The prediction of daily water demand based on fuzzy neural network with an improved Levenberg-Marquardt algorithm

Abstract

Short-term prediction of water demand provides basic guarantee of water supply system operation and management. In this study, an effective model for daily water demand forecasting is proposed. Firstly, principle component analysis (PCA) is utilized to simplify the complexity and reduce the correlation between influence variables, and the score values of selected principle components (PCs) turn into the irrelevant input data of fuzzy neural network (FNN), which models the prediction of water demand. Moreover, an improved Levenberg-Marquardt (ILM) algorithm is employed to optimize the parameters of FNN simultaneously. Quassi-Hessian and gradient matrices could be calculated directly without the storage and multiplication of whole Jaccobian matrix, therefore the problems of heavy computing burden and limited memory space could be solved. At last, contrast experiments are implemented to demonstrate the fuzzy neural network with Levenberg-Marquardt algorithm (ILM-FNN) has better prediction performance and capability to handle practical issues.