Prediction of suspended sediment concentration using hybrid SVM-WOA approaches. Issue 19 (2nd October 2022)

• Record Type: Journal Article
• Authors: Samantaray, Sandeep; Sahoo, Abinash
• Is Part Of: Geocarto international. Volume 37:Issue 19(2022)
• Page Start: 5609
• Page End: 5635
• Publication Date: 2022-10-02
• Subjects: Prediction -- SSC -- SVM-PSO -- SVM-WOA -- Mahanadi basin; Remote sensing -- Periodicals; Geographic information systems -- Periodicals; Geology -- Periodicals; Cartography -- Periodicals; 621.3678
• DOI: 10.1080/10106049.2021.1920638
• Languages: English
• ISSNs: 1010-6049

Abstract

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.