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Contaminant source identification of water distribution networks using cultural algorithm
Author(s) -
Yan Xuesong,
Gong Wenyin,
Wu Qinghua
Publication year - 2017
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4230
Subject(s) - robustness (evolution) , computer science , water quality , identification (biology) , water supply , pollution , optimization algorithm , water pollution , water supply network , water source , stability (learning theory) , wireless sensor network , data mining , environmental science , algorithm , environmental engineering , mathematical optimization , water resource management , machine learning , mathematics , ecology , biochemistry , chemistry , botany , computer network , gene , environmental chemistry , biology
Summary In recent years, the drinking water pollution incident occurred frequently, a serious threat to social stability and security. By using the sensor networks real‐time monitoring the urban water supply networks, the water pollution event probability can be greatly reduced. But knowing how to use the water quality monitor sensor networks to collect information to identify pollution sources is still a challenging problem. In this paper, we formulate the contaminant source identification problem into an optimization problem, and then design the cultural algorithm to solve it by considering different sizes of water supply networks as the experimental data. Finally, the experimental results verify the effectiveness and robustness of the proposed method.