Explainable Deep Learning Models for Critical Infrastructure Monitoring

  • Authors

    • Dr. Nimal Perera Professor, University of Colombo, Sri Lanka. Author
    • Dr. Tharindu Jayasinghe Associate Professor, University of Peradeniya, Sri Lanka. Author

    DOI:

    https://doi.org/10.67228/30713498/IJADSMC-2025PIIQF7T2X

    Published 09-04-2025

  • Explainable Artificial Intelligence (XAI), Deep Learning, Critical Infrastructure Monitoring, Predictive Maintenance, Anomaly Detection, LSTM, CNN, SHAP, LIME, Smart Infrastructure, Industrial AI

    Issue

    Vol. 8 No. 2 (2025)

    Section

    Articles

    How to Cite

    [1]
    N. Perera and T. Jayasinghe, “Explainable Deep Learning Models for Critical Infrastructure Monitoring”, IJADSMC, vol. 8, no. 2, pp. 01–15, Sep. 2025, doi: 10.67228/30713498/IJADSMC-2025PIIQF7T2X.

  • Abstract

    Critical infrastructure systems such as power, transportation, water, oil and gas, manufacturing, and smart cities increasingly rely on advanced monitoring technologies to manage complex operations. Deep Learning (DL) techniques, including CNNs, RNNs, LSTMs, Autoencoders, and Transformers, have demonstrated strong capabilities in fault detection, anomaly detection, predictive maintenance, and operational optimization. However, their "black-box" nature limits adoption in safety-critical environments where transparency and trust are essential. Explainable Artificial Intelligence (XAI) addresses this challenge by providing interpretable insights into model decisions. This study presents a comprehensive survey of explainable deep learning approaches for critical infrastructure monitoring, focusing on SHAP, LIME, Attention Mechanisms, Gradient-Based Attribution, and Feature Visualization techniques. A framework integrating sensor data acquisition, deep learning-based anomaly detection, explainability generation, and decision support is proposed. Experimental findings indicate that incorporating XAI improves user trust, fault diagnosis, decision transparency, and maintenance planning while preserving predictive accuracy. The study also discusses implementation challenges and emerging research directions, including federated explainable learning, digital twins, and edge intelligence. The results highlight explainable deep learning as a key enabler for next-generation intelligent infrastructure monitoring systems.

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