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.
- bed load
- decision tree (DT)
- multilayer perceptron neural network (MLP-NN)
- sediment transport
- sensitivity analysis
- First received 1 May 2015.
- Accepted in revised form 29 February 2016.
- © IWA Publishing 2016