Using Machine Learning to Propose a Qualitative Classification of Risk of Soil Erosion

Abstract

Soil loss compromises ecosystem services essential for sustainable development, necessitating effective strategies to identify priority areas for conservation practices aimed at reducing soil erosion. Current methods often rely on literature-based classification, which can be subjective. This study explores the use of artificial intelligence techniques to enhance the objectivity and efficiency of qualitative classifications for soil erosion risk. Accordingly, the aims were to apply Machine Learning methods, specifically cluster analysis, to categorize soil erosion risk in the Peixe Angical Basin, in addition to using a discriminant analysis to propose a discriminant classifier vectors for current and future predictions of soil loss risks. Our database consisted of pixel-based data on the R, K, LS, and C factors. These input data were linked to soil losses (output data), which had been classified based on findings from studies conducted in a different basin. Following this, machine learning techniques were applied to analyze the data. The cluster analysis identified seven distinct erosion risk groups: slight, slight to moderate, moderate, moderate to severe, severe, very severe, and extremely severe. Additionally, discriminant analysis facilitated the development of seven predictive models for current and future soil erosion risk, streamlining the need of new soil erosion modeling and enhancing decision-making processes. We anticipate that this methodology can be applied to other basins, providing a more robust framework for assessing soil erosion risk without relying on arbitrary qualitative classification.