Modeling Average Grain Velocity for Rectangular Channel Using Soft Computing Techniques

This study was undertaken with the primary objective of modeling grain velocity based on experimental data obtained under the controlled conditions of a laboratory using a rectangular hydraulic tilting channel. Soft computing approaches, i.e., support vector machine (SVM), artificial neural network (ANN), and multiple linear regression (MLR), were applied to simulate grain velocity using four input variables; shear velocity, exposed area to base area ratio (EATBAR), relative depth, and sediment particle weight. Quantitative performance evaluation of predicted values was performed with the help of three different standard statistical indices, such as the root mean square error (RMSE), Pearson's correlation coefficient (PCC), and Wilmot index (WI). The results during the testing phase revealed that the SVM model has RMSE (m/s), PCC, and WI values obtained as 0.1195, 0.8877, and 0.7243, respectively, providing more accurate predictions than the MLR and ANN models during the testing phase.