Prediction of forest fire susceptibility using machine learning tools in the Triunfo do Xingu Environmental Protection Area, Amazon, Brazil

Machine learning tools have demonstrated promising results for fire prediction, which have included the generation of models that have been developed across a large range of contexts and locations. This research aims to map areas susceptible to forest fires within the Triunfo do Xingu Environmental Protection Area, employing machine learning algorithms to ascertain the influence of environmental, topographic and socioeconomic factors on fire occurrence. For this purpose, the Random Forest and Extreme Gradient Boosting regression models were used to predict kernel density values calculated over 15,291 confirmed burn points between 2010 and 2020, using 11 predictor factors, including Altitude, Slope, Aspect, Topographic Wetness Index, Precipitation, Temperature, Proximity to Inhabited areas, Proximity to Roads, Land Use and Cover, Vegetation Continuous Fields, and the Normalized Difference Vegetation Index. To evaluate the performance of the algorithms, the metrics used were Mean Absolute Error, Root Mean Square Error, and the Coefficient of Determination. The test results showed that the models had similar performance, and both the Random Forest (RMSE = 36.26, MAE = 17.45, and R2 = 0.99) and the Extreme Gradient Boosting (RMSE = 35.73, MAE = 18.74, and R2 = 0.99) demonstrated good predictive capacity. The elaborated map presented areas of high and very high susceptibility occupying 39% of the total area of the conservation unit, mainly located in the central-east and central-west regions. The variables with the greatest importance and contribution to the final model were environmental and socioeconomic variables, notably precipitation, distance from inhabited areas, and land use type. •Machine learning models for fire prediction in protected areas in the Amazon.•Random Forest and XGBoost models map forest fire susceptibility.•Fire risk mapping using topographic, environmental and socioeconomic factors.•Higher influence of Annual Precipitation and Proximity to human activities.