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

Author(s)	Mr. Abhishek Badola, Mr. Jitendra Kumar Badola
Country	India
Abstract	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.
Keywords	Forest Fire, Early Detection, ML model, Fire, Alert, Temperature, Humidity, Pressure, Soil Moisture, MQ-5, FFMC, DMC, DC, ISI, BUI, FWI, CatBoost, Random Forest, Decision Tree, LightGBM, Uttarakhand
Field	Computer
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-05-26
DOI	https://doi.org/10.36948/ijfmr.2025.v07i03.46085

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