Flood Forecasting Method of Small and Medium-sized Watershed Based on Convolutional Neural Network

Abstract

A data-driven flood forecasting model based on a convolution neural network is proposed for the small and medium-sized watershed. Pearson correlation coefficient analysis was used to determine the time length of the input data. The historical rainfall and discharge were used to create the two-dimensional input data matrix. NAS was used to determine the structure of the model. The experiment results in Tunxi watershed in Anhui Province show that the accuracy is high under the 1 3h forecast period, with the forecast period increased, accuracy would decrease.