Prediction of suspended sediment concentration using hybrid SVM-WOA approaches

Abstract

Suspended sediment concentration (SSC) is one of the primary reasons with respect to watersheds or river basins, which must be assessed in a correct manner so that it will help decision makers to make right decisions regarding hydraulic structure, flash-flood, flood-mitigation of the basin. The present research evaluated efficacy of a hybrid model integrating Support Vector Machine with Whale optimization algorithm (SVM-WOA) for predicting SSC at Sundargarh and Salebhata stations in Mahanadi River, India. Various quantitative statistical evaluation constrains are applied to evacuate the model performance. Also, model performance of SVM-WOA is compared with SVM-PSO (Particle Swarm Optimization) and conventional SVM and RBFN (Radial Basis Function Network) models. The results reveal that, SVM-WOA performed superiorly in comparison to SVM-PSO, SVM and RBFN models for five different input scenarios during both training and testing phases. Hence, it is recommended to apply SVM-WOA as an appropriate technique for hydrological simulation at the basin.