Machine learning models for the spatial prediction of gully erosion susceptibility in the Piraí Drainage Basin, Paraíba Do Sul Middle Valley, Southeast Brazil

Filho, Jorge da Paixão Marques; Guerra, Antônio José Teixeira; Cruz, Carla Bernadete Madureira; Jorge, Maria do Carmo Oliveira; Booth, Colin A.

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.

• Journal Article Type: Article
• Acceptance Date: Oct 9, 2024
• Online Publication Date: Oct 13, 2024
• Publication Date: 2024-10
• Deposit Date: Oct 16, 2024
• Publicly Available Date: Oct 17, 2024
• Journal: Land
• Electronic ISSN: 2073-445X
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 13
• Issue: 10
• Article Number: 1665
• DOI: https://doi.org/10.3390/land13101665
• Public URL: https://uwe-repository.worktribe.com/output/13288738