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A Comparison Between Probabilistic and Dempster‐Shafer Theory Approaches to Model Uncertainty Analysis in the Performance Assessment of Radioactive Waste Repositories

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  • Piero Baraldi
  • Enrico Zio

Abstract

Model uncertainty is a primary source of uncertainty in the assessment of the performance of repositories for the disposal of nuclear wastes, due to the complexity of the system and the large spatial and temporal scales involved. This work considers multiple assumptions on the system behavior and corresponding alternative plausible modeling hypotheses. To characterize the uncertainty in the correctness of the different hypotheses, the opinions of different experts are treated probabilistically or, in alternative, by the belief and plausibility functions of the Dempster‐Shafer theory. A comparison is made with reference to a flow model for the evaluation of the hydraulic head distributions present at a radioactive waste repository site. Three experts are assumed available for the evaluation of the uncertainties associated with the hydrogeological properties of the repository and the groundwater flow mechanisms.

Suggested Citation

  • Piero Baraldi & Enrico Zio, 2010. "A Comparison Between Probabilistic and Dempster‐Shafer Theory Approaches to Model Uncertainty Analysis in the Performance Assessment of Radioactive Waste Repositories," Risk Analysis, John Wiley & Sons, vol. 30(7), pages 1139-1156, July.
    • Handle: RePEc:wly:riskan:v:30:y:2010:i:7:p:1139-1156
    DOI: 10.1111/j.1539-6924.2010.01416.x
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    References listed on IDEAS

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    1. Enrique López Droguett & Ali Mosleh, 2008. "Bayesian Methodology for Model Uncertainty Using Model Performance Data," Risk Analysis, John Wiley & Sons, vol. 28(5), pages 1457-1476, October.
    2. Robert T. Clemen & Robert L. Winkler, 1999. "Combining Probability Distributions From Experts in Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 19(2), pages 187-203, April.
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    Cited by:

    1. Michael Greenberg & Charles Haas & Anthony Cox & Karen Lowrie & Katherine McComas & Warner North, 2012. "Ten Most Important Accomplishments in Risk Analysis, 1980–2010," Risk Analysis, John Wiley & Sons, vol. 32(5), pages 771-781, May.
    2. Shang Gao & Yong Deng, 2019. "An evidential evaluation of nuclear safeguards," International Journal of Distributed Sensor Networks, , vol. 15(12), pages 15501477198, December.
    3. Piero Baraldi & Michele Compare & Enrico Zio, 2013. "Uncertainty analysis in degradation modeling for maintenance policy assessment," Journal of Risk and Reliability, , vol. 227(3), pages 267-278, June.
    4. Enrique López Droguett & Ali Mosleh, 2013. "Integrated treatment of model and parameter uncertainties through a Bayesian approach," Journal of Risk and Reliability, , vol. 227(1), pages 41-54, February.
    5. Edoardo Tosoni & Ahti Salo & Enrico Zio, 2018. "Scenario Analysis for the Safety Assessment of Nuclear Waste Repositories: A Critical Review," Risk Analysis, John Wiley & Sons, vol. 38(4), pages 755-776, April.
    6. Matteo Vagnoli & Francesco Di Maio & Enrico Zio, 2018. "Ensembles of climate change models for risk assessment of nuclear power plants," Journal of Risk and Reliability, , vol. 232(2), pages 185-200, April.
    7. Ibsen Chivatá Cárdenas & Saad S.H. Al‐jibouri & Johannes I.M. Halman & Frits A. van Tol, 2013. "Capturing and Integrating Knowledge for Managing Risks in Tunnel Works," Risk Analysis, John Wiley & Sons, vol. 33(1), pages 92-108, January.
    8. Enrique López Droguett & Ali Mosleh, 2014. "Bayesian Treatment of Model Uncertainty for Partially Applicable Models," Risk Analysis, John Wiley & Sons, vol. 34(2), pages 252-270, February.
    9. Tu Duong Le Duy & Laurence Dieulle & Dominique Vasseur & Christophe Bérenguer & Mathieu Couplet, 2013. "An alternative comprehensive framework using belief functions for parameter and model uncertainty analysis in nuclear probabilistic risk assessment applications," Journal of Risk and Reliability, , vol. 227(5), pages 471-490, October.

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