Uncertainty analysis using evidence theory - confronting level-1 and level-2 approaches with data availability and computational constraints

Dempster-Shafer Theory of Evidence (DST), as an alternative or complementary approach to the representation of uncertainty, is gradually being explored with complex practical applications beyond purely algebraic examples. This paper reviews literature documenting such complex applications and studies its applicability from the point of view of the nature and amount of data that is typically available in industrial risk analysis: medium-size frequential observations for aleatory components, small noised datasets for model parameters and expert judgment for other components. On the basis of a simple flood model encoding typical risk analysis features, different approaches to quantify uncertainty in DST are reviewed and benchmarked in that perspective: (i) combining all sources of uncertainty under a single-level DST model; (ii) separating aleatory and epistemic uncertainties, respectively, modeled with a first probabilistic layer and a second one under DST. Methods for handling data in probabilistic studies such as Kolmogorov-Smirnov tests and quantile-quantile plots are transferred to the domain of DST. We illustrate how data availability guides the choice of the settings and how results and sensitivity analyses can be interpreted in the domain of DST, concluding with recommendations for industrial practice.