Pipeline Leak Identification and Prediction of Urban Water Supply Network System with Deep Learning Artificial Neural Network
Pipeline leakage, which leads to water wastage, financial losses, and contamination, is a significant challenge in urban water supply networks. Leak detection and prediction is urgent to secure the safety of the water supply system. Relaying on deep learning artificial neural networks and a specific...
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| Veröffentlicht in: | Water (Basel) 2024-10, Vol.16 (20), p.2903 |
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| creator | Xi, Fei Liu, Luyi Shan, Liyu Liu, Bingjun Qi, Yuanfeng |
| description | Pipeline leakage, which leads to water wastage, financial losses, and contamination, is a significant challenge in urban water supply networks. Leak detection and prediction is urgent to secure the safety of the water supply system. Relaying on deep learning artificial neural networks and a specific optimization algorithm, an intelligential detection approach in identifying the pipeline leaks is proposed. A hydraulic model is initially constructed on the simplified Net2 benchmark pipe network. The District Metering Area (DMA) algorithm and the Cuckoo Search (CS) algorithm are integrated as the DMA-CS algorithm, which is employed for the hydraulic model optimization. Attributing to the suspected leak area identification and the exact leak location, the DMA-CS algorithm possess higher accuracy for pipeline leakage (97.43%) than that of the DMA algorithm (92.67%). The identification pattern of leakage nodes is correlated to the maximum number of leakage points set with the participation of the DMA-CS algorithm, which provide a more accurate pathway for identifying and predicting the specific pipeline leaks. |
| doi_str_mv | 10.3390/w16202903 |
| format | Article |
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Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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Leak detection and prediction is urgent to secure the safety of the water supply system. Relaying on deep learning artificial neural networks and a specific optimization algorithm, an intelligential detection approach in identifying the pipeline leaks is proposed. A hydraulic model is initially constructed on the simplified Net2 benchmark pipe network. The District Metering Area (DMA) algorithm and the Cuckoo Search (CS) algorithm are integrated as the DMA-CS algorithm, which is employed for the hydraulic model optimization. Attributing to the suspected leak area identification and the exact leak location, the DMA-CS algorithm possess higher accuracy for pipeline leakage (97.43%) than that of the DMA algorithm (92.67%). 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| subjects | Accuracy Algorithms Artificial intelligence China Costs Deep learning Design and construction Efficiency Fiber optics Genetic algorithms Identification Leak detection Localization Mathematical optimization Methods Neural networks Optimization algorithms Pipe lines Pipelines Sensors Urban development Water shortages Water supply |
| title | Pipeline Leak Identification and Prediction of Urban Water Supply Network System with Deep Learning Artificial Neural Network |
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