Susceptibility based on different landslide types has rarely been assessed. Therefore, this paper aims to compare the prediction performance of hybrid approaches by combining the logistic model tree (LMT) with Decorate, random subspace, and rotation forest ensemble techniques (De-LMT, RS-LMT, and RoF-LMT) for susceptibility assessments of different landslide types. The Yuzi River catchment along the eastern margin of the Tibetan Plateau was selected as the study area. In this catchment, 478 rockfalls and 167 landslides were identified, and further partitioned into training (70%) and validation (30%) datasets, respectively. Subsequently, 13 conditioning factors were initially considered, and then 11 and 8, respectively, were selected as the final rockfall and landslide influencing parameters to conduct susceptibility models through multicollinearity analysis and information gain. Finally, the model performances were evaluated by area under the receiver operating characteristic curves (AUC). The obtained results demonstrated that elevation and rainfall were most influential factors for rockfall and landslide. For the rockfall prediction, the De-LMT model achieved the best prediction accuracy with the highest AUC (0.939), followed by the RS-LMT (0.938), RoF-LMT (0.928), and LMT (0.919) models. For the landslide prediction, the De-LMT model (AUC = 0.931) outperformed and outclassed the RoF-LMT (0.926), RS-LMT (0.922), and LMT (0.915) models. Therefore, it is reasoned out that all models exhibited satisfactory performance (AUC > 0.9), and the De-LMT model was superior in rockfall and landslide spatial prediction.

Coseismic landslide susceptibility mapping plays a critical role in disaster risk mitigation, infrastructure protection, and territorial spatial planning, particularly in tectonically active mountain belts such as the Tianshan Mountains. Conventional machine learning models often suffer from overfitting and inadequate generalization capability due to their homogeneous structures and inflexible modeling strategies. In this study, targeting the coseismic landslides triggered by the 2024 Ms 7.1 Wushi earthquake in the Biedieli River basin, a landslide inventory comprising 904 events was compiled and randomly partitioned into training (70%) and validation (30%) datasets. Nine conditioning factors pertaining to topography, geology, environment, and seismicity were selected, including elevation, slope angle, slope aspect, curvature, lithology, distance to faults, distance to rivers, distance to roads, and peak ground acceleration (PGA). The predictive performance of an ensemble learning model, LightGBM, was compared against three benchmark machine learning models, i.e., artificial neural network (ANN), decision tree (DT), and naive Bayes (NB). Multicollinearity diagnosis and factor importance ranking were conducted using variance inflation factor (VIF) and correlation attribute evaluation (CAE), respectively. The model performance was validated and compared via the area under the receiver operating characteristic curve (AUC) and several statistical metrics including accuracy and F ₁ -score. The results indicate that elevation (AM = 0.6296), PGA (AM = 0.5810), and distance to roads (AM = 0.4934) are the primary determinants of landslide occurrence. The LightGBM model achieved the best overall predictive performance on the validation dataset, with an AUC of 0.961 and the highest accuracy (0.890) and F ₁ -score (0.895), followed by ANN (AUC = 0.957, accuracy = 0.876, F ₁ -score = 0.876), DT (AUC = 0.947, accuracy = 0.856, F ₁ -score = 0.860), and NB (AUC = 0.919, accuracy = 0.862, F ₁ -score = 0.869). From the training dataset to the validation dataset, the AUC decay of LightGBM was merely 2.6%, indicating good generalization capability. The high and very high susceptibility zones account for 10.51% to 19.43% of the study area across the four models, predominantly concentrated in the mid-elevation valley zones (2036.5–2959 m), steep slopes exceeding 40°, areas with PGA greater than 0.48 g, and the vicinity of the Maidan fault zone. The susceptibility maps produced in this study can provide a scientific basis for post-earthquake emergency response, hazard risk assessment, and disaster mitigation in the Tianshan seismic belt.

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