AquaSentinel: Next-Generation AI System Integrating Sensor Networks for Urban Underground Water Pipeline Anomaly Detection via Collaborative MoE-LLM Agent Architecture

2025-11-19

AI Summary

Urban underground water distribution networks face critical threats to water resource and environmental security due to undetected leaks and seepage; conventional manual inspection suffers from limited coverage and delayed response. To address this, we propose a real-time anomaly detection framework integrating physics-informed modeling with sparse sensing. Our method features: (i) a centrality-driven sparse sensor deployment strategy; (ii) a physics-enhanced state augmentation mechanism coupled with a dual-threshold adaptive Real-Time Change-point Analysis (RTCA) algorithm; and (iii) a Mixture-of-Experts (MoE)-based spatiotemporal graph neural network augmented with causal flow analysis for precise leak source localization. Evaluated across 110 diverse leakage scenarios, the system achieves 100% detection accuracy. Crucially, it maintains performance parity with dense-sensor baselines under significantly reduced sensor density—substantially lowering monitoring costs. This work delivers a scalable, interpretable, AI-driven solution for intelligent urban water management.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Underground pipeline leaks and infiltrations pose significant threats to water security and environmental safety. Traditional manual inspection methods provide limited coverage and delayed response, often missing critical anomalies. This paper proposes AquaSentinel, a novel physics-informed AI system for real-time anomaly detection in urban underground water pipeline networks. We introduce four key innovations: (1) strategic sparse sensor deployment at high-centrality nodes combined with physics-based state augmentation to achieve network-wide observability from minimal infrastructure; (2) the RTCA (Real-Time Cumulative Anomaly) detection algorithm, which employs dual-threshold monitoring with adaptive statistics to distinguish transient fluctuations from genuine anomalies; (3) a Mixture of Experts (MoE) ensemble of spatiotemporal graph neural networks that provides robust predictions by dynamically weighting model contributions; (4) causal flow-based leak localization that traces anomalies upstream to identify source nodes and affected pipe segments. Our system strategically deploys sensors at critical network junctions and leverages physics-based modeling to propagate measurements to unmonitored nodes, creating virtual sensors that enhance data availability across the entire network. Experimental evaluation using 110 leak scenarios demonstrates that AquaSentinel achieves 100% detection accuracy. This work advances pipeline monitoring by demonstrating that physics-informed sparse sensing can match the performance of dense deployments at a fraction of the cost, providing a practical solution for aging urban infrastructure.

Problem

• Detecting underground water pipeline leaks and infiltrations in real-time
• Overcoming limited coverage and delayed response of manual inspections
• Providing cost-effective monitoring for aging urban water infrastructure

Innovation

• Sparse sensor deployment with physics-based state augmentation
• Dual-threshold RTCA algorithm for anomaly detection
• MoE ensemble of spatiotemporal graph neural networks