Machine Learning Models for the Spatial Prediction of Gully Erosion Susceptibility in the Piraí Drainage Basin, Paraíba Do Sul Middle Valley, Southeast Brazil

Jorge da Paixão Marques Filho (), Antônio José Teixeira Guerra, Carla Bernadete Madureira Cruz, Maria do Carmo Oliveira Jorge and Colin A. Booth ()
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Jorge da Paixão Marques Filho: Departament of Geography, Federal University of Rio de Janeiro, Rio de Janeiro 21910-240, Brazil
Antônio José Teixeira Guerra: Departament of Geography, Federal University of Rio de Janeiro, Rio de Janeiro 21910-240, Brazil
Carla Bernadete Madureira Cruz: Departament of Geography, Federal University of Rio de Janeiro, Rio de Janeiro 21910-240, Brazil
Maria do Carmo Oliveira Jorge: Departament of Geography, Federal University of Rio de Janeiro, Rio de Janeiro 21910-240, Brazil
Colin A. Booth: School of Engineering, University of the West of England, Bristol BS16 1QY, UK

Land, 2024, vol. 13, issue 10, 1-21

Abstract: Soil erosion is a global issue—with gully erosion recognized as one of the most important forms of land degradation. The purpose of this study is to compare and contrast the outcomes of four machine learning models, Classification and Regression (CART), eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Machine (SVM), used for mapping susceptibility to soil gully erosion. The controlling factors of gully erosion in the Piraí Drainage Basin, Paraíba do Sul Middle Valley were analysed by image interpretation in Google Earth and gully erosion samples (n = 159) were used for modelling and spatial prediction. The XGBoost and RF models achieved identical results for the area under the receiver operating characteristic curve (AUROC = 88.50%), followed by the SVM and CART models, respectively (AUROC = 86.17%; AUROC = 85.11%). In all models analysed, the importance of the main controlling factors predominated among Lineaments, Land Use and Cover, Slope, Elevation and Rainfall, highlighting the need to understand the landscape. The XGBoost model, considering a smaller number of false negatives in spatial prediction, was considered the most appropriate, compared to the Random Forest model. It is noteworthy that the XGBoost model made it possible to validate the hypothesis of the study area, for susceptibility to gully erosion and identifying that 9.47% of the Piraí Drainage Basin is susceptible to gully erosion. Furthermore, replicable methodologies are evidenced by their rapid applicability at different scales.

Keywords: gully erosion susceptibility; land degradation; machine learning; spatial modelling (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:13:y:2024:i:10:p:1665-:d:1497671

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