优化传感器位置可显著提升图神经网络漏水检测效果
The impact of sensor placement on graph-neural-network-based leakage detection
- 基于页面排名中心性设计新型传感器部署方法
- 在EPANET Net1上显著提升压力重建与漏水检测性能
- 适合水务系统优化与智能监测研究者参考
供水管网中的漏水检测传感器部署是水务公司面临的重要实际挑战。近期研究表明，图神经网络可估算压力并预测泄漏，但其性能高度依赖于传感器测量数据及其配置。本文研究传感器布置对基于GNN的漏水检测性能的影响，提出一种新的基于页面排名中心性的传感器部署方法，并在EPANET Net1上验证该方法显著提升了压力重建、预测及漏水检测效果。
原文摘要 · Abstract (English)
Sensor placement for leakage detection in water distribution networks is an important and practical challenge for water utilities. Recent work has shown that graph neural networks can estimate and predict pressures and detect leaks, but their performance strongly depends on the available sensor measurements and configurations. In this paper, we investigate how sensor placement influences the performance of GNN-based leakage detection. We propose a novel PageRank-Centrality-based sensor placement method and demonstrate that it substantially impacts reconstruction, prediction, and leakage detection on the EPANET Net1.
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