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Major Accidents (Gray Swans) Likelihood Modeling Using Accident Precursors and Approximate Reasoning

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  • Nima Khakzad
  • Faisal Khan
  • Paul Amyotte

Abstract

Compared to the remarkable progress in risk analysis of normal accidents, the risk analysis of major accidents has not been so well‐established, partly due to the complexity of such accidents and partly due to low probabilities involved. The issue of low probabilities normally arises from the scarcity of major accidents’ relevant data since such accidents are few and far between. In this work, knowing that major accidents are frequently preceded by accident precursors, a novel precursor‐based methodology has been developed for likelihood modeling of major accidents in critical infrastructures based on a unique combination of accident precursor data, information theory, and approximate reasoning. For this purpose, we have introduced an innovative application of information analysis to identify the most informative near accident of a major accident. The observed data of the near accident were then used to establish predictive scenarios to foresee the occurrence of the major accident. We verified the methodology using offshore blowouts in the Gulf of Mexico, and then demonstrated its application to dam breaches in the United Sates.

Suggested Citation

  • Nima Khakzad & Faisal Khan & Paul Amyotte, 2015. "Major Accidents (Gray Swans) Likelihood Modeling Using Accident Precursors and Approximate Reasoning," Risk Analysis, John Wiley & Sons, vol. 35(7), pages 1336-1347, July.
    • Handle: RePEc:wly:riskan:v:35:y:2015:i:7:p:1336-1347
    DOI: 10.1111/risa.12337
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    References listed on IDEAS

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    2. Glette-Iversen, Ingrid & Aven, Terje, 2021. "On the meaning of and relationship between dragon-kings, black swans and related concepts," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    3. Yongliang Deng & Ying Zhang & Zhenmin Yuan & Rita Yi Man Li & Tiantian Gu, 2023. "Analyzing Subway Operation Accidents Causations: Apriori Algorithm and Network Approaches," IJERPH, MDPI, vol. 20(4), pages 1-20, February.
    4. Spada, Matteo & Paraschiv, Florentina & Burgherr, Peter, 2018. "A comparison of risk measures for accidents in the energy sector and their implications on decision-making strategies," Energy, Elsevier, vol. 154(C), pages 277-288.

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