Using the regression method and GMDH model in estimating the ratio of input sediment to lateral Intake

Due to the changes that occur in the distribution of velocity in the area of ​​the catchment opening, sedimentation usually takes place, which reduces the efficiency of water intake, increases the operational costs for sedimentation operations, and finally changes the direction and contour of the trough. The river drains towards the opposite bank. The use of sediment control structures separating the wall in front of the catchment and at the same time the breakwater in front of the catchment reduces the incoming sediment and increases the catchment efficiency. In the current research, the effect of the separating wall and the breakwater structure in estimating the ratio of incoming sediment to the catchment has been evaluated by laboratory and data mining and multiple regression methods. First, by performing dimensional analysis, dimensionless ratios were extracted and the relationship between the variables and their value in the experiments was determined. Using XLSTAT and SPSS statistical software, equations for the relationship between independent and dependent variables were extracted from the step-by-step and inter method. After obtaining the equations, the relative error of each equation was calculated. Then the best equation with high R2 and low relative error was selected and proposed. In the next step, modeling was done with the methods of artificial neural networks and the method of group data control (GMDH) and the best method was selected to estimate the ratio of input sediment to the catchment. The results showed that in estimating the ratio of input sediment to the catchment, the best performance is related to the model (GMDH) with statistical indices, R2=0.850, MAD=0.03, RMSE=0.039 and MAPE=26.95. In the step-by-step regression method, R2=0.38, RMSE=4.99 and inter regression method, R2=0.76, RMSE=4.16. also data-mining methods compared to the method Regression has higher accuracy.