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

arXiv: https://arxiv.org/abs/2511.15870 · HTML: https://mlsanity.com/paper/2511.15870/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 15 references that are in the map.
- First posted on arXiv 19 Nov 2025.

## Builds on (most cited first, 8 of 8)

- [Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting](https://mlsanity.com/paper/1709.04875/) (2017)
- [GMAN: A Graph Multi-Attention Network for Traffic Prediction](https://mlsanity.com/paper/1911.08415/) (2019)
- [Deciphering Spatio-Temporal Graph Forecasting: A Causal Lens and Treatment](https://mlsanity.com/paper/2309.13378/) (2023)
- [Integrating Large Language Models for Automated Structural Analysis](https://mlsanity.com/paper/2504.09754/) (2025)
- [SouLLMate: An Application Enhancing Diverse Mental Health Support with Adaptive LLMs, Prompt Engineering, and RAG Techniques](https://mlsanity.com/paper/2410.16322/) (2024)
- [LLM-based Online Prediction of Time-varying Graph Signals](https://mlsanity.com/paper/2410.18718/) (2024)
- [TigerGPT: A New AI Chatbot for Adaptive Campus Climate Surveys](https://mlsanity.com/paper/2504.13925/) (2025)
- [AI-Driven Forecasting and Monitoring of Urban Water System](https://mlsanity.com/paper/2510.06631/) (2025)

## Built on by (most cited first, 3 of 3)

- [FinInvest-GTCN: Explainable Graph-Temporal-Causal Modeling for Risk-Aware Investment Decision Optimization](https://mlsanity.com/paper/2606.28933/) (2026)
- [When Compliance Data Masquerades as Evaluation: Measurement Validity for Deployed AI Systems](https://mlsanity.com/paper/2609.13642/) (2026)
- [LLMersion: A Local-First AI Agent Framework for Low-Cost Home Language Learning toward Educational Equity](https://mlsanity.com/paper/2609.29672/) (2026)

## Abstract (arXiv)

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.