Postprocessing the Hybrid Method for Addressing Uncertainty in Risk Assessments
Publication: Journal of Environmental Engineering
Volume 131, Issue 12
Abstract
In a previous paper in this Journal, a “hybrid method” was proposed for the joint propagation of probability distributions (expressing variability) and possibility distributions (i.e., fuzzy numbers, expressing imprecision or partial ignorance) in the computation of risk. In order to compare the results of the hybrid computation (a random fuzzy set) to a tolerance threshold (a tolerable level of risk), a postprocessing method was proposed. Recent work has highlighted a shortcoming of this postprocessing step which yields overly conservative results. A postprocessing method based on Shafer’s theory of evidence provides a rigorous answer to the problem of comparing a random fuzzy set with a threshold. The principles behind the new postprocessing scheme are presented and illustrated with a synthetic example.
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References
Baudrit, C., Dubois, D., and Fargier, H. (2003). “Propagation of uncertainty involving imprecision and randomness.” Proc., EUSFLAT, 3rd International Conf. in Fuzzy Logic and Technology, Zittau, Germany, 653–658.
Baudrit, C., Dubois, D., Guyonnet, D., and Fargier, H. (2004). “Joint treatment of imprecision and randomness in uncertainty propagation.” Proc., Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU-2004, Perugia, Italy, 873–880.
Connover, W., and Iman, R. (1982). “A distribution-free approach to inducing rank correlation among input variables.” Technometrics, 3, 311–334.
Dubois, D., and Prade, H. (1988). Possibility theory, Plenum, New York.
Dubois, D., and Prade, H. (1992). “When upper probabilities are possibility measures.” Fuzzy Sets Syst., 49, 95–74.
Ferson, S. (1996). “What Monte Carlo methods cannot do.” Hum. Environ. Risk Assess., 2, 990–1007.
Ferson, S., and Ginzburg, L. (1996). “Different methods are needed to propagate ignorance and variability.” Reliability Eng. Sys. Safety, 54, 133–144.
Ferson, S., Ginzburg, L., and Akcakaya, R. (2004). “Whereof one cannot speak: when input distributions are unknown.” Risk Anal, in press.
Gil, M. A. (2001). “Fuzzy random variables.” Inf. Sci. (N.Y.), 133(1-2), 89–100.
Guyonnet, D., Bourgine, B., Dubois, D., Fargier, H., Côme, B., and Chilès, J.-P. (2003). “Hybrid approach for addressing uncertainty in risk assessments.” J. Environ. Eng., 129(1), 68–78.
Shafer, G. (1976). A mathematical theory of evidence, Princeton Univ. Press, Princeton, N.J.
Zadeh, L. (1978). “Fuzzy sets as a basis for a theory of possibility.” Fuzzy Sets Syst., 1, 3–28.
Information & Authors
Information
Published In
Journal of Environmental Engineering
Volume 131 • Issue 12 • December 2005
Pages: 1750 - 1754
Copyright
© 2005 ASCE.
History
Received: Jan 5, 2004
Accepted: Mar 22, 2005
Published online: Dec 1, 2005
Published in print: Dec 2005
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