Machine Learning-Based Spatial Prediction of Soil Erosion Susceptibility Using Geo-Environmental Variables in Karst Landscapes of Southwest China

Binglan Yang, Yiqiu Li (), Man Li, Ou Deng, Guangbin Yang and Xinyong Lei
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Binglan Yang: Karst Institute, School of Geographic and Environments Sciences, Guizhou Normal University, Guiyang 550001, China
Yiqiu Li: Karst Institute, School of Geographic and Environments Sciences, Guizhou Normal University, Guiyang 550001, China
Man Li: Karst Institute, School of Geographic and Environments Sciences, Guizhou Normal University, Guiyang 550001, China
Ou Deng: Karst Institute, School of Geographic and Environments Sciences, Guizhou Normal University, Guiyang 550001, China
Guangbin Yang: Karst Institute, School of Geographic and Environments Sciences, Guizhou Normal University, Guiyang 550001, China
Xinyong Lei: Karst Institute, School of Geographic and Environments Sciences, Guizhou Normal University, Guiyang 550001, China

Land, 2025, vol. 14, issue 11, 1-23

Abstract: Soil erosion poses a significant threat to the sustainability of land systems in karst mountainous regions, where steep slopes, shallow soils, and intensive human activities exacerbate land degradation, undermining both the productive functions and ecological services of land resources. This study evaluated soil erosion susceptibility in the karst-dominated Qingshui River watershed, Southwest China, and identified key drivers of land degradation to support targeted land management strategies. Four machine learning models, BPANN, BRTs, RF, and SVR were trained using twelve geo-environmental variables representing lithological, topographic, pedological, hydrological, and anthropogenic factors. Variable importance analysis revealed that annual precipitation, land use type, distance to roads, slope, and aspect consistently had the greatest influence on soil erosion patterns. Model performance assessment indicated that BRTs achieved the highest predictive accuracy (RMSE = 0.161, MAE = 0.056), followed by RF, BPANN, and SVR. Spatial susceptibility maps showed that high and very high erosion risk zones were mainly concentrated in the central and southeastern areas with steep slopes and exposed carbonate rocks, while low-risk zones were located in flatter, vegetated southwestern regions. These results confirm that hydrological conditions, topography, and anthropogenic activities are the primary drivers of soil erosion in karst landscapes. Importantly, the findings provide actionable insights for land and landscape management—such as optimizing land use, restoring vegetation on steep slopes, and regulating human activities in sensitive areas—to mitigate erosion, preserve land quality, and enhance the sustainability of karst land systems.

Keywords: soil erosion susceptibility; karst landscapes; machine learning; spatial prediction; Southwest China (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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