Enhancing Earthquake-Induced Landslide Susceptibility Mapping Through Integration of Climatological Soil Moisture: A Hybrid CNN–Swin Transformer Approach

Assessing earthquake-induced landslide (EQIL) susceptibility is essential for hazard mitigation in mountainous regions. While background hydrological variations influence slope stability, long-term mean soil moisture is rarely incorporated into deep learning-based landslide susceptibility mapping (LSM). This study proposes a hybrid Convolutional Neural Network and Swin Transformer (CNN-SwinT) framework that integrates long-term mean soil moisture as a static covariate to represent persistent background moisture conditions. The model couples the local spatial feature extraction of CNNs with the hierarchical contextual representation of Swin Transformers to capture multi-scale spatial dependencies. Using Minxian County of China as the study area, thirteen conditioning factors were selected via multicollinearity and information gain ratio analyses. The dataset was split into training (70%) and validation (30%) sets. Performance comparison against standalone CNN and SwinT models revealed that the hybrid CNN-SwinT achieved the highest accuracy (0.856) and AUC (0.95), with predicted high-susceptibility zones closely aligning with historical inventories. However, these reported metrics reflect a random, spatially non-independent split, and spatial block cross-validation is recommended for future operational deployment. The results demonstrate that incorporating long-term mean soil moisture provides critical complementary hydrological information that enhances predictive performance. These findings indicate that the proposed hybrid framework is reliable and effective for high-resolution EQIL susceptibility mapping.