Forest Fire Prediction Using Random Forest Regressor: A Comprehensive Machine Learning Approach

Forest fires are catastrophic events with profound environmental, economic, and social consequences. Their increasing frequency and intensity, driven by climate change, make early and accurate predictions essential for disaster management, mitigation, and response efforts. This study presents a comprehensive machine learning-based approach to predict forest fire confidence levels using the Random Forest Regressor. Leveraging satellite data from the MODIS instrument on NASA’s Terra satellite, our model incorporates various critical attributes such as brightness temperature, fire radiative power, and geographical coordinates. Extensive experimentation on data preprocessing, feature selection, and model optimization led to a highly accurate prediction model, achieving 94.5% accuracy. This paper provides a detailed examination of the methodology, including hyperparameter tuning and model evaluation. The findings emphasize the significant potential of integrating advanced machine learning algorithms with real-time satellite data to enhance fire management strategies, providing valuable insights for policymakers, environmentalists, and disaster management authorities. By offering timely predictions, our model can facilitate proactive forest fire prevention and reduce the severe impacts of wildfires on biodiversity, air quality, and human livelihoods.