Study of Discharge Model in South-to-North Water Diversion Middle Route Project Based on Radial Basis Function Neural Network

Abstract

Abstract. The technology for water dispatch is very complex in South-to-North Water Diversion Middle Route Project, and it is necessary to take advantage of automation system for water delivery. The model for calculating flow rate is important to water dispatch, but traditional method often needs to rectify parameters manually. A model based on radial basis function neural network is established to describe the relationship between water level, gate opening and flux. The model uses the network to simulate the optimal function between water level, gate opening and flux coefficient, and calculates the flow rate by the coefficient. By taking the new method into South-to-North Water Diversion Middle Route Project and comparing the neural network model with traditional methods, the results show that the radial basis function neural network model has higher accuracy and efficiency

Study on Urban Rainfall–Runoff Model under the Background of Inter-Basin Water Transfer

“…As areas with a highly concentrated population, cities alleviate many problems, such as water resource shortages, through inter-basin water transfer projects on the one hand, while on the other hand, they are inevitably affected by other impacts brought by interbasin water transfer [26], mainly manifested as changes in water quality [27] and river hydrological factors [26,[28][29][30][31]. However, changes in hydrological elements such as river runoff and river water levels are related to urban flood control and drainage [32].…”