Digital Twin-enabled predictive fire detection using IOT and Machine learning

Abstract

Fire incidents in built environments continue to cause significant human and economic losses, despite widespread deployment of automated fire detection systems. Most existing systems rely on reactive threshold-based sensing, which limits early intervention capabilities. In this study, a digital twin-enabled predictive fire detection system is designed, implemented, and evaluated. The proposed system integrates real-time IoT sensor data, physics-based fire simulation, and machine learning models within a synchronized digital twin environment. A prototype digital twin is developed for a building-scale scenario, and predictive models are trained using a combination of historical fire data and simulated fire scenarios. Experimental results demonstrate that the proposed system can predict fire risk significantly earlier than conventional detection methods, providing extended lead time for preventive actions. The findings validate the feasibility and effectiveness of digital twin technology for proactive fire risk management.