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 physics-informed (PI) 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 F 1 = 0 . 979 , showing 3.3pp gain and high robustness under 15% parameter noise.
Homaei et al. (Fri,) studied this question.