Use of Fire Radiative Power as an Indicator for Forest Fire Prediction: A Study of Machine Learning Architectures

This project addresses contemporary climate, sociological, and environmental challenges by exploring the use of machine learning models to analyze fire radiative power (FRP) as an indicator for forest fire prediction. Climate change has intensified extreme weather events and biodiversity loss, demanding more accurate predictive tools. Machine learning enables the extraction of hidden patterns from large-scale environmental data, supporting more reliable forecasting and mitigation strategies. Using satellite data provided by the Brazilian National Institute for Space Research (INPE), this study evaluates fire occurrences across critical biomes, including the Amazon, Cerrado, and Pantanal. The dataset includes geospatial coordinates, time of detection, and complementary meteorological variables. By identifying patterns in wildfire behavior, this work aims to support the development of more effective prevention policies and environmental risk management approaches. Despite time constraints and the large volume of available data, the project highlights the potential of machine learning architectures to enhance climate resilience and guide data-driven decision-making in Brazil’s environmental context.