Environmental Science and Pollution Research

, Volume 26, Issue 18, pp 17901–17910 | Cite as

Pollution source localization in an urban water supply network based on dynamic water demand

  • Xuesong Yan
  • Zhixin Zhu
  • Tian LiEmail author
Environmental Pollution and Energy Management


Urban water supply networks are susceptible to intentional, accidental chemical, and biological pollution, which pose a threat to the health of consumers. In recent years, drinking-water pollution incidents have occurred frequently, seriously endangering social stability and security. The real-time monitoring for water quality can be effectively implemented by placing sensors in the water supply network. However, locating the source of pollution through the data detection obtained by water quality sensors is a challenging problem. The difficulty lies in the limited number of sensors, large number of water supply network nodes, and dynamic user demand for water, which leads the pollution source localization problem to an uncertainty, large-scale, and dynamic optimization problem. In this paper, we mainly study the dynamics of the pollution source localization problem. Previous studies of pollution source localization assume that hydraulic inputs (e.g., water demand of consumers) are known. However, because of the inherent variability of urban water demand, the problem is essentially a fluctuating dynamic problem of consumer’s water demand. In this paper, the water demand is considered to be stochastic in nature and can be described using Gaussian model or autoregressive model. On this basis, an optimization algorithm is proposed based on these two dynamic water demand change models to locate the pollution source. The objective of the proposed algorithm is to find the locations and concentrations of pollution sources that meet the minimum between the analogue and detection values of the sensor. Simulation experiments were conducted using two different sizes of urban water supply network data, and the experimental results were compared with those of the standard genetic algorithm.


Pollution source localization Dynamic water demand Genetic algorithm Gaussian model Autoregressive model Simulation optimization 


Funding information

This paper is supported by Natural Science Foundation of China (No. 61673354, 61672474) and the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan).


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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.School of Computer ScienceChina University of GeosciencesWuhanChina

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