用稀疏传感器+AI模型，精准监测地下管网泄漏
AI-Driven Forecasting and Monitoring of Urban Water System
- 用稀疏远程传感器采集流量与水位数据，结合管道属性建模
- 在真实校园污水网络上，检测精度超越现有先进方法
- 适合城市水务部门用于低成本高效率的管网监控
地下供水与污水管道是城市运行的关键，但泄漏和渗入等问题导致大量水资源损失、环境破坏及高昂维修成本。传统人工巡检效率低，密集传感器部署又代价过高。近年来，人工智能在城市基础设施中应用日益广泛。本研究提出一种融合AI与远程传感器的集成框架，通过部署少量远程传感器实时获取流量与水位数据，并结合HydroNet——一种利用管道属性（如材质、管径、坡度）构建有向图的专用模型，实现更高精度的建模。在真实校园污水网络数据集上的评估表明，该系统能有效收集时空水力数据，使HydroNet性能优于多个先进基线模型。通过边缘感知的消息传递机制与水力模拟的结合，仅依赖有限传感器即可实现全网精准预测。该方法有望广泛应用于各类地下供水管网系统。
原文摘要 · Abstract (English)
Underground water and wastewater pipelines are vital for city operations but plagued by anomalies like leaks and infiltrations, causing substantial water loss, environmental damage, and high repair costs. Conventional manual inspections lack efficiency, while dense sensor deployments are prohibitively expensive. In recent years, artificial intelligence has advanced rapidly and is increasingly applied to urban infrastructure. In this research, we propose an integrated AI and remote-sensor framework to address the challenge of leak detection in underground water pipelines, through deploying a sparse set of remote sensors to capture real-time flow and depth data, paired with HydroNet - a dedicated model utilizing pipeline attributes (e.g., material, diameter, slope) in a directed graph for higher-precision modeling. Evaluations on a real-world campus wastewater network dataset demonstrate that our system collects effective spatio-temporal hydraulic data, enabling HydroNet to outperform advanced baselines. This integration of edge-aware message passing with hydraulic simulations enables accurate network-wide predictions from limited sensor deployments. We envision that this approach can be effectively extended to a wide range of underground water pipeline networks.
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