Prediction of the Slope Solute Loss Based on BP Neural Network

Abstract

The existence of soil macropores is a common phenomenon. Due to the existence of soil macropores, the amount of solute loss carried by water is deeply modified, which affects watershed hydrologic response. In this study, a new improved BP (Back Propagation) neural network method, using Levenberg–Marquand training algorithm, was used to analyze the solute loss on slopes taking into account the soil macropores. The rainfall intensity, duration, the slope, the characteristic scale of macropores and the adsorption coefficient of ions, are used as the variables of network input layer. The network middle layer is used as hidden layer, the number of hidden nodes is five, and a tangent transfer function is used as its neurons transfer function. The cumulative solute loss on the slope is used as the variable of network output layer. A linear transfer function is used as its neurons transfer function. Artificial rainfall simulation experiments are conducted in indoor experimental tanks in order to verify this model. The error analysis and the performance comparison between the proposed method and traditional gradient descent method are done. The results show that the convergence rate and the prediction accuracy of the proposed method are obviously higher than that of traditional gradient descent method. In addition, using the experimental data, the influence of soil macropores on slope solute loss has been further confirmed before the simulation.