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

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 Fo...