Forest Fire Prevention

Forest fire prediction involves using various methods to assess the likelihood and severity of fires in forests. Factors like weather conditions, high temperatures, and human activities contribute to these fires. Approaches such as statistical analysis, machine learning, and remote sensing gather data on weather, humidity, and terrain to predict fire risk. These models provide early warnings to alert authorities and residents about potential fire hazards and identify high-risk areas. This enables preventive measures like fire bans or evacuation orders to reduce fire impact. Forest fire prediction helps significantly in preventing and minimizing fire damage by offering timely information for proactive measures. Forest fire prediction system using meteorological parameters like location, temperature, employing the Random Forest regression algorithm, which helps to enhance prediction accuracy and contribute to reducing future fire impacts.

Keywords:  Decision Tree Regresor , Ann-Gbm , Extra Tree Regressor, Machine learning techniques.