Estimation of Suspended Sediment Concentration in Gorganrood River Using Artificial Intelligence Methods

Over the last years, artificial intelligence models have been widely and successfully applied in many fields. In the present study, the efficiency of adaptive neuro-fuzzy inference system (ANFIS), SVM (Support Vector Machine) and Least Squares Support Vector Machines (LS-SVM) have been investigated to estimate the sediment concentration in four gauging stations, namely Jangaldeh, Nodeh, Arazkoosh, and Gazaghly along the Gorganrood River in Golestan province, Iran. The models were defined based on the five different combinations of the river flow and precipitation using time lags from 0 to 2 previous days. The results showed that the LS-SVM model with simplex search procedure had a better performance than the grid search method. Meanwhile, the results obtained from ANFIS model which estimated sediment concentration in Jangaldeh, Nodeh, Arazkoose and Ghazaghli stations with MEF Error of 5.3, 13.4, 4.8 and 2.8 percent, respectively, suggested a higher performance than other models. Overall, at all stations except Gazaghly, considering the antecedent flow with two-day time lag as the input data of the model increased the error magnitudes. Furthermore, the rainfall of the same day and one-day time lag could only enhance the efficiency of the model at Arazkooseh station.