IoT-Driven Smart Forest Fire Detection and Prediction System with AI-based Risk Mitigation

Forest fires are a perilous threat to wildlife, human habitation, and ecosystems, rapidly spreading with hidden ignition and latent response. The proposed project proposes a Smart Forest Fire Alert and Forecasting System on the basis of IoT sensing, cloud computing, and machine learning to counter the threat. The proposed solution employs an ESP32 microcontroller with flame, smoke, temperature, humidity, wind speed sensor, and GPS for sensing major environmental parameters. The sensed parameters are transmitted to the ThingSpeak cloud for real-time visualization and email-based alert. The intention is not only real-time sensing but also a future prediction of fire risk based on a MATLAB-trained ML model with aggregates of data. The proposed system predicts fire spread patterns on the basis of GPS coordinates and wind direction for forest fire preparedness. The novelty of the proposed system is the real-time alert facility combined with AI-based predictive analytics to proactively counter risk, an end-to-end, scalable, and smart solution to forest fire management.