Uncertainty Reduction in Environmental Data with Conflicting Information

The assessment of ecotoxicological effects of chemicals for regulatory purposes requires large amounts of experimental data which are expensive to obtain and eventually might entail exhaustive animal testing. The required decision-making processes in this regulatory context, must often be carried out with limited or even contradictory sources of information. To benefit from all sources of information without compromising the quality of the decision process, uncertainty management and reduction techniques, such as the Dempster-Shafer theory of evidence, have to be applied. This theory was applied to both experimental and in silico biodegradation data sources to assess chemical persistence. Uncertainties of the initially less uncertain estimates for biodegradation rates in water were reduced by as much as 20-60%. The analysis showed that conflicting evidence can be detected, quantified, and redistributed proportionally among all the feasible subsets of hypotheses. The advantages of the Dempster-Shafer theory over Bayesian approaches to represent evidence concerning hypotheses by assigning probabilities were also analyzed.


About robertrallo

Associate Professor, Dept. d'Eng. Informàtica i Matemàtiques at URV; Director of BioCENIT, Bioinformatics & Computational Environmental Engineering Research Team at URV; Member of ATIC, Advanced Technology & Innovation Center at URV; Visiting Scholar at UCLA; Member of the WaTeR center, UCLA; Member of the Center for Environmental Implications of Nanotechnology (CEIN), UCLA; Member of the Center for Nanobiology and Predictive Toxicology (CNPT), UCLA.

Posted on July 1, 2009, in Uncategorized and tagged , , , . Bookmark the permalink. Leave a comment.

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