Investigation of the Effects of Hydraulic and Sedimentary Parameters on the Rate of Bed Load Transfport Using Statistical Correlations and Machine Learning Methods

In hydraulic and river engineering, solid load sediment play an essential role in determining river behavior and morphological control; For this reason, the assessment and correct estimation of  solid load sediment transfport from a long time ago is one of the important issues in the sciences related to river engineering and the environment. The purpose of this study is to estimate the bed load transfer in 19 gravel-bed rivers. For this purpose, first the statistical correlation trend between sediment transport parameter (bed load discharge) and hydraulic and sedimentary parameters (flow discharge, flow depth, flow velocity, the median bed material particle diameter, Froude number,…) is investigated and the bed load discharge is estimated as a univariate regression function. According to the presented results, a favorable correlation was reached between the sediment transport parameter and hydraulic and sedimentary parameters and the results showed that these simple regression relationships in most rivers had acceptable accuracy. Also, the performance of 10 experimental formulas in bed load prediction was investigated. All formulas have had very poor results. For this reason, the parameters related to the formulas that had relatively better results than the other formulas were selected and, in order to increase the estimation accuracy, once again using two kernel-based machine learning methods Support Vector Machine (SVM). Gaussian process regression (GPR) modeling was performed. The results showed that the machine methods have acceptable accuracy in predicting the bed load and the model is related to the parameters of  Begnold formula, which includes the parameters of the stream power, the average flow depth and the median bed material particle diameter, with R =0.923 and NSE =0.851 has the best results in the machine methods.