SAMPLE UNCERTAINTY IN FLOOD LEVEE DESIGN: BAYESIAN VERSUS NON-BAYESIAN METHODS1

ABSTRACT: Bayesian and non-Bayesian flood levee design methods that account for the uncertainty due to limited record length are compared using a case study. The first method, Bayesian decision theory (BDT), imbeds the uncertainty in the parameters of the yearly peak stage into a loss function. The optimum design of the flood levee, called Bayes design, corresponds to the minimum expected loss, called Bayes risk. The second method, induced safety algorithm (ISA), computes a margin of safety to be added to either an existing levee or a levee designed by classical benefit-cost analysis. The design decision is shown to fluctuate as different record lengths are considered. For short record lengths, BDT, which takes small sample bias into account, appears to yield a more conservative design than ISA. On the other hand, ISA, which is simple to implement, seems to be preferable to BDT for longer record lengths.