Journal Article

Multi-Model Machine Learning Mapping of Gully Erosion Susceptibility in the Heihe Region of the Xiaoxingán Mountains, China

Liu, Junshuai, Wang, Dake, Guo, Xiaoyu, Zheng, Jilin, Chen, Bowei, Cai, Yanlong, Wan, Fanle

2026

Overview

What are the main findings? * In Northeast China’s Heihe Mollisol (black soil) belt, anthropogenic factors—land use and the Human Footprint Index—outweighed topography in driving gully erosion, with the highest susceptibility concentrated in the southwestern cultivated lowlands. * Tree-based models (XGBoost best, AUC 0.95) outperformed logistic regression, but spatial cross-validation across districts of the Heihe region exposed a 0.11 AUC optimism from random splitting that conventional studies overlook. In Northeast China’s Heihe Mollisol (black soil) belt, anthropogenic factors—land use and the Human Footprint Index—outweighed topography in driving gully erosion, with the highest susceptibility concentrated in the southwestern cultivated lowlands. Tree-based models (XGBoost best, AUC 0.95) outperformed logistic regression, but spatial cross-validation across districts of the Heihe region exposed a 0.11 AUC optimism from random splitting that conventional studies overlook. What are the implications of the main findings? * The Heihe case shows that gully susceptibility mapping in Mollisol farmlands requires spatially explicit validation and interpretable ML to avoid overstated accuracy and to credibly attribute risk to human disturbance. * For black-soil regions like Heihe, a 20% NDVI increase could cut high-susceptibility area by 12%, identifying targeted revegetation in the southwestern lowlands (e.g., Beian, Nenjiang) as a practical complement to engineering controls. The Heihe case shows that gully susceptibility mapping in Mollisol farmlands requires spatially explicit validation and interpretable ML to avoid overstated accuracy and to credibly attribute risk to human disturbance. For black-soil regions like Heihe, a 20% NDVI increase could cut high-susceptibility area by 12%, identifying targeted revegetation in the southwestern lowlands (e.g., Beian, Nenjiang) as a practical complement to engineering controls. Gully erosion is a major driver of irreversible soil loss in Northeast China’s Mollisol belt, a region that supplies roughly one-quarter of the national grain output. Existing susceptibility assessments in this region have rarely combined multi-model comparison with spatially explicit cross-validation, and the predictive contribution of composite anthropogenic indicators such as the Human Footprint Index (HFI) has not been quantitatively benchmarked against conventional topographic variables. This study addresses these gaps for the Heihe region by combining an inventory of 4020 gully polygons supported by field checks in Xunke County, 16 VIF-screened environmental factors, three tree-based ensemble models and a logistic regression baseline. Under stratified random splitting, XGBoost achieved the highest discrimination (AUC = 0.95, κ = 0.74); under leave-one-district-out spatial cross-validation all tree-based models retained AUC above 0.83, confirming that random-split metrics overestimate discrimination by approximately 0.11 AUC units due to spatial autocorrelation and inter-district covariate shift. SHAP analysis identified LULC and HFI as the dominant predictors, exceeding all topographic variables, while slope gradient contributed least—consistent with the low-relief, intensively cultivated character of the study area. Susceptibility was highest in the southwestern agricultural lowlands. A one-factor sensitivity test in which only NDVI was increased by 20% suggested a reduction in modelled high-susceptibility area of approximately 12%, although co-occurring land-cover and hydrological changes were not simulated. The multi-model framework, integrating spatial cross-validation and post hoc interpretability, provides an explicit estimate of conventional evaluation optimism and supports spatially differentiated erosion management.

• Publisher: MDPI AG
• Subject: Accuracy / Agricultural land / Algorithms / Anthropogenic factors / Environmental aspects / Environmental factors / Environmental monitoring / Erosion / Erosion control / Footprints / Gullies / Gully erosion / Heihe region / Human impact / Land cover / Land use / Lowlands / Machine learning / Mapping / Methods / Morphology / Mountains / Regression analysis / Remote sensing / Revegetation / Sensitivity analysis / SHAP / Slope gradients / Soil erosion / Splitting / Susceptibility / susceptibility mapping / Vegetation / XGBoost