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Burst Detection Using an Artificial Immune Network in Water-Distribution Systems
A new method using artificial immune network is presented to identify pipe burst in water-distribution systems. Burst detection is considered as the problem of pattern recognition in the proposed method. An artificial database that includes information on burst events (BEs) is first established. Using the clonal selection algorithm, the artificial immune network is constructed based on the principle of immune system. The burst location is finally identified using the nearest neighbor method. Three offline case studies are illustrated in detail to evaluate the current method. A total of five possible burst locations are identified from 34 nodes in Case Study 1, whereas four possible burst locations are identified from 77 nodes in Case Study 2. The results derived from the first two case studies show that the method can identify the possible burst areas, including the true burst location, using model-simulated results. The data derived from real BEs in Case Study 3 are used to evaluate the proposed method, through which performance of the method is further investigated. Based on all three case studies, the proposed method has the potential to be a useful tool for burst detection.
Burst Detection Using an Artificial Immune Network in Water-Distribution Systems
A new method using artificial immune network is presented to identify pipe burst in water-distribution systems. Burst detection is considered as the problem of pattern recognition in the proposed method. An artificial database that includes information on burst events (BEs) is first established. Using the clonal selection algorithm, the artificial immune network is constructed based on the principle of immune system. The burst location is finally identified using the nearest neighbor method. Three offline case studies are illustrated in detail to evaluate the current method. A total of five possible burst locations are identified from 34 nodes in Case Study 1, whereas four possible burst locations are identified from 77 nodes in Case Study 2. The results derived from the first two case studies show that the method can identify the possible burst areas, including the true burst location, using model-simulated results. The data derived from real BEs in Case Study 3 are used to evaluate the proposed method, through which performance of the method is further investigated. Based on all three case studies, the proposed method has the potential to be a useful tool for burst detection.
Burst Detection Using an Artificial Immune Network in Water-Distribution Systems
Tao, Tao (author) / Huang, Haidong (author) / Li, Fei (author) / Xin, Kunlun (author)
2013-08-27
Article (Journal)
Electronic Resource
Unknown
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