Assessment of Modern Regression Methods to Suspended Sediment Load Estimation in the Sistan River

Correct estimation of the river sediment volume is important for many water resources projects.In this study, the empirical equations of sediment transport, support vector regression (SVR), and developed k-nearest neighbor regression (KNN) were used in order to estimate suspended sediment load in the Sistan River. In this regard, in addition to the maximum temperature, minimum temperature, and dischargeand Suspended Loud In the period 1374 to 1390, it was paired on 1682, discharge, the classified discharge was used as effecting variable to suspended sediment load modeling. For each of the input combinations in support vector regression and development k-nearest neighbor regression the best structure of the regression model determined based on the performance criteria. The Result showed that the Toffaleti method is the best method between empirical equations with R2 equal to 0.705 and RMSE equal to 66558 Ton/day. Also, the support vector regression model with discharge, minimum temperature, maximum temperature and classified discharge as the set of input data is the best model in estimation of suspended sediment load with R2 equal to 0.96 and RMSE equal to 2809.3 Ton/day. The results indicate that the regression method estimate suspended sediment load much better than empirical equations in the Sistan River.