Title: A prediction method of urban water pollution based on improved BP neural network

Authors: Feng Liu; Bing Han; Weifeng Qin; Liang Wu; Sumin Li

Addresses: School of Geosciences and Info-Physics, Central South University, Changsha 410083, China ' School of Geosciences and Info-Physics, Central South University, Changsha 410083, China ' School of Geosciences and Info-Physics, Central South University, Changsha 410083, China ' School of Geosciences and Info-Physics, Central South University, Changsha 410083, China ' School of Architecture, Changsha University of Science and Technology, Changsha 410002, China

Abstract: The existing methods for urban water pollution prediction have some problems, such as large prediction error and inconsistency with the actual pollution situation. A new urban water pollution prediction method is proposed. The water pollution data collection system of mobile GIS is used to collect urban water pollution data, analyse the overall structure of the water pollution data collection system, and classify the obtained urban water pollution data at different levels. The application concept of BP neural network is clarified, and the obtained urban water pollution data is entered into the network to obtain the urban water pollution prediction results. Genetic algorithm is used to improve the weights and thresholds obtained above, and the urban water pollution prediction model is constructed, and the prediction results of urban water pollution are output. Through the effective experimental analysis, it is concluded that the minimum error value is about 0.1%, and the prediction time is consistent with the actual time consumption.

Keywords: urban water pollution; data acquisition; bp neural network; genetic algorithm; threshold.

DOI: 10.1504/IJETM.2021.116829

International Journal of Environmental Technology and Management, 2021 Vol.24 No.3/4, pp.294 - 306

Received: 29 Aug 2020
Accepted: 05 Dec 2020
Published online: 03 Aug 2021 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article