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Real-time localization of pollution source for urban water supply network in emergencies

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Abstract

In recent years, drinking water pollution happens from time to time, severely endangering social stability and residents’ life. By placing sensors in urban water supply pipes to monitor water quality in real time, the negative impacts of pollution accidents can be reduced to a great degree. However, how to localize pollution source in real time according to the detection information from water quality sensors is still a challenging topic. The difficulty is that when the pollution is detected, the pollution information collected by sensors is insufficient to localize the pollution source. This paper mainly studies the real-time localization of the source of paroxysmal pollution when water demand is uncertain. Many previous studies adopted the simulation-optimization method to simulate pollution event in a fixed period of time after it happens, then localize the pollution source reversely using all the simulation data; however, when the pollution source has been detected, real-time localization by simulation-optimization method can shorten the time of simulation and thus deal with the pollution in real time to reduce its harm. This paper first describes the problem of real-time paroxysmal pollution source localization and provides the diagram of simulation-optimization model to the problem; then the design of real-time localization algorithm is proposed with consideration of objective function; at last, experiments are carried out on pipe networks of different scales, and the results show that compared to traditional pollution source localization method, the real-time pollution source localization method can find the true pollution event with less sensor data in a shorter period of time.

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Acknowledgements

This paper is supported by Natural Science Foundation of China (No. 61673354), the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) and the State Key Lab of Digital Manufacturing Equipment & Technology.

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Authors and Affiliations

  1. School of Computer Science, China University of Geosciences, Wuhan, 430074, Hubei, China

    Xuesong Yan, Tian Li & Chengyu Hu

  2. State Key Lab of Digital Manufacturing Equipment & Technology, Wuhan, 430074, Hubei, China

    Xuesong Yan

  3. Faculty of Computer Science and Engineering, WuHan Institute of Technology, Wuhan, 430205, Hubei, China

    Qinghua Wu

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  1. Xuesong Yan
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Correspondence to Qinghua Wu.

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Yan, X., Li, T., Hu, C. et al. Real-time localization of pollution source for urban water supply network in emergencies. Cluster Comput 22 (Suppl 3), 5941–5954 (2019). https://doi.org/10.1007/s10586-018-1725-y

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