Modeling contaminant intrusion in water distribution networks: A new similarity-based DST method |
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| Authors: | Yong Deng Wen Jiang Rehan Sadiq |
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| Affiliation: | 1. College of Computer and Information Sciences, Southwest University, Chongqing 400715, China;2. School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China;3. School of Engineering, University of British Columbia Okanagan, 3333 University Way, Kelowna, BC, Canada V1V 1V7;1. Department of Urban Engineering, School of Engineering, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan;2. Hitachi, Ltd., 6-6, Marunouchi 1, Chiyoda-ku, Tokyo, 100-8280, Japan;3. Fukushima Medical University, 1, Hikariga-oka, Fukushima City, 960-1295, Japan;4. National Institute of Public Health, 2-3-6 Minami, Wako-shi, Saitama 351-0197, Japan;5. Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro, Tokyo, 153-8505, Japan;1. College of Information Engineering, Northwest A&F University, Yangling, Shaanxi 712100, China;2. Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling, Shaanxi 712100, China;3. Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service, Yangling, Shaanxi 712100, China |
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| Abstract: | ![]()
Contaminant intrusion in a water distribution network is a complex but a commonly observed phenomenon, which depends on three elements – a pathway, a driving force and a contamination source. However, the data on these elements are generally incomplete, non-specific and uncertain. In an earlier work, Sadiq, Kleiner, and Rajani (2006) have successfully applied traditional Dempster–Shafer theory (DST) to estimate the “risk” of contaminant intrusion in a water distribution network based on limited uncertain information. However, the method used for generating basic probability assignment (BPA) was not very flexible, and did not handle and process uncertain information effectively. In this paper, a more pragmatic method is proposed that utilizes “soft” computing flexibility to generate BPAs from uncertain information. This paper compares these two methods through numerical examples, and demonstrates the efficiency and effectiveness of modified method. |
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