AI-Powered Forest Fire Detection and Prediction System Using Multi-Sensor IoT and Deep Learning
Forest fires represent one of the most ecologically destructive and economically costly natural hazards, with global wildfire activity destroying an estimated 350 to 450 million hectares of vegetation annually and contributing significantly to greenhouse gas emissions, biodiversity loss, and air quality degradation. In India, the Forest Survey of India recorded over 2.23 lakh forest fire alert points during the 2023 fire season alone, with the Eastern Ghats, Western Ghats, and Central Indian forest belts being recurrently affected. Conventional forest fire monitoring relies predominantly on satellite-based thermal anomaly detection, which suffers from coarse temporal resolution (revisit intervals of 1–12 hours depending on satellite), cloud cover interference, and an inherent detection lag that allows fires to spread substantially before alerts reach ground response teams. This paper presents an integrated AI-powered forest fire detection and prediction system that combines a ground-based wireless sensor network for early smoke and thermal anomaly detection, a deep learning-based fire risk prediction model that forecasts fire probability up to 72 hours in advance using meteorological and vegetation moisture data, and a real-time alert dissemination pipeline for forest department response coordination. The detection module employs a lightweight CNN trained on multi-spectral sensor fusion data (temperature, humidity, CO concentration, and particulate matter) deployed across a wireless mesh network, achieving fire event detection within 4 minutes of ignition with 95.8% accuracy. The prediction module uses a Random Forest-LSTM ensemble trained on 12 years of Forest Survey of India fire occurrence records combined with IMD meteorological data, achieving fire risk forecasting accuracy of 89.4% for the 72-hour prediction window. Field-calibrated simulation results based on deployment parameters from the Sathyamangalam Tiger Reserve forest range in Tamil Nadu demonstrate that the proposed system reduces fire detection-to-alert latency by 91% compared to satellite-only monitoring and provides actionable early warning that extends the intervention window for forest department fire suppression teams by an average of 6.4 hours. Keywords — Forest Fire Detection, Wildfire Prediction, Wireless Sensor Network, CNN, LSTM, Random Forest, IoT, Fire Risk Index, Remote Sensing, Environmental Monitoring