This study aims to improve landslide susceptibility analysis in Nyingchi City by addressing the challenge of limited landslide sample data. A total of 11 influencing factors-including elevation, slope, aspect, terrain roughness, terrain moisture index, profile curvature, and plane curvature-were initially considered. After correlation and importance analysis, eight key factors were selected for modeling. To augment the limited dataset, the Markov Chain Monte Carlo (MCMC) method was employed to synthetically generate additional landslide sample points. The quality of the generated samples was validated using a Support Vector Machine (SVM) classifier. Further sensitivity analysis and susceptibility modeling were conducted using both the original and augmented datasets. The Light Gradient Boosting Machine (LightGBM) model was selected based on performance evaluation, and its predictive accuracy was assessed using the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve. The results show that: (1) The SVM achieved 97.3% classification accuracy with the MCMC-augmented data, indicating the effectiveness of the generation method; (2) The LightGBM model trained on augmented data yielded a higher AUC value than that trained on original data; (3) The most influential factors for landslide susceptibility were distance to roads, aspect, and elevation; (4) Although there were minor differences in the susceptibility maps generated from the two datasets, their overall spatial patterns were similar. This study provides methodological insights for landslide risk assessment and disaster mitigation in resource-limited mountainous areas.

The increasing scarcity of urban spatial resources has led to the growing development of urban subways. The earth pressure balanced (EPB) shield machine has become one of the main tunnel boring machines in metro tunnel construction due to its economic efficiency and versatility. In this study, the spatial heterogeneity of soil properties in the shield tunnel stratum is considered, and the three‐dimensional (3D) random discrete element method (RDEM) is applied to investigate the mechanisms of tunnel face instability and ground deformation for EPB shield tunneling in spatially heterogeneous stratum at both macro and microscopic levels. Numerical triaxial tests with a flexible boundary are carried out to establish the relationship between key macro‐microscopic parameters. Based on the RDEM, a shield tunnel model with spatially heterogeneous stratum and EPB shield machine is built and validated. Then, 16 sets of shield tunnel excavation simulations are conducted, including one set with homogeneous stratum and 15 sets with spatially heterogeneous strata. To consider the influence of excavation disturbance, the EPB shield machine keeps moving and the cutterhead keeps rotating in all simulation processes. Results reveal that overlooking spatial heterogeneity in soil properties may underestimate the risks of shield tunnel face instability and ground deformation.

Quantitative assessment of historical urban waterlogging on the Qinghai–Tibet Plateau (QTP) is severely hindered by the lack of early instrumental records. To bridge this data gap during the initial rapid urbanization period (1985–2003), this study proposes an integrated retrospective framework combining Large Language Models (LLMs)-based semantic mining, spatial reconstruction, and Extreme Gradient Boosting (XGBoost)- SHapley Additive exPlanations (SHAP) modeling under a Spatial Block Cross-Validation (SBCV) strategy. Historical disaster archives were transformed into spatially explicit training samples, enabling the reconstruction of a high-resolution urban waterlogging susceptibility atlas across the QTP. The results indicate that high-susceptibility areas are predominantly concentrated within urbanized river valleys and account for approximately 45% of the total urban built-up area across the QTP. The proposed framework achieved an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.9793 under the SBCV strategy, indicating good spatial transferability within the study area. SHAP analysis revealed that geomorphic variables contributed more strongly than most climatic variables, highlighting the important role of a geomorphic confinement effect in shaping susceptibility patterns. Comparative analyses further suggest a spatial transition from basin-dominated accumulation patterns to increasingly valley-confined susceptibility distributions under stronger topographic constraints. In addition, surface albedo and land surface temperature were identified as influential predictors, likely reflecting integrated thermal-hydrological conditions associated with antecedent soil moisture and local urban thermal dynamics. This study establishes a historical risk baseline for the QTP and provides a reproducible and cost-effective framework for historical hazard assessment in other data-scarce mountainous and high-altitude regions.