Research on water supply network pressure prediction using PSO-BP neural network algorithm

• Published in: Proceedings of SPIE, the international society for optical engineering Vol. 13395; pp. 1339532 - 1339532-7
• Main Authors: Wang, Chaoran, Wang, Changtao, Shan, Dan, Yuan, Baolong
• Format: Conference Proceeding
• Language: English
• Published: SPIE 12.11.2024
• ISBN: 9781510685437; 151068543X
• ISSN: 0277-786X
• DOI: 10.1117/12.3049406

Ensuring the accurate prediction of water supply network pressure is crucial for the efficient operation of the water supply system. The purpose is to address the issue of insufficient accuracy in short-term forecasting. The method of short-term prediction for network pressure nodes using the Particle Swarm Optimization-Back Propagation (PSO-BP) neural network algorithm is proposed in this paper. Firstly, the real pressure data collected from a water supply network, upon which this paper relies, is cleaned using the Cook’s distance method. After the dirty data is removed, Back Propagation (BP), Genetic Algorithm-Back Propagation (GA-BP), and PSO-BP are employed to predict the pressure in the water supply network. By comparing the prediction results, PSO-BP was found to be the most accurate among the three algorithms, with an RMSE (Root Mean Square Error) of 1.9132. In order to solve the problem of insensitive regions in pressure prediction, a variable sliding window method is proposed to determine the data set based on the previous method. The results indicate that this method can effectively improve the accuracy of prediction in insensitive areas.