Smart water grid technology based on deep learning: a review

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.