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Representing external hazard initiating events using a Bayesian approach and a generalized extreme value model

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  • Smith, Curtis L.

Abstract

•This paper details how Bayesian inference can be used to evaluate the Generalized Extreme Value (GEV) model, including how to quantify parameter, and modeling uncertainty. The GEV is potentially useful to represent the occurrence of natural hazards as an initiating event in computational risk analysis.•This paper demonstrates how to incorporate “inferred information†such as paleo-data (data collected before the time of continuous records or direct measurements) with an observed set of data.•This paper demonstrates an approach to probabilistic weighting of data in order to emphasize different parts of an observed data set.•This paper incorporates physical limits into the statistical-based GEV modeling approach.•This paper includes trending in order to account for medium- to long-term variations such as climate change.The objective of this paper is to demonstrate a Bayesian approach to using Generalized Extreme Value (GEV) models for representing a hazard-magnitude-frequency relationship within a computational risk assessment (CRA) framework. This paper will provide discussion and demonstration of:1How Bayesian inference can be used to evaluate the GEV model, including a detailed treatment of data, parameter, and modeling uncertainty.2Three synthetic examples where the underlying data-producing mechanisms are fully known in order to understand how GEVs can be used to represent the occurrence of extreme events. The three examples used in this paper are (i) a linear-type model, (ii) a logarithmic-type model, and (iii) an exponential-type model.3Incorporation of inferred information such as “paleo data†with an observed data set.4Probabilistic weighting of data in order to emphasize different parts of an observed data set over other less-important data.5Incorporation of physics or physical limits into the statistical-based GEV modeling approach.6Inclusion of trending within the GEV approach in order to account for medium- to long-term variations such as climate change.

Suggested Citation

  • Smith, Curtis L., 2020. "Representing external hazard initiating events using a Bayesian approach and a generalized extreme value model," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    • Handle: RePEc:eee:reensy:v:193:y:2020:i:c:s0951832019302790
    DOI: 10.1016/j.ress.2019.106650
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    References listed on IDEAS

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    1. Kelly, Dana L. & Smith, Curtis L., 2009. "Bayesian inference in probabilistic risk assessment—The current state of the art," Reliability Engineering and System Safety, Elsevier, vol. 94(2), pages 628-643.
    2. Enrico Zio, 2013. "Monte Carlo Simulation: The Method," Springer Series in Reliability Engineering, in: The Monte Carlo Simulation Method for System Reliability and Risk Analysis, edition 127, chapter 0, pages 19-58, Springer.
    3. Enrico Zio, 2013. "The Monte Carlo Simulation Method for System Reliability and Risk Analysis," Springer Series in Reliability Engineering, Springer, edition 127, number 978-1-4471-4588-2, December.
    4. Vicki M. Bier & Yacov Y. Haimes & James H. Lambert & Nicholas C. Matalas & Rae Zimmerman, 1999. "A Survey of Approaches for Assessing and Managing the Risk of Extremes," Risk Analysis, John Wiley & Sons, vol. 19(1), pages 83-94, February.
    5. Enrico Zio, 2013. "System Reliability and Risk Analysis by Monte Carlo Simulation," Springer Series in Reliability Engineering, in: The Monte Carlo Simulation Method for System Reliability and Risk Analysis, edition 127, chapter 0, pages 59-81, Springer.
    6. Manfred Gilli & Evis këllezi, 2006. "An Application of Extreme Value Theory for Measuring Financial Risk," Computational Economics, Springer;Society for Computational Economics, vol. 27(2), pages 207-228, May.
    7. Enrico Zio, 2013. "System Reliability and Risk Analysis," Springer Series in Reliability Engineering, in: The Monte Carlo Simulation Method for System Reliability and Risk Analysis, edition 127, chapter 0, pages 7-17, Springer.
    8. Dana Kelly & Curtis Smith, 2011. "Bayesian Inference for Probabilistic Risk Assessment," Springer Series in Reliability Engineering, Springer, number 978-1-84996-187-5, December.
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    2. Dai, Baorui & Xia, Ye & Li, Qi, 2022. "An extreme value prediction method based on clustering algorithm," Reliability Engineering and System Safety, Elsevier, vol. 222(C).

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