ML Model to Tackle Forest Fire Problem using Forest Flare in Uttarakhand

This project aims to detect forest fires early in Uttarakhand using machine learning and real-time weather data. Environmental parameters like temperature, humidity, pressure, and soil moisture are collected and analyzed using five algorithms: CatBoost, Random Forest, LightGBM, XGBoost, and Decision Tree. CatBoost achieved the highest accuracy at 96.23%. The system enables reliable fire risk prediction, supporting early warnings and proactive response. It offers a practical solution to assist forest departments in preventing and managing wildfire incidents effectively.