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

• Published in: Land (Basel) Vol. 13; no. 10; p. 1665
• Main Authors: Filho, Jorge da Paixão Marques, Guerra, Antônio José Teixeira, Cruz, Carla Bernadete Madureira, Jorge, Maria do Carmo Oliveira, Booth, Colin A.
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.10.2024
• Subjects: Decision making; Drainage; Drainage basins; Erosion control; Geographic information systems; Geomorphology; Gullies; Gully erosion; gully erosion susceptibility; Hydrology; Land degradation; Land use; Learning algorithms; Machine learning; Predictions; Rain and rainfall; Rainfall; Regression analysis; Soil analysis; Soil classification; Soil degradation; Soil erosion; spatial modelling; Stochastic models; Support vector machines; Brazil; United Kingdom; Taiwan
• ISSN: 2073-445X; 2073-445X
• DOI: 10.3390/land13101665

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.