## 5 Conclusions  
This paper represents a model for the analysis of landslide susceptibility over large areas, in particular, the causal factors chosen lend themselves to an interpretation that provides good results, in terms of AUC compared to the existing literature on the topic, especially in relation to the area of over 9000 km\({}^{2}\) analyzed. Topographical, land-use, climatic, and vegetation parameters were considered, with weights that discriminated in most cases those classes that are also identified as more prone to landslide movements in the literature. The database used was very large, but also reliable and allowed for a detailed analysis of landslide movements, even over such a large area as the one analyzed. The WoE method has proven to be very reliable (AUC = 0.75), as it is based on the analysis of layers (causal factors) that overlap the landslide map, and this especially with such a large database allows very good discrimination of features contributing to triggering. However, similar to any research, there is a margin for improvement that would be interesting to explore and that could further improve the already good value obtained from validation through the AUC method. First, it has been observed in the scientific literature that WoE, similar to most statistical methods, obtains the best results in the case of homogeneous environments, so it might be interesting to subdivide the territory, on elevation or lithological bands, to be analyzed separately; this would determine for large spatial extensions two or more models that would work with different but more accurate results for individual isolated areas, thus with different weights for the causal factors. Second, the resolution of NDVI could be improved and raised from 250 m to much lower values although it would be much more complicated to handle hundreds of satellite images. In addition, the consideration made to improve the WoE could also improve the significance of extreme precipitation weights, which could be differentiated according to homogeneous areas. In conclusion, it is crucial to provide institutions with increasingly reliable and high-performing susceptibility analysis tools in order to be able to choose the areas on which to intervene before disasters involving property and especially people occur. **Author Contributions:** Conceptualization, M.G. ; methodology, M.G. ; software, N.P. ; validation, M.G. and G.P. ; formal analysis, N.P. ; investigation, N.P. and A.R. ; resources, M.G. ; data curation, G.P. ; writing--original draft preparation, M.G. ; writing--review and editing, M.G. ; visualization, M.G. and A.D.; supervision, G.P. ; project administration, G.P. All authors have read and agreed to the published version of the manuscript. This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Landslides ([[https://www.autoritadistrettoac.it/pianificazione/bacin](https://www.autoritadistrettoac.it/pianificazione/bacin)]([https://www.autoritadistrettoac.it/pianificazione/bacin](https://www.autoritadistrettoac.it/pianificazione/bacin)) o-idrografico/bacini-marchigiani/cartografia-pai-marche accessed on 20 March 2023). Geological map ([[https://www.regione.marche.it/Regione-Utile/PaesaggioTerritoryLrbanPlanning/Cartogra](https://www.regione.marche.it/Regione-Utile/PaesaggioTerritoryLrbanPlanning/Cartogra)]([https://www.regione.marche.it/Regione-Utile/PaesaggioTerritoryLrbanPlanning/Cartogra](https://www.regione.marche.it/Regione-Utile/PaesaggioTerritoryLrbanPlanning/Cartogra)) phy/Repertoire/Cartotageologicaregionale10000 accessed on 20 March 2023). CLC (https://groupw are.sinanet.isprambiente.it/uso-copertura-e-consumo-di-suolo/library/copertura-del-suolo/corin e-land-cover/clc2018_shapefile accessed on 20 March 2023).