Review of Urban Drinking Water Contamination Source Identification Methods

Jinyu Gong, Xing Guo, Xuesong Yan () and Chengyu Hu
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Jinyu Gong: School of Computer Science, China University of Geosciences, 430078 Wuhan, China
Xing Guo: School of Computer Science, China University of Geosciences, 430078 Wuhan, China
Xuesong Yan: School of Computer Science, China University of Geosciences, 430078 Wuhan, China
Chengyu Hu: School of Computer Science, China University of Geosciences, 430078 Wuhan, China

Energies, 2023, vol. 16, issue 2, 1-14

Abstract: When drinking water flows into the water distribution network from a reservoir, it is exposed to the risk of accidental or deliberate contamination. Serious drinking water pollution events can endanger public health, bring about economic losses, and be detrimental to social stability. Therefore, it is obviously crucial to research the water contamination source identification problem, for which scholars have made considerable efforts and achieved many advances. This paper provides a comprehensive review of this problem. Firstly, some basic theoretical knowledge of the problem is introduced, including the water distribution network, sensor system, and simulation model. Then, this paper puts forward a new classification method to classify water contamination source identification methods into three categories according to the algorithms or methods used: solutions with traditional methods, heuristic methods, and machine learning methods. This paper focuses on the new approaches proposed in the past 5 years and summarizes their main work and technical challenges. Lastly, this paper suggests the future development directions of this problem.

Keywords: water distribution network; contamination source identification; heuristic algorithm; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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