Development of the FCM-SVR Hybrid Model for Estimating the Suspended Sediment Load

The accurate estimation of suspended sediment load (SSL) carried by a river is one of the primary issues in river engineering, water resources, and environment projects. Boundary condition and simplification of some important parameters lead to some limitation on the empirical sediment equations which are based on flow and sediment properties. In this study, the potential of a developed Fuzzy C-mean clustering-support vector regression model, as a FCM-SVR hybrid model, was investigated in comparison with sediment rating curve (SRC), artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector regression (SVR) models for estimating the daily SSL in Sistan River in Iran. Root mean square error (RMSE), mean absolute error (MAE), determination coefficient (R2), and Nash-Sutcliffe efficiency coefficient (NSE) statistics were utilized to evaluate the accuracy of the models. The result showed that FCM-SVR model estimates SSL (with RMSE, MAE, R2, and NSE equal to 34,415.52 ton/day, 12,256.28 ton/day, 0.922, and 0.918 respectively in testing period) more accurate than other models. The RMSE value, which was 50% lower compared to other models, reveals that this model possesses the lowest error than the other models. Also, the obtained results indicated that the estimated SSLs, using the best FCM-SVR, were in good agreement and linear dependence with observed values. Unlike other models, FCM-SVR model appropriately estimates extreme values of SSL.