基于图神经网络（GNN）的供水管网多漏点定位方法
Multiple Leak Localization in Water Distribution Networks through Graph Neural Networks
AI 导读
一项研究提出图神经网络（GNN）模型，可定位供水管网中数量不定的同时漏点，无需预先指定漏点数目或穷举漏点位置组合进行训练。模型在 31 节点 Hanoi 网络和 92 节点 NET3 网络上评估，涵盖需求变化、稀疏压力传感器覆盖和不同漏点规模等条件，准确率较多层感知机、SVM、K 近邻和随机森林分类器最高分别提升 12% 和 20%，且在两个网络上表现一致，支持方法的可扩展性。
正文 · 原文
Abstract:Leak localization in water distribution networks is challenging when multiple leaks occur simultaneously, particularly under sparse monitoring. Existing data-driven methods often assume a single or predefined number of leaks or require extensive enumeration of leak combinations. This study proposes a graph neural network (GNN) model that localizes a variable number of simultaneous leaks without specifying the leak count in advance or exhaustively generating every possible combination of leak locations for training. The model is evaluated on two benchmark networks of different sizes, the 31-node Hanoi network and the larger 92-node NET3 network, under variable demand, sparse pressure-sensor coverage, variable leak magnitudes, and varying numbers of simultaneous leaks. The GNN outperforms multilayer perceptron, support vector machine, K-nearest neighbors, and random forest classifiers by up to 12% and 20% in accuracy respectively in Hanoi and NET3 networks. Consistent performance across both networks supports the scalability of the proposed approach.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.35972 [cs.LG] |
| (or arXiv:2610.35972v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.35972 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Arvin Ajoodani [view email]
[v1]
Mon, 26 Oct 2026 07:16:01 UTC (654 KB)
来源：arXiv cs.AI · arxiv.org