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dc.contributor.authorGuan, Yihong
dc.contributor.authorZhang, Wei
dc.contributor.authorLv, Mou
dc.contributor.authorCui, Lingzhi
dc.contributor.authorQiao, Peng
dc.contributor.authorZhao, Huan
dc.contributor.authorLi, Hang
dc.date.accessioned2026-08-20T12:06:16Z
dc.date.available2026-08-20T12:06:16Z
dc.date.copyright2025/12/23
dc.date.issued2026
dc.identifier.otherJPSEA2.PSENG-1944.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4313102
dc.description.abstractAbstractCompanies are making significant efforts to improve leakage detection efficiency. Therefore, this paper proposes the identification of leak zones using a probabilistic neural network (PNN) and model-based localization in real water supply ...The first figure is a flowchart of the research methods in the paper, and the second figure is a comparison chart of the leak location results of the two methods. Compared with the traditional model-based localization using QGA, the accuracy of the PNN-...Practical ApplicationsThe leak detection method of water supply network based on probabilistic neural network (PNN) and quantum genetic algorithm (QGA) proposed in this paper could significantly improve the efficiency and accuracy of leak location. By ...
dc.publisherAmerican Society of Civil Engineers
dc.titleLeakage Detection Using Probabilistic Neural Networks and Model-Based Localization Using Quantum Genetic Algorithms in Real Water Supply Networks
dc.typeJournal Article
dc.journal.volume17
dc.journal.issue2
dc.journal.titleJournal of Pipeline Systems Engineering and Practice
dc.identifier.doi10.1061/JPSEA2.PSENG-1944
dc.journal.fristpage04025111-1
dc.journal.lastpage04025111-10
dc.page10
dc.treeJournal of Pipeline Systems Engineering and Practice:;2026:;Volume ( 017 ):;issue: 002
dc.contenttypeFulltext


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