Explainable AI-Based Anomaly Detection for Municipal Flood Pump Maintenance: Transfer Learning from Industrial Systems

The increasing frequency of urban flooding events has underscored the critical need for intelligent, predictive maintenance systems in municipal flood pump infrastructures. This review explores the integration of Explainable Artificial Intelligence (XAI) within anomaly detection frameworks to enhance the transparency, reliability, and operational resilience of flood pump systems. By leveraging transfer learning from established industrial predictive maintenance models—such as those used in manufacturing and power generation—municipal systems can achieve faster model adaptation and reduced data requirements. The paper critically analyzes state-of-the-art methods including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based architectures, emphasizing their interpretability through SHAP, LIME, and attention-based visualization techniques. It further discusses data-driven approaches for handling sensor noise, multivariate time series, and equipment degradation trends in hydraulic systems. The review identifies key challenges in model generalization, domain adaptation, and explainability fidelity, while proposing a framework for integrating real-time monitoring, XAI dashboards, and edge-AI deployment in flood pump stations. Ultimately, this study highlights how explainable, transferable AI systems can bridge the gap between industrial reliability models and municipal flood management, supporting sustainable urban resilience and proactive infrastructure maintenance.