Weather Forecasts-Based Machine Learning Models to Predict Wildfire Characteristics

Wildfires are increasingly severe global challenges, driven by environmental, economic, and health impacts. This study investigates the use of weather forecasts into machine learning models to predict wildfire characteristics across seven Australian regions. Various machine learning models were evaluated for their ability to predict wildfire attributes, including fire area, brightness, and radiative power. Results show that mean fire brightness predictions achieved moderate success nationally (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{R}^{2}$</tex>: 0.504), while fire area and radiative power remained challenging due to their complexity. Region-specific analysis revealed better predictive performance in New South Wales and Queensland, with lower accuracy in regions like Northern Territory and Tasmania, emphasizing the need for region-specific approaches. This study highlights the potential of integrating real-time weather data into predictive frameworks and calls for enhanced feature engineering and regional modeling to improve accuracy, and wildfire management strategies.