Assessment and comparing of support vector machines model and regression equations for predicting alluvial channel geometry

Abstract

Determine the stable channel geometry of the river is one of the most important topics in river engineering. Various relationships (based on statistical and theoretical methods) to predict the stable channels dimensions are expressed by many scientists. In this study, three Support Vector Machines (SVM) models are designed to predict width (w), depth (h) and slope (s) of stable channel. 85 cross-section river field data is used in training and testing models. The models input parameters are the flow discharge (Q), median sediment diameter (d50) and affecting Shields parameter (τ*). Furthermore, the width, depth and slope values are calculated by Afzalimehr regression relationship. Several statistical indexes are used to check the accuracy of the models in comparison with field data. Results show that SVM models with correlation coefficient (R) 0.86, 0.66 and 0.646 in width, depth and slope prediction respectively have a good agreement with observational data. Also, the models comparison show a considerably better performance of the SVM models over the available regressions equations with a mean absolute relative error (MARE) decreasing of 72%, 20% and 11% in width, depth and slope prediction, respectively. The presented methodology in this paper is a good approach in predicting cross section geometry of alluvial rivers also it can be used to design stable irrigation and water conveyance channels.