AI-Driven Forecasting and Monitoring of Urban Water System

2025-10-08

AI Summary

Urban underground sewer networks suffer from leakage and infiltration, causing water loss, environmental pollution, and high operational costs. Conventional detection methods are inefficient, while dense sensor deployment is economically infeasible. This paper proposes a lightweight monitoring framework integrating artificial intelligence and remote sensing: sparse flow and water-depth sensors collect spatiotemporal hydraulic data; a directed graph model is constructed incorporating pipe attributes (e.g., material, diameter, slope); and an edge-aware message-passing mechanism—tightly coupled with hydraulic simulation—drives a graph neural network (HydroNet) for high-accuracy, system-wide state inference. Evaluated on a real-world campus wastewater network dataset, the approach significantly reduces hardware cost and enhances scalability, achieving superior leakage localization and prediction accuracy compared to state-of-the-art baselines.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Remote Sensing / Geospatial AI
• Intelligent Robots: State Estimation

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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.

Problem

• Detecting leaks in underground water pipelines efficiently
• Overcoming costly sensor deployments and manual inspections
• Improving prediction accuracy with sparse sensor data

Innovation

• Sparse remote sensors capture real-time pipeline data
• HydroNet model uses directed graph for precise modeling
• Edge-aware message passing enables accurate network predictions