| contributor author | Guan, Yihong | |
| contributor author | Zhang, Wei | |
| contributor author | Lv, Mou | |
| contributor author | Cui, Lingzhi | |
| contributor author | Qiao, Peng | |
| contributor author | Zhao, Huan | |
| contributor author | Li, Hang | |
| date accessioned | 2026-08-20T12:06:16Z | |
| date available | 2026-08-20T12:06:16Z | |
| date copyright | 2025/12/23 | |
| date issued | 2026 | |
| identifier other | JPSEA2.PSENG-1944.pdf | |
| identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4313102 | |
| description abstract | AbstractCompanies 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 ... | |
| publisher | American Society of Civil Engineers | |
| title | Leakage Detection Using Probabilistic Neural Networks and Model-Based Localization Using Quantum Genetic Algorithms in Real Water Supply Networks | |
| type | Journal Article | |
| journal volume | 17 | |
| journal issue | 2 | |
| journal title | Journal of Pipeline Systems Engineering and Practice | |
| identifier doi | 10.1061/JPSEA2.PSENG-1944 | |
| journal fristpage | 04025111-1 | |
| journal lastpage | 04025111-10 | |
| page | 10 | |
| tree | Journal of Pipeline Systems Engineering and Practice:;2026:;Volume ( 017 ):;issue: 002 | |
| contenttype | Fulltext | |