A Stochastic Optimization Algorithm for Optimizing Flood Risk Management Measures Including Rainfall Uncertainties and Nonphysical Flood Damages

Abstract

One of the basic assumptions usually made by researchers for obtaining optimal designs of flood mitigation measures that take into account the associated uncertainties is consideration of flood damage probability directly correlated with flood event probability. The present work demonstrates that this assumption leads to 42% underestimation of expected damages for the case study of the Kan River basin near the capital city of Iran, Tehran. To eliminate this limitation, in this paper, inherent rainfall uncertainties are included through random sampling techniques and simulation-based optimization approaches. A Monte Carlo simulation method is employed to generate multivariate synthetic rainfalls, which are then imported in a rainfall-runoff model. This model gives the flood hydrographs of subbasins for hydraulic routing in waterways of the watershed by a hydraulic model, considering different flood mitigation measures. These models are then coupled with the NSGA-II optimization algorithm to provide optimal Pareto solutions considering two competitive objectives of minimizing investment costs and expected physical and nonphysical damages in the flood-prone areas. The results obtained by applying the proposed hybrid model to a watershed show that some designs that seem inappropriate in terms of benefit-to-cost ratios may still be valuable for decision makers when nonphysical vulnerabilities are considered. Experimental results also showed that neglecting the spatial dependence of rainfalls throughout the watershed may lead to up to 26% underestimation of the calculated expected annual flood damages (EADs).