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A practical methodology for information fusion in presence of uncertainty: application to the analysis of a nuclear benchmark

Author

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  • Jean Baccou

    (Institut de Radioprotection et de Sûreté Nucléaire (IRSN), PSN-RES, SEMIA, LIMAR, Cadarache)

  • Eric Chojnacki

    (Institut de Radioprotection et de Sûreté Nucléaire (IRSN), PSN-RES, SEMIA, LIMAR, Cadarache)

Abstract

This work is devoted to some methodological developments on information fusion in presence of uncertainty and their application in the frame of the BEMUSE Programme. In nuclear safety studies, different uncertainty analyses using different codes and implying different experts are generally performed. It is then useful to define formal methods to combine all these information sources in order to improve the reliability of the expertise process and especially to detect possible conflicts (if any) between the sources. Starting from the IRSN methodology already introduced in Destercke and Chojnacki (Nucl Eng Des 238(9):2484–2493, 2008), this paper presents a more convenient reformulation of its construction to allow its use by engineers. It is then applied to analyse the results coming from the BEMUSE Programme.

Suggested Citation

  • Jean Baccou & Eric Chojnacki, 2014. "A practical methodology for information fusion in presence of uncertainty: application to the analysis of a nuclear benchmark," Environment Systems and Decisions, Springer, vol. 34(2), pages 237-248, June.
    • Handle: RePEc:spr:envsyd:v:34:y:2014:i:2:d:10.1007_s10669-014-9496-3
    DOI: 10.1007/s10669-014-9496-3
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    References listed on IDEAS

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    1. Cooke, Roger M. & Kelly, G.N., 2010. "Climate Change Uncertainty Quantification: Lessons Learned from the Joint EU-USNRC Project on Uncertainty Analysis of Probabilistic Accident Consequence Codes," RFF Working Paper Series dp-10-29, Resources for the Future.
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    Cited by:

    1. Myriam Merad, 2014. "Expertise processes in risk assessment and management: How to improve their governance and their conduct?," Environment Systems and Decisions, Springer, vol. 34(2), pages 181-182, June.
    2. Jeremy Rohmer & Eric Chojnacki, 2021. "Forecast of environment systems using expert judgements: performance comparison between the possibilistic and the classical model," Environment Systems and Decisions, Springer, vol. 41(1), pages 131-146, March.

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