IoT and AI for Efficient Forest Fire Detection, Monitoring, and Risk Prediction With Real-Time Alerts

Wildfires pose severe threats to ecosystems, wildlife, and human settlements, necessitating efficient early detection and monitoring systems. This research introduces a real-time wildfire detection and risk assessment system powered by IoT and AI. The proposed system continuously monitors environmental parameters such as temperature, humidity, and gas concentrations using IoT-based sensors for early fire detection. AI-driven models analyze both historical and real-time sensor data to identify high-risk areas, improving predictive accuracy. A cloud-integrated architecture facilitates data collection, processing, and transmission between sensor nodes, cloud servers, and a mobile alert system. Fire response teams can leverage the system’s real-time fire alerts, GPS-based tracking, and predictive analytics via a smartphone application, enhancing emergency response coordination. Extensive testing demonstrates high accuracy, efficiency, and scalability, significantly reducing detection times while improving resource allocation. Designed for cost efficiency and adaptability, this framework presents a proactive strategy for wildfire prevention, disaster mitigation, and environmental protection.