Affiliations: [a] School of Computer Science, China University of Geosciences, Wuhan, Hubei, China | [b] Hubei Provincial Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, Hubei, China | [c] School of Computer Science and Engineering, Wuhan Institute of Technology, Wuhan, Hubei, China
Correspondence:
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Corresponding author: Qinghua Wu, Hubei Provincial Key Laboratory of Intelligent Robot, Wuhan Institute of Technology, Wuhan, Hubei, China. Tel.: +86 18908632572; Fax: +86 027 67883716; E-mail:[email protected]
Abstract: In recent years, drinking water contamination incidents
happen frequently, which is a serious threat to social stability and
security. By placing the sensors for real-time monitoring of water quality
in urban water supply, it can greatly reduce the probability of incidents of
contamination. But how to use the information collected by water quality
sensors to identify the contaminant source is a challenging problem. In this
paper, we formulated the contaminant source identification problem into an
optimization problem, and used hybrid encoding method to code the problem
according to the properties of a variable, so as to improve the convergence
speed and accuracy. We used different size of pipe network data in
experiment, which results finally verified the validity and robustness of
the proposed method.
Keywords: Water distribution network, contaminant source identification, optimization, genetic algorithm, hybrid encoding
