(1) Background: The aim of this paper was to study landslide susceptibility mapping based on interpretable machine learning from the perspective of topography differentiation. (2) Methods: This paper selects three counties (Chengkou, Wushan and Wuxi counties) in northeastern Chongqing, delineated as the corrosion layered high and middle mountain region (Zone I), and three counties (Wulong, Pengshui and Shizhu counties) in southeastern Chongqing, delineated as the middle mountainous region of strong karst gorges (Zone II), as the study area. This study used a Bayesian optimization algorithm to optimize the parameters of the LightGBM and XGBoost models and construct evaluation models for each of the two regions. The model with high accuracy was selected according to the accuracy of the evaluation indicators in order to establish the landslide susceptibility mapping. The SHAP algorithm was then used to explore the landslide formation mechanisms of different landforms from both a global and local perspective. (3) Results: The AUC values for the test set in the LightGBM mode for Zones I and II are 0.8525 and 0.8859, respectively, and those for the test set in the XGBoost model are 0.8214 and 0.8375, respectively. This shows that LightGBM has a high prediction accuracy with regard to both landforms. Under the two different landform types, the elevation, land use, incision depth, distance from road and the average annual rainfall were the common dominant factors contributing most to decision making at both sites; the distance from a fault and the distance from the river have different degrees of influence under different landform types. (4) Conclusions: the optimized LightGBM-SHAP model is suitable for the analysis of landslide susceptibility in two types of landscapes, namely the corrosion layered high and middle mountain region, and the middle mountainous region of strong karst gorges, and can be used to explore the internal decision-making mechanism of the model at both the global and local levels, which makes the landslide susceptibility prediction results more realistic and transparent. This is beneficial to the selection of a landslide susceptibility index system and the early prevention and control of landslide hazards, and can provide a reference for the prediction of potential landslide hazard-prone areas and interpretable machine learning research.

The accuracy of the evaluation model of rockfall susceptibility lies on reasonable conditioning factors and algorithm hyperparameters optimization. A rockfall geological database was created with 220 historical rockfalls and 220 non-rockfall cells, which was randomly divided into two datasets for model training (70%) and model testing (30%). 23 factors were selected to establish the rockfall conditioning factor database. The rockfall conditioning factors are selected by recursive feature elimination and combined with the hyperparameter optimization of grid search of machine learning-extreme gradient boosting. Thereafter, this work develops a coupling optimization model for rockfall susceptibility mapping. The results show that 9 main factors are selected by recursive feature elimination from the 23 conditioning factors, and the top ranking five factors are elevation, distance from houses, perennial average precipitation, distance from rivers, hydrogeology. After coupling optimization of factors and hyperparameters, the accuracy, precision and AUC value of the RF model are 0.7769, 0.7432, and 0.8246, respectively. Compared with the pre-optimized XGBoost model, the accuracy, precision and AUC value are improved by 0.0846, 0.0809 and 0.0616 respectively. The XGBoost model based on factor screening and hyperparameter optimization has good mapping performance.