A Convolutional Graph Neural Network Model for Water Distribution Network Leakage Detection Based on Segment Feature Fusion Strategy

In this study, an innovative leak detection model based on Convolutional Graph Neural Networks (CGNNs) is proposed to enhance response speed during pipeline bursts and to improve detection accuracy. By integrating node features into pipe segment features, the model effectively combines CGNN with water distribution networks, achieving leak detection at the pipe segment level. Optimizing the receptive field and convolutional layers ensures high detection performance even with sparse monitoring device density. Applied to two representative water distribution networks in City H, China, the model was trained on synthetic leak data generated by EPANET simulations and validated using real-world leak events. The experimental results show that the model achieves 90.28% accuracy in high-density monitoring areas, and over 85% accuracy within three pipe segments of actual leaks in low-density areas (10%-20%). The impact of feature engineering on model performance is also analyzed and strategies are suggested for optimizing monitoring point placement, further improving detection efficiency. This research provides valuable technical support for the intelligent management of water distribution networks under resource-limited conditions.