Real Time Smart Forest Fire Detection and Monitoring System Using IoT and Machine Learning

According to the National Interagency Fire Center (NIFC) forest fires pose a major threat to our planet, causing severe ecological, economic, and human loss, with an estimated 7 million acres of forest burned annually in the United States alone. To overcome this, in this paper proposed a cutting-edge solution that integrates Internet of Things (IoT), remote sensing, and machine learning technology. By deploying a network of sensors throughout a certain forest area to monitor temperature, humidity, and smoke levels, we can collect real-time data on forest conditions. This data is transmitted to a centralized system with an accuracy rate of 92% in predicting potential fire outbreaks. This approach provides early detection of forest fires, allowing for rapid response and mitigation efforts. Proposed system ensures swift communication between the monitoring station, authorities, and firefighting teams, reducing response times. Rigorous testing, including over multiple simulations, has validated the system's effectiveness, positioning it as a major tool in forest fire management and potentially reducing fire damage by 30%.