Application of machine learning models in the behavioral study of forest fires in the Brazilian Federal District region

Ecosystems, settlements, and human lives are put at risk by forest fires every year. Several models proposed for the prediction of their occurrence and behavior have aimed at identifying their conditioning factors, risks, and post-effects. However, their application in other regions is impracticable or very difficult, due to the distinct geographic characteristics of the areas and the unavailability of data. This research is devoted to the prediction of both spread and behavior of wildfires at a specific time and/or in specific regions for helping fire management agencies minimize the damages caused. The Brazilian Federal District, inserted in the Cerrado biome, is the focus of the analyses, due to its large number of fire occurrences and reduced number of studies conducted on the region. A dataset was compiled from Brazilian governmental open data for the prediction of the wildfire behavior and used for the training of several Machine Learning models that consider the fire point of ignition to predict the areas that will be impacted. It includes observations on climate features from 5 monitoring stations and satellite data on fires that occurred over the past two decades and was enriched with other topographic, hydrographic, and anthropogenic features, such as urbanization index, distance to rivers/roads, and Normalized Difference Vegetation Index (NDVI). According to the results, AdaBoost model predicted the area affected by the wildfire with 91% accuracy, showing better performance than Random Forest (RF) 88%, Artificial Neural Network (ANN) 86%, and Support Vector Machine (SVM) 81%.