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Leak Detection in Water Supply Network Using a Data-Driven Improved Graph Convolutional Network
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DOI：10.1109/ACCESS.2023.3326470.png)
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摘要
En 中文
Due to the complex correlation within data collection, it is a challenging task to detect leakage in the water supply network. The Graph Convolutional Network (GCN) has recently gained significant attention in correlation research. However, most existing GCN-based models assumed that the topology of whole network should be derived from expert knowledge, which is always time-consuming and difficult to acquire. To tackle this problem, a data-driven improved graph convolutional network (IGCN) is proposed based on a self-learning fully connected association graph. Compared with traditional GCN, the proposed model can adaptively learn necessary data relationship information without accurate fixed undirected association graph. On this basis, a leakage-detection IGCN (LD-IGCN) is carried out for leakage detection. Finally, two case studies of water supply networks are utilized to demonstrate the efficiency of the proposed leakage detection method in this paper.
Keyword:
Data models
Feature extraction
Convolutional neural networks
Mathematical models
Correlation
Adaptation models
Water resources
Leakage currents
Leakage detection
deep learning
graph convolutional network
adaptive
correlation mining
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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