关键词: 集成学习, 神经网络, 洪水预报, 中小河流

Abstract: In order to solve the problems that the traditional data-driven flood forecasting method has large prediction error and the subnetworks in the traditional ensemble learning forecasting method can’t interact with each other, on the basis of single model predisction, the heterogeneous BP, CNN, LSTM neural networks are selected to establish a neural network integrated flood forecasting model based on negative correlation learning, and the overall error-variance decomposition and bifurcation decomposition of the model are carried out by explicitly adding regularization term, which makes the subnetworks in the integrated neural network incompletely independent,  so as to ensure the diversity of the ensemble model and improve the prediction accuracy of the final model. The experiment in Tunxi basin of Anhui Province shows that the model based on negative correlation learning can effectively forecast the flood process, and the prediction accuracy is higher than the traditional single model.

Key words: ensemble learning, neural network, flood forecasting, small and medium rivers