Title: Smart water grid technology based on deep learning: a review

Authors: Huan Wu; Lin Peng; Feng Jiang; Shuiping Cheng; Jie Chen; Linda Yan

Addresses: College of Environmental Science and Engineering, Tongji University, Shanghai, China; T.Y. Lin International Engineering Consulting (China) Co., Ltd., Chongqing, China ' School of Big Data and Software Engineering, Chongqing University, Chongqing, China ' School of Finance and Management, Chongqing Business Vocational College, Chongqing, China ' College of Environmental Science and Engineering, Tongji University, Shanghai, China ' College of Environment and Ecology, Chongqing University, Chongqing, China; T.Y. Lin International Engineering Consulting (China) Co., Ltd., Chongqing, China ' School of Engineering, University of Portsmouth, Portsmouth, Hampshire PO1 2UP, UK

Abstract: In recent years, the development of deep learning technology has made breakthroughs in computer vision, natural language processing and other fields. The Smart Water Grid (SWG) technology based on deep learning has also been a hot area of research in recent years. It has achieved better performance in the related detection and prediction of urban pipe networks. Therefore, this survey paper presents an extensive review of the application of deep learning to several different issues related to the SWG. This paper emphasises feasibility studies and summarises the state-of-the-art development in this field from a technical point of view, which consists of pipeline leakage and burst detection, contamination source identification and water demand forecasting. Furthermore, this paper also proposes challenges and future directions in these key research areas, demonstrating that deep learning-based SWG technology is still an emerging and encouraging research field.

Keywords: smart water grid; pipeline leakage and burst detection; contamination source identification; water demand forecasting; deep learning.

DOI: 10.1504/IJWMC.2022.124825

International Journal of Wireless and Mobile Computing, 2022 Vol.22 No.3/4, pp.338 - 349

Received: 24 Oct 2021
Received in revised form: 02 Mar 2022
Accepted: 07 Mar 2022
Published online: 09 Aug 2022 *

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