Assessment of Sediment Load Concentration Using SVM, SVM-FFA and PSR-SVM-FFA in Arid Watershed, India: A Case Study

Abstract

Improvement in area of artificial intelligence for predicting different hydrological phenomenon has shaped an enormous alteration in predictions. Knowledge on suspended sediment load (SSL) is vital in managing water resources problems and safe guard environment. Present study evaluated accurateness of five soft computing techniques, i.e. radial basis function network (RBFN), cascade forward back propagation neural network (CFBPNN), support vector machine (SVM), integration of support vector machine with firefly algorithm (SVM-FFA) and phase space reconstruction (PSR) with SVM-FFA (PSR-SVM-FFA) approaches to estimate daily SSL in Salebhata, Suktel, Lant gauge stations in western part of Odisha, India. Performance of selected models were evaluated on basis of performance criterion namely root mean square error (RMSE), Nash-Sutcliffe (NSE), Wilton index (WI) for choosing best fit model. Results acquired verified that application of various neural network methods in present field of study showed fine concurrence with observed SSL values. Comparison of estimation accuracies of different methods exemplified that PSR-SVM-FFA is very precise to estimate SSL when compared with other models. Result shows that Suktel gauge station, the best value of WI is 0.978 for PSR-SVM-FFA model, while it is 0.959, 0.923, 0.885, and 0.842 for SVM-FFA, SVM, CFBPNN, RBFN models in testing phase. Moreover, cumulative SSL data calculated by PSR-SVM-FFA method are closer to observed data as compared to other methods.