Improved Neural Network Model and Its Application in Hydrological Simulation

Abstract

When applying a back-propagation neural network (BPNN) model in hydrological simulation, researchers generally face three problems. The first one is that real-time correction mode must be adopted when forecasting basin outlet flow, i.e., observed antecedent outlet flows must be utilized as part of the inputs of the BPNN model. Under this mode, outlet flow can only be forecasted one time step ahead, i.e., continuous simulation cannot be implemented. The second one is that topology, weights, and biases of BPNN cannot be optimized simultaneously by traditional training methods. Topology designed by the trial-and-error method and weights and biases trained by back-propagation (BP) algorithm are not always global optimal and the optimizations are experience-based. The third one is that simulation accuracy for the validation period is usually much lower than that for the calibration period, i.e., generalization property of BPNN is not good. To solve these problems, a novel coupled black-box model named BK (BP-KNN) and a new methodology of calibration are proposed in this paper. The BK model was developed by coupling BPNN model with