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 the use of deterministic approaches. The competencies of EA have been applied in solving various environmental and water resources problems. In this study, the storm water management model (SWMM) was set up to authenticate the capability of the model for simulating catchment response in the upper Damodar River basin. Auto-calibration and validation of SWMM were done for the years 2002–2011 at a daily scale using three EAs: genetic algorithms (GAs), particle swarm optimisation (PSO) and shuffled frog leaping algorithm (SFLA). Statistical parameters like Nash–Sutcliffe effectiveness (NSE), percent bias (PBIAS) and root-mean-squared error–observations standard deviation ratio (RSR) were used to analyse the efficacy of the results. NSE and PBIAS values obtained from GA were superior, with the recorded flow with NSE and PBIAS ranging between 0.63 and 0.69 and between 1.12 and 9.81, respectively, for five discharge locations. The value of RSR was approximately 0 indicating the sensibly exceptional performance of the model. The results obtained from SFLA were robust and superior. Our results showed the prospective use and blending of the hydrodynamic model with EA would aid the decision-makers in analysing the vulnerability in river watersheds. HIGHLIGHTS GA, SFLA and PSO coupled with SWMM to characterise temporal dynamics of river discharge in Upper Damodar River Basin.; GA provided model iteration equivalent to 2,000 and performed robustly with NSE and PBIAS ranging between 0.65 and 0.72 and between 1.51 and 9.51, respectively.; SFLA performed comparatively to GA with a higher convergence speed value, whereas PSO performed satisfactorily.;

Keywords

• auto-calibration
• evolutionary algorithms
• gis
• optimisation
• swmm