A nonlinear simulation method based on a combination of multilayer perceptron and decision trees for predicting non-deposition sediment transport

Abstract

This article presents a fast and powerful new hybrid decision tree (DT) method based on multilayer perceptron neural networks (MLP-NN) to determine the limiting velocity in sediment transport for preventing solid matter deposition. The parameters with the greatest influence on limiting-velocity prediction are exploited from the literature in order to present the MLP-DT-based model in this study. The effect of each parameter presented as part of functional relationships in previous studies is first surveyed by means of sensitivity analysis with the MLP-NN. After identifying the most effective parameters, the hybrid MLP-DT method is used to predict the limiting velocity. A comparison between MLP (R2 = 0.957, MARE = 0.072, RMSE = 0.434, SI = 0.107, BIAS = 0.029) and MLP-DT (R2 = 0.975, MARE = 0.063, RMSE = 0.328, SI = 0.081, BIAS = −0.01) shows that the MLP and DT combination leads to increased MLP-NN ability to predict the required limiting velocity and prevent sediment deposition. The approach developed in this study yields explicit expressions for practical applications.