Evaluating evolutionary algorithms for simulating catchment response to river discharge

Abstract Evolutionary algorithms (EAs) are proficient in solving the controlled, nonlinear multimodal, non-convex problems that limit use of deterministic approaches. The competencies EA have been applied various environmental and water resources problems. In this study, storm management model (SWMM) was set up to authenticate capability for simulating catchment response upper Damodar River basin. Auto-calibration validation SWMM were done years 2002–2011 at a daily scale using three EAs: genetic (GAs), particle swarm optimisation (PSO) shuffled frog leaping algorithm (SFLA). Statistical parameters like Nash–Sutcliffe effectiveness (NSE), percent bias (PBIAS) root-mean-squared error–observations standard deviation ratio (RSR) used analyse efficacy results. NSE PBIAS values obtained from GA superior, with recorded flow ranging between 0.63 0.69 1.12 9.81, respectively, five discharge locations. value RSR approximately 0 indicating sensibly exceptional performance model. results SFLA robust superior PSO. Our showed prospective blending hydrodynamic would aid decision-makers analysing vulnerability river watersheds.