MERCATOR

The advancement of predictive models by Machine Learning Algorithms (ML) associated with environmental data enables the improvement of models of environmental fragility, which are essential tools for decision-making.This study aimed to derive a prediction of environmental fragility by testing ML associated with environmental covariates in the state of Minas Gerais.We use physical-environmental variables (soil, geology, climate, relief) with a weight of fragility for the attributes and calculation of the average to obtain a model of Potential Environmental Fragility (PEF).Subsequently, we extracted the PEF values to a 4,800-point grid, which was used to generate a new prediction by ML called PEFML.This prediction was based on testing five algorithms and a set of 105 environmental covariates.The results indicated that the best-performing PEFML prediction was the Random Forest model (R2 0.59 and RMSE 0.47), indicating a predominance of the low environmental fragility level.The PEF and PEFML models have strong correlations (0.7 Pearson); however, PEFML has stronger correlations with other environmental data.Therefore, the PEFML prediction is a robust model that captures information from covariates and has coherent spatial patterns.