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Review of Urban Drinking Water Contamination Source Identification Methods

Author

Listed:
  • 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)

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.

Suggested Citation

  • Jinyu Gong & Xing Guo & Xuesong Yan & Chengyu Hu, 2023. "Review of Urban Drinking Water Contamination Source Identification Methods," Energies, MDPI, vol. 16(2), pages 1-14, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:705-:d:1028100
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    References listed on IDEAS

    as
    1. Chao Wang & Shiyu Zhou, 2017. "Contamination source identification based on sequential Bayesian approach for water distribution network with stochastic demands," IISE Transactions, Taylor & Francis Journals, vol. 49(9), pages 899-910, September.
    2. D. Costa & L. Melo & F. Martins, 2013. "Localization of Contamination Sources in Drinking Water Distribution Systems: A Method Based on Successive Positive Readings of Sensors," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(13), pages 4623-4635, October.
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