Landslide Susceptibility Assessment Using a Machine Learning Model with VAE-CDM Sample Augmentation: A Case Study of Southeastern Xizang

Landslide susceptibility mapping in alpine regions with limited data has two persistent obstacles: too few recorded landslide cases and the questionable reliability of non-landslide samples. We address these issues with a hybrid generative framework—a Variational Autoencoder (VAE) performs latent-space interpolation to produce additional positive samples, while a Conditional Diffusion Model (CDM) generates counterfactual negative samples. To validate generation quality, we used Principal Component Analysis (PCA) scatterplots, convex hull containment analysis, and covariance structure checks. The generated samples were then evaluated across five classifiers (Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), and The Convolutional Neural Network-Transformer-Long Short-Term Memory-Graph Convolutional Network (CTLGNet)). VAE-CDM consistently outperformed the original dataset, Synthetic Minority Over-sampling Technique (SMOTE), and Conditional Tabular Generative Adversarial Network (CTGAN), with XGBoost achieving the best results—Area Under the Curve (AUC) of 0.9337, recall of 0.91, and F1 of 0.87. DeLong’s test confirmed the AUC gain was statistically significant (p = 0.043). After augmentation, the very-high-susceptibility capture rate rose from 68.5% to 89.4%, while cumulative capture curves reflected a 4.9% improvement in spatial targeting efficiency. Shapley Additive Explanations (SHAP) analysis highlighted distance to roads, elevation, and the Freeze-Thaw Index (FTI) as the dominant controlling factors. Application to Southeastern Xizang shows the framework offers a viable pathway for susceptibility mapping in data-limited environments, although the generated samples show moderate diversity reduction (0.53) due to the interpolation-based generation strategy.