), pages 1401-1426, September.
    5. Rogerson, Ellen C. & Lambert, James H., 2012. "Prioritizing risks via several expert perspectives with application to runway safety," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 22-34.
    6. Arash Malekian & Ali Azarnivand, 2016. "Application of Integrated Shannon’s Entropy and VIKOR Techniques in Prioritization of Flood Risk in the Shemshak Watershed, Iran," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(1), pages 409-425, January.
    7. Ibsen Chivatá Cárdenas & Saad S.H. Al‐Jibouri & Johannes I.M. Halman & Frits A. van Tol, 2014. "Modeling Risk‐Related Knowledge in Tunneling Projects," Risk Analysis, John Wiley & Sons, vol. 34(2), pages 323-339, February.
    8. Prakash Shenoy, 1998. "Game Trees For Decision Analysis," Theory and Decision, Springer, vol. 44(2), pages 149-171, April.
    9. Marquez, David & Neil, Martin & Fenton, Norman, 2010. "Improved reliability modeling using Bayesian networks and dynamic discretization," Reliability Engineering and System Safety, Elsevier, vol. 95(4), pages 412-425.
    10. George-Williams, Hindolo & Patelli, Edoardo, 2017. "Efficient availability assessment of reconfigurable multi-state systems with interdependencies," Reliability Engineering and System Safety, Elsevier, vol. 165(C), pages 431-444.
    11. Vimal Vijayan & Sanjay K Chaturvedi & Ritesh Chandra, 2020. "A failure interaction model for multicomponent repairable systems," Journal of Risk and Reliability, , vol. 234(3), pages 470-486, June.
    12. Michail Cheliotis & Evangelos Boulougouris & Nikoletta L Trivyza & Gerasimos Theotokatos & George Livanos & George Mantalos & Athanasios Stubos & Emmanuel Stamatakis & Alexandros Venetsanos, 2021. "Review on the Safe Use of Ammonia Fuel Cells in the Maritime Industry," Energies, MDPI, vol. 14(11), pages 1-20, May.
    13. Oepping, Hardy, 2016. "Ein Bayes-Netz zur Analyse des Absturzrisikos im Gerüstbau [A Bayesian network for analysing the risk of falling from height in scaffolding]," MPRA Paper 73602, University Library of Munich, Germany.
    14. Borgonovo, Emanuele & Tonoli, Fabio, 2014. "Decision-network polynomials and the sensitivity of decision-support models," European Journal of Operational Research, Elsevier, vol. 239(2), pages 490-503.
    15. Bensi, Michelle & Kiureghian, Armen Der & Straub, Daniel, 2013. "Efficient Bayesian network modeling of systems," Reliability Engineering and System Safety, Elsevier, vol. 112(C), pages 200-213.
    16. Iamsumang, Chonlagarn & Mosleh, Ali & Modarres, Mohammad, 2018. "Monitoring and learning algorithms for dynamic hybrid Bayesian network in on-line system health management applications," Reliability Engineering and System Safety, Elsevier, vol. 178(C), pages 118-129.
    17. Penttinen, Jussi-Pekka & Niemi, Arto & Gutleber, Johannes & Koskinen, Kari T. & Coatanéa, Eric & Laitinen, Jouko, 2019. "An open modelling approach for availability and reliability of systems," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 387-399.
    18. Groth, Katrina M. & Smith, Reuel & Moradi, Ramin, 2019. "A hybrid algorithm for developing third generation HRA methods using simulator data, causal models, and cognitive science," Reliability Engineering and System Safety, Elsevier, vol. 191(C).
    19. Vic Hasselblad & Douglas C. McCrory, 1995. "Meta-analytic Tools for Medical Decision Making: A Practical Guide," Medical Decision Making, , vol. 15(1), pages 81-96, February.
    20. Zhong, Shengtong & Langseth, Helge & Nielsen, Thomas Dyhre, 2014. "A classification-based approach to monitoring the safety of dynamic systems," Reliability Engineering and System Safety, Elsevier, vol. 121(C), pages 61-71.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:reensy:v:142:y:2015:i:c:p:192-202. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/reliability-engineering-and-system-safety .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.