Published on: April 2026
AI-BASED FIRE DETECTION AND PREDICTION SYSTEMS FOR SMART BUILDINGS: A COMPREHENSIVE REVIEW
Deepak Davis Sherlin Paul P S Mago Stalany V
Aniver Chanth R
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Abstract
Fire hazards in smart buildings pose significant risks to life, infrastructure, and economic assets. Traditional fire detection systems, primarily based on smoke, heat, and flame sensors, often suffer from delayed response times and high false alarm rates. Recent advancements in Artificial Intelligence (AI), particularly machine learning (ML) and deep learning (DL), have enabled the development of intelligent fire detection and prediction systems with improved accuracy, reliability, and response speed. This review paper presents a comprehensive analysis of AI-based fire detection and prediction techniques for smart buildings, focusing on sensor-based, vision-based, and hybrid approaches. It further explores predictive modeling using AI for early fire risk assessment, integration with IoT-enabled smart building frameworks, and real-time decision-making systems. Challenges, limitations, and future research directions are also discussed, emphasizing the need for robust datasets, real-time processing capabilities, and system scalability. The study concludes that AI-driven fire safety systems have the potential to significantly enhance proactive fire management in next-generation smart infrastructure.
How to Cite this Paper
Davis, D., S, S. P. P. & V, M. S. (2026). AI-Based Fire Detection and Prediction Systems for Smart Buildings: A Comprehensive Review. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.1049
Davis, Deepak, et al.. "AI-Based Fire Detection and Prediction Systems for Smart Buildings: A Comprehensive Review." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.1049.
Davis, Deepak,Sherlin S, and Mago V. "AI-Based Fire Detection and Prediction Systems for Smart Buildings: A Comprehensive Review." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.1049.
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References
- Zhang, Y., & Chen, X. (2022). Deep learning-based fire detection in smart buildings. Fire Safety Journal, 128, 103520.
- Jain, A., & Kumar, S. (2020). Fire risk prediction using machine learning techniques. Safety Science, 130, 104877.
- Li, J., & Zhao, Y. (2021). IoT-based intelligent fire monitoring systems. IEEE Internet of Things Journal, 8(3), 1578–1587.
- Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. KDD Conference.
- Muhammad, K., Ahmad, J., & Baik, S. W. (2018). Early fire detection using convolutional neural networks. Neural Computing and Applications, 30(4), 1109–1123.
- Redmon, J., & Farhadi, A. (2018). YOLOv3: An incremental improvement.
- Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.
- Krizhevsky, A., Sutskever, I., & Hinton, G. (2012). ImageNet classification with deep convolutional neural networks. NeurIPS.
- Shorten, C., & Khoshgoftaar, T. M. (2019). A survey on image data augmentation for deep learning. Journal of Big Data, 6(1), 60.
- Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
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- •Published on: May 01 2026
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