## 4 Conclusions and Recommendations  
This study presents a comprehensive and data-driven framework for co-seismic landslide susceptibility mapping in the central Nepal Himalaya, triggered by the 25 April 2015 Gorkha Earthquake (Mw 7.8). By integrating 14 carefully selected landslide conditioning factors (LCFs) and a balanced dataset of 4775 landslide and non-landslide points, we evaluated the predictive performance of three models: Logistic Regression (LR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost). Among them, the ensemble models--particularly RF--exhibited superior classification ability, achieving an accuracy of 87.9% and an ROC AUC of 0.94. Comparative model performance metrics, including precision, recall, F1 score, Kappa, and MCC, consistently favored RF and XGBoost over LR. Interpretability analysis using SHAP values revealed that elevation, distance to the fault, peak ground acceleration (PGA), slope, and post-seismic rainfall were the most influential features governing the occurrence of co-seismic landslides. Notably, the inclusion of cumulative post-seismic rainfall (April-June 2015) significantly improved model sensitivity  
Figure 22: Heatmaps showing the percentages of overlap between landslide susceptibility zones and infrastructure for three models: (**a**) fault overlap and (**b**) road overlap. and overall balance, reinforcing the role of rainfall as a secondary yet critical landslide trigger that exacerbates slope instability in seismically disturbed terrains. Spatial analysis indicated that more than 50% of observed landslides occurred within 3 km of fault lines and 1 km of road networks, reflecting the intersection of geological and anthropogenic risk factors. Both RF and XGBoost successfully classified over 95% of observed landslide points and over 85% of landslide areas into the High and Very High susceptibility classes. These models also demonstrated strong spatial coherence with mapped landslides, validating their applicability in operational hazard assessment. Infrastructure exposure analysis showed that susceptibility zones derived from RF and XGBoost models closely aligned with critical infrastructure, including roads and fault lines. Moreover, advanced validation using spatial cross-validation based on Leave-One-Cluster-Out (LOCO), confirmed the robustness and spatial generalizability of the ensemble models, minimizing overfitting caused by spatial autocorrelation. Based on these findings, we recommend the operational adoption of ensemble machine learning models, especially RF and XGBoost, for landslide susceptibility mapping in tectonically active and mountainous regions like Nepal. Rainfall predictors, particularly post-seismic precipitation, should be routinely integrated into susceptibility models to account for delayed slope failures. Additionally, spatial cross-validation approaches should replace traditional random partitioning to ensure realistic performance evaluation and improve transferability to unseen areas. Future research should integrate seismic and hydrometeorological factors with high-resolution data to better simulate landslide conditions. Deep learning models, such as CNNs, attention-based networks, and physics-informed neural networks (PINNs), offer strong potential for real-time detection and prediction. PINNs, by embedding physical laws into neural training, enhance modeling even with limited data. Finally, integrating susceptibility maps with infrastructure vulnerability and exposure analysis is essential to inform evidence-based land-use planning, mitigation strategies, and early warning systems in multi-hazard environments.