ENVIRONMENTAL FRAGILITY BY MACHINE LEARNING ALGORITHMS

FRAGILIDADE AMBIENTAL USANDO ALGORITMOS DE APRENDIZADO DE MÁQUINA

FRAGILIDAD AMBIENTAL MEDIANTE ALGORITMOS DE APRENDIZAJE AUTOMÁTICO

Abstract

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.

Keywords:
Environmental Fragility Model; Spatial Prediction; Random Forest; Environmental Planning.