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arXiv · 2601.12426

Graph Attention Networks with Physical Constraints for Anomaly Detection

Mohammadhossein Homaei·Iman Khazrak·Ruben Molano·Andres Caro·Mar Avila
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Abstract

Water distribution systems (WDSs) face increasing cyber-physical risks, which make reliable anomaly detection essential. Many data-driven models ignore network topology and are hard to interpret, while model-based ones depend strongly on parameter accuracy. This work proposes a hydraulic-aware graph attention network using normalized conservation law violations as features. It combines mass and energy balance residuals with graph attention and bidirectional LSTM to learn spatio-temporal patterns. A multi-scale module aggregates detection scores from node to network level. On the BATADAL dataset, it reaches $F1=0.979$, showing $3.3$pp gain and high robustness under $15\%$ parameter noise.

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Mohammadhossein Homaei, Iman Khazrak, Ruben Molano, Andres Caro, Mar Avila. 2026-01-18. Graph Attention Networks with Physical Constraints for Anomaly Detection. https://doi.org/10.1016/j.icte.2026.05.004

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