## 4 Discussion  
The area analyzed by this study is rather large, a good 9344 km\({}^{2}\) for the application of a statistical method aimed at analyzing the landslide susceptibility of the territory. Moreover, there is a very high degree of inhomogeneity in terms of vegetation, geological and morphological characteristics, which makes the generalization of the areas more complicated, also in light of the many landslides surveyed, more than 14,000. In fact, very often similar research conducted with statistical methods and in some cases with the WoE are carried out on smaller territories with an analysis of far fewer landslide events [2; 52; 53; 54; 55]. The analysis of a contained area inevitably leads to better results in the detection of probable landslide areas, precisely because one is in a more homogenous territory that therefore has similar characteristics throughout its extension. In this case, a very complex problem was faced, namely that of finding a valid model for large and diverse territories, which nevertheless gave good results with an AUC of 0.75 and allows an analysis on a decidedly larger scale also on the basis of the causal factors chosen. In some cases, much smaller study areas show worse or similar validation results to those found in this research [56; 57; 58], although many cases show better performance, but always within much smaller areas, very often on a basin scale [59; 60; 61]. Obviously, it is the chosen causal factors that lead to the results of this study in addition to the statistical method used and the use of GIS software for layer representation and map creation. In general, the scientific literature agrees that lithological characteristics are crucial for the calculation of susceptibility because they profoundly influence soil stability, and different lithological units have different landslide susceptibilities, as also shown in this study [62; 63]. There are also other parameters that are routinely considered in the scientific literature on the subject, such as slope gradient, but  
Figure 8: Validation of the model with the ROC curve. also exposure, in this case there were discriminatory results for the various classes and in agreement with what is to be expected. In particular, inclinations between 16 and 25 degrees were found to be the most likely to accommodate landslide movements, as were slopes facing predominantly North. In the literature while it is easy to find correspondences with slope grades and with similar degrees of activity to the present study, good performance is not always found in the case of slope exposure, although when significant the North is the direction most prone to landslide movements [64; 65]. Among these causal factors, TWI can also be considered topographical, as it allows the quantification of topographical control over hydrological processes [40]. In the literature and in some cases, especially on much smaller areas than the one under study here, TWI shows a directly proportional relationship with landsl, although in this case only values between 4 and 8 [66]. Most studies take geological and geomorphological causal factors into account, to the exclusion of others such as vegetational and climatic ones [67]. In particular, it has been scientifically proven that extreme precipitation events increase the possibility of a landslide triggering [68; 69]. In this study, the weight of extreme precipitation is probably partly obscured by the fact that the most intense precipitation occurs in the mountainous area, which is not a prevalent location for the landslide movements studied [70]. Finally, the last two indices, one the CLC, which allows land use to be taken into account in the analysis of landslide susceptibility, while the other the NDVI relates to vegetation. The CLC is widely used in the literature and always with good results, showing as in the present study that the areas most prone to landslides are those related to agricultural soils used [24; 71].