Water Demand Prediction Model Based on Radial Basis Function Neural Network

Abstract

Artificial Neural Network (ANN) simulates the structure and function of human brain. It has the abilities of parallel information processing, distributed storage and self-learning and reasoning. ANN features fault tolerance, nonlinearity, nonlocality, nonconvexity, etc., and is suitable for identifying and mapping fuzzy information or complex nonlinear relationship. Combined with the characteristics of domestic water consumption, industry water consumption and agriculture water consumption, the influencing factors are analyzed. A Radial Basis Function (RBF) Neural Network model is established for water demand prediction, using 17 water demand predication factors as input of the network. On output layer, the four nodes include urban household water demand, rural household water demand, industrial water demand and agricultural water demand. Dynamic Clustering Learning algorithm is used to determine RBF width, cluster center, number of nodes in hidden layer and weight. The number of hidden layer determined by network learning is 8. The relative error of three years are 2.74%, 3.33% and 1.41% respectively. The results show that RBF neural network has such advantages that the output is independent the initial weight value and the convergence speed is faster. And a better forecasting result is achieved through such a model.