Gully erosion, as a typical form of hydraulic erosion, is a major driver of soil degradation on sloping farmland and poses a serious threat to agricultural production and food security. Accurate and automated identification of gully erosion susceptibility (GES) therefore remains an urgent challenge. In regions with a soil-rock dual structure, soils are thin, heterogeneous, and weakly developed, and gully erosion is highly prevalent. Thus, it is essential to incorporate underlying-surface factors such as rock fragment content and soil thickness into susceptibility assessment. With the Yimeng Mountain area of northern China as a case study, five modelling approaches were developed including machine learning (Random Forest, RF and eXtreme Gradient Boosting, XGBoost), multivariate regression (Logistic Regression, LR), and deep learning (Transformer and Convolutional Neural Network, CNN). SHapley Additive exPlanations (SHAP) was applied for model interpretability. The results showed that: (1) The RF model achieved the highest prediction accuracy (ACC = 0.9534, AUC = 0.9809), outperforming LR, XGBoost, CNN, and Transformer. (2) In the susceptibility map produced by integrating RF, XGBoost, and LR, high and very high susceptibility zones account for 47 % of the study area. (3) 15 influencing factors contribute to gully erosion, among which topographic wetness index (TWI), slope, and distance to rivers are the most significant drivers. Areas with high wetness (TWI > 8–10), gentle slopes (10–15°), and proximity to rivers (<2000 m) exhibit a high likelihood of gully initiation and development. Under deep soil conditions, the combination of high TWI and short distances to rivers strongly promotes gully erosion, whereas higher rock fragment content on steep slopes helps to inhibit it. By automatically generating gully erosion susceptibility maps (GESMs), this study effectively identifies high-risk zones for gully initiation and expansion, providing robust scientific support for gully erosion control, farmland protection, land-use management, and sustainable agricultural development.
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