arXiv cs.AI· Arvin Ajoodani, Sara Nazif, Pouria Ramazi·· 2 天前AI 评分20
图神经网络实现供水管网多漏点定位，无需预设漏点数
Multiple Leak Localization in Water Distribution Networks through Graph Neural Networks
AI 导读
一项研究提出基于图神经网络的模型，可在不预设漏点数量、不穷举漏点组合的情况下定位供水管网中同时发生的多个泄漏。模型在31节点Hanoi网络和92节点NET3网络上测试，准确率分别比多层感知机、SVM、KNN和随机森林等分类器最高提升12%和20%。两个规模不同网络的稳定表现支持该方法的可扩展性。
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