Improving accuracy of SVM for monthly sediment load prediction using Harris hawks optimization

Abstract

One of the most significant concerns for water resource projects is estimating sediment load (SL). In last three decades, the application of soft computing models for SL. prediction has broadly increased and provided effective outcomes. Present study applies a support vector machine (SVM) for estimating SL. at two stations of Baitarani river basin, Odisha, India. A meta-heuristic algorithm called Harris hawks optimization (HHO) is utilized for enhancing accuracy of SVM model in monthly SL prediction. Lagged rainfall, temperature, stage, runoff are considered different inputs for developing all models. The ability of SVM-HHO is benchmarked with SVM-PSO (particle swarm optimization), SVM-GWO (Grey wolves optimization), along with conventional SVM and ANN (artificial neural network) models. Collected data is divided into two subgroups (training and testing), and SL is estimated. Performance of applied methodologies is evaluated by quantitative statistical measures, such as coefficient of determination (R2), root mean square error (RMSE), and Nash-Sutcliffe efficiency (NSE). Obtained results revealed that highest value of NSE (0.9898), R2 (0.988), and a minimum of RMSE (0.6682) was generated by SVM-HHO in testing phase compared to other studied techniques. This leads to a conclusion regarding superiority of HHO evolutionary algorithm in enhancing accuracy of conventional SVM for monthly SL prediction. Ability of HHO algorithm to escape local optima marks SVM-HHO as a potential method in SL estimation. Findings of this study tend to ascertain the suitability of employed approach for precisely modelling SL in rivers.