## 4 Discussion  
This study investigated the use of Extreme Gradient Boosting (XGBoost) to model flood and landslide susceptibility within the Basento River basin in southern Italy. The findings showed the model's strong predictive capability, particularly in identifying flood-prone areas, through the integration of topographic, geological, and remote sensing data. Flood susceptibility mapping achieved higher accuracy than landslide susceptibility, likely due to the relatively simpler and more consistent conditioning factors involved--such as low elevation and proximity to drainage networks--compared to the more variable factors influencing landslides, such as slope stability and lithological heterogeneity. Additionally, the completeness of landslide inventories was often lower, which may further affect the model's performance. The analysis showed that flood-prone areas were primarily associated with low elevation, relative elevation, and high drainage density. In contrast, landslide-prone areas were influenced by a broader and less uniform set of variables, including elevation, drainage density, relative elevation, distance, and lithology. These results aligned with previous studies in the field [15; 17; 20], yet this study offered a novel contribution by employing a single, unified machine learning model for both hazard phenomena. The use of multi-source satellite data (e.g., Copernicus EMS, COSMO-SkyMed) further improved the robustness of the predictions. Practically, the generated susceptibility maps served as valuable tools for spatial planning, infrastructure development, and risk mitigation. They allowed decision makers to prioritize interventions in the most vulnerable areas. The key innovative aspects of this work include the following:  
* A unified multi-hazard modeling approach, using the same framework to assess both flood and landslide susceptibility, overcoming the traditional separation between these two analyses. * Integration of multi-source data, including high-resolution satellite imagery (Copernicus EMS, COSMO-SkyMed) and geospatial datasets, enhancing the reliability of predictions. * The use of SHAP analysis to interpret the model's decisions and understand the impact of different predisposing factors, ensuring transparency and interpretability. Figure 9: SHAP analysis for landslide-prone areas: (**a**) feature importance based on SHAP values; (**b**) Bee Swarm plot showing the distribution and impact of each conditioning factor. * A scalable and transferable model, which can be adapted to other geographical areas with similar hazards, supporting land-use planning and risk management. Nonetheless, several limitations emerged. The lower performance for landslides reflected their complex and heterogeneous nature. Since the current model was based solely on spatial variables, integrating temporal indicators--such as rainfall intensity, soil moisture variation, and vegetation dynamics--could substantially improve accuracy. A further limitation was the potential incompleteness and spatial bias of the landslide inventory. Unlike floods, which can be more directly mapped from satellite imagery, landslide delineation and categorization often require expert interpretation. Increasing the spatial and temporal resolution of landslide data would enhance the model's performance. Moreover, while a unified set of predictors simplifies implementation, it may overlook hazard-specific characteristics. The misclassification errors observed can be attributed, in part, to factors such as the different spatial resolution of the data and the geomorphological complexity of the studied areas. The input maps used in the model, derived from official sources, while widely accepted and reliable for risk modeling, may not have a high enough spatial resolution to capture finer, localized features of the landscape.