Hierarchical Self-Attention Graph Pooling Networks for Leakage Detection of Water Distribution Networks

Leakage detection in urban water supply systems is crucial for saving water resources and ensuring social production. Unlike traditional data-driven methods, the approach introduced to leak detection in this study is based on graph neural networks, which can better explore the hidden information in the topology structure of the pipeline network itself. This article first simulates the leakage of a real pipeline network using EPANET to obtain experimental data and uses a hierarchical self-attention pooling graph neural network for graph classification. The experimental results indicated that this method has certain effectiveness in large-scale water supply networks.