Artificial Intelligence
Modeling vague beliefs using fuzzy-valued belief structures
Fuzzy Sets and Systems - Special issue on fuzzy numbers and uncertainty
Risk assessment based on weak information using belief functions: a case study in water treatment
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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In the environment impacts assessment context, several methods are proposed in literature, such as life cycle analysis, multi criteria analysis and cost benefit analysis etc. Recently, others methods combining MCDA (Multi Criteria Decision Analysis) and AI (Artificial Intelligence) have been explored to develop enhanced methodologies for knowledge based decision support. In this paper, a new approach is presented for environmental assessment of urban mobility. Various criteria should be evaluated using complex data obtained from several information sources. Therefore, the measure evaluation related to urban mobility is a hard task that does not always lead to efficient results. The problem treated in this paper, is complex with insufficient, fuzzy and uncertain data. A hybrid approach based on multi criteria analysis and various information sources has been proposed. The methodology uses fuzzy set theory for modeling criteria and belief theory (Dempster-Shafer Theory (DST)) for evaluations fusion and it is able to handle uncertainty and vagueness.