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contributor authorGuan, Yihong
contributor authorZhang, Wei
contributor authorLv, Mou
contributor authorCui, Lingzhi
contributor authorQiao, Peng
contributor authorZhao, Huan
contributor authorLi, Hang
date accessioned2026-08-20T12:06:16Z
date available2026-08-20T12:06:16Z
date copyright2025/12/23
date issued2026
identifier otherJPSEA2.PSENG-1944.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4313102
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 ...
publisherAmerican Society of Civil Engineers
titleLeakage Detection Using Probabilistic Neural Networks and Model-Based Localization Using Quantum Genetic Algorithms in Real Water Supply Networks
typeJournal Article
journal volume17
journal issue2
journal titleJournal of Pipeline Systems Engineering and Practice
identifier doi10.1061/JPSEA2.PSENG-1944
journal fristpage04025111-1
journal lastpage04025111-10
page10
treeJournal of Pipeline Systems Engineering and Practice:;2026:;Volume ( 017 ):;issue: 002
contenttypeFulltext


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