Evaluating the Performance of Data-Driven Methods for Prediction of Total Sediment Load in Gravel-Bed Rivers

Numerous studies on sediment transport, especially prediction of this phenomenon, indicate its high importance in the sciences related to engineering and water resources management. In recent years, intelligent methods have been applied successfully to predict bed, suspended and total sediment load. However, due to the lack of measured data, limited researches have been done to deal with prediction of total load in gravel-bed rivers. The aim of this study is to apply Support Vector Machine (SVM), Artificial Neural Network (ANN) and Gaussian Process Regression (GPR) to predict total sediment load for 19 gravel-bed rivers and to compare the obtained results with well- known classic methods. For this purpose, different non-dimensional parameters based on hydraulic condition and sediment characteristics were defined and the performance of these methods was evaluated. According to the obtained results, the ANN model with correlation coefficient of R =0.952 and Nash–Sutcliffe efficiency (NSE=0.903) showed a better performance as compared to the other methods. Finally, by performing sensitivity analysis, the ratio of mean flow to shear velocity was introduced as the most effective parameter in predicting total sediment load.