IOT - Enabled Fire Detection And Risk Prediction Using ML

Abstract

Fire incidents in residential, commercial, and industrial environments can lead to severe loss of life, property damage, and environmental harm if not detected at an early stage. Conventional fire alarm systems primarily respond only after fire or smoke reaches a predefined threshold, often resulting in delayed emergency response. To overcome these limitations, this study proposes an IoT-Enabled Fire Detection and Risk Prediction System Using Machine Learning, which integrates real-time sensor monitoring with intelligent predictive analytics. The proposed system employs Internet of Things (IoT) devices equipped with temperature, smoke, gas, flame, and humidity sensors to continuously collect environmental data. The sensed information is transmitted to a cloud platform for storage, monitoring, and analysis. Machine learning algorithms are utilized to analyze historical and real-time sensor readings to predict the probability of fire occurrence before it becomes critical. By identifying abnormal environmental patterns and classifying risk levels, the system generates early warnings and sends instant alerts to users and emergency authorities through mobile or web-based applications. This proactive approach enables timely preventive actions, minimizes false alarms, and enhances overall safety. Furthermore, cloud-based data storage supports continuous learning and improves prediction accuracy over time. The proposed framework offers a scalable, cost-effective, and reliable solution for smart buildings, industrial facilities, warehouses, and public infrastructures. Experimental evaluation demonstrates that the integration of IoT and machine learning significantly improves fire detection accuracy, reduces response time, and enhances risk prediction compared to traditional fire alarm systems. This intelligent fire safety solution contributes to the development of smart cities by providing continuous monitoring, predictive decision-making, and efficient emergency management.