Abstract:Aiming at the problems of low accuracy and over-fitting of traditional neural network model in flood forecasting process, this study takes the monthly average water level of Waizhou Hydrological Station in Ganjiang River Basin as the research object, and proposes a flood forecasting model based on regularized GRU neural network to improve the accuracy of flood forecasting. Relu function is selected as the output layer activation function of the whole neural network. To improve the generalization performance of GRU model, regularization of elastic network is introduced into GRU model, and regularizes the input weights in the network. The model is applied to the fitting and prediction of the monthly average water level at Waizhou Hydrological Station, and the experimental comparison shows that the model optimized by regularization of elastic network has a higher fitting degree, the qualified rate is increased by 9.3%, and the calculated root mean square error is small.