Ensuring high-quality water resources is crucial for sustainable urban development, public health, and resilient city infrastructure, yet traditional anomaly detection methods struggle with the highly variable, non-stationary, and concept-drifting nature of urban water quality data streams. This study proposes a Physics-Informed Dynamic Ensemble Learning (PIDEL) framework, an artificial intelligence approach that combines diverse classical and deep learning models with Physics-Informed Neural Networks (PINNs) embedding convection–diffusion constraints, a Genetic Algorithm (GA) for ensemble optimization, and a Jensen–Shannon Divergence (JSD) based mechanism for dynamic model switching. Applied to a real-world urban water quality dataset, PIDEL achieves an F1-score of 0.95, representing a 59% improvement over the best static ensemble, while reducing false alarms by 73% compared to traditional methods and maintaining F1-scores above 0.9 across all sliding windows. The framework processes each 60-minute window in approximately 2.3 s on standard hardware, demonstrating its suitability for real-time deployment in smart city water systems. These results highlight that integrating physics-informed constraints with dynamic ensemble learning can substantially enhance the reliability, interpretability, and operational value of automated water quality anomaly detection for urban utilities. • Novel LSTM-PINN integrates physics constraints with deep learning for anomaly detection. • Dynamic ensemble adapts to concept drift via Jensen–Shannon divergence-based switching. • Genetic algorithm optimizes ensemble, achieving 95% F1-score and 59% improvement. • Optimal physics loss coefficient ( λ = 0 . 35 ) balances physical and data-driven learning. • Real-time processing (2.3 s per window) enables practical smart city water monitoring.
Wang et al. (Thu,) studied this question.