Multi-sensor Deep Learning Approach for Landslide Susceptibility Mapping under Climate and Anthropogenic Pressures

The Karakoram Highway (KKH) is a critical high-altitude transportation corridor connecting and is frequently affected by landslides due to steep terrain, complex geology, intense rainfall, and increasing anthropogenic activities. Reliable landslide susceptibility mapping is therefore essential for hazard mitigation and infrastructure resilience along this corridor. In this study, landslide susceptibility was assessed along a section of the KKH using two ensemble Machine Learning (ML) models: Random Forest (RF) and Extreme Gradient Boosting (XGB). A landslide inventory comprising 447 events was classified into fall, flow, and slide types and combined with nonlandslide samples for supervised modeling. Fourteen conditioning factors derived from remote sensing, hydrological indices, and geological data were used. Multicollinearity was addressed using Variance Inflation Factor analysis. Models were trained and validated using a 70/30 train-test split, and performance was evaluated using accuracy, precision, recall, F1-score, and AUC-ROC. The models showed excellent predictive performance for slide-type landslides (AUC = 0.99 for both RF and XGB) and strong performance for fall-type landslides (AUC = 0.92 for RF and 0.89 for XGB). Flow-type landslides exhibited moderate predictability (AUC = 0.68 for RF and 0.65 for XGB). The combined (total) models achieved very high accuracy (0.95) and AUC values of 0.99 (RF) and 0.98 (XGB). XGB classified a larger proportion of the study area into very high susceptibility (281.99 km2) compared to RF (71.23 km2), while RF distributed more area into low-to-moderate susceptibility classes. Both models produced geomorphologically consistent susceptibility patterns aligned with known landslide-prone zones along the KKH. XGB emphasized localized high-risk hotspots, whereas RF provided a more conservative spatial distribution. The results highlight the strong influence of anthropogenic activities and terrain controls on landslide occurrence and demonstrate the suitability of ensemble ML methods for robust landslide susceptibility assessment in complex mountainous environments.