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Movement of sediment in the river causes many changes in the river bed. These changes are called bedform. River bedform has significant and direct effects on bed roughness, flow resistance, and water surface profile. Thus, having adequate knowledge of the bedform is of special importance in river engineering. Several methods have been developed by researchers for estimation of bed form dimensions. In this investigation, bedform has been estimated using Artificial Neural Network (ANN) and Support Vector Machine (SVM) methods. The results obtained from these two methods were compared with empirical formulas of Van Rijn. The accuracy of the model was evaluated using (RMSE), (MSRE), (CE), (R²) and (RB) statistical parameters. Higher values of statistical parameters indicated that the SVM model with RBF kernel function predicted the bedform more accurately than the other method. The values calculated for R², RMSE, MSRE, CE and RB parameters were 0.79, 0.024, 0.066, 0.786, -0.081, respectively. Comparison of the results of the SVM model with RBF kernel with other models indicated that SVM had a higher capability for estimating and simulating height of the bedform than Artificial Neural Networks.

Keywords: Bed roughness, RBF kernel function, River engineering

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