Improving the Muskingum Flood Routing Method Using a Hybrid of Particle Swarm Optimization and Bat Algorithm

Ali Najah Ahmed,Zaher Mundher Yaseen,Ahmed El-Shafie,Faridah Binti Othman,Mohammed Falah Allawi,Vijay P Singh,Shamsuddin Shahid,Marlinda Bt Abdul Malek,Haitham Abdulmohsin Afan,Mohammad Ehteram

doi:10.3390/w10060807

Abstract

Flood prediction and control are among the major tools for decision makers and water resources planners to avoid flood disasters. The Muskingum model is one of the most widely used methods for flood routing prediction. The Muskingum model contains four parameters that must be determined for accurate flood routing. In this context, an optimization process that self-searches for the optimal values of these four parameters might improve the traditional Muskingum model. In this study, a hybrid of the bat algorithm (BA) and the particle swarm optimization (PSO) algorithm, i.e., the hybrid bat-swarm algorithm (HBSA), was developed for the optimal determination of these four parameters. Data for the three different case studies from the USA and the UK were utilized to examine the suitability of the proposed HBSA for flood routing. Comparative analyses based on the sum of squared deviations (SSD), sum of absolute deviations (SAD), error of peak discharge, and error of time to peak showed that the proposed HBSA based on the Muskingum model achieved excellent flood routing accuracy compared to that of other methods while requiring less computational time.

Highlights

Floods cause huge economic and social effects on the surrounding environment [1,2], such as breaking levees [3], inundating houses, disrupting transportation systems [4], damaging crops and eroding fertile lands [5]
The results indicated that Gene-expression programming (GEP) had a convergence speed that was approximately 100 times higher than that of weed algorithm (WA); in addition, the computed sum of squared deviations (SSD) for the estimated discharges was lower than that of WA
Was 27 s and 25 s for the particle swarm optimization (PSO) algorithm and bat algorithm (BA); the computational time decreased by 25% and

Summary

Introduction

Floods cause huge economic and social effects on the surrounding environment [1,2], such as breaking levees [3], inundating houses, disrupting transportation systems [4], damaging crops and eroding fertile lands [5]. Hydrological and hydraulic models are used for flood prediction. Hydrological models use the spatially lumped continuity equation and a storage equation for flood routing. These models need a small amount of data to predict floods [11]. The Muskingum model is an important hydrological model for flood routing. This model has multiple parameters that should be obtained to accurately predict floods [12], and different versions of the model have been applied for flood routing. The Nelder-Mead simplex algorithm was used for flood routing in the USA [16] This method obtained the best values for the 3 Muskingum model parameters. The peak discharges were predicted to be better than those of other nonlinear programming methods

Methods

Results

Discussion

Conclusion