## 1 Introduction
In the case of statistical methods, therefore, it is sufficient to map the disasters that have occurred in order to be able to interpret them in the future by analyzing the factors that predisposed them. There are many statistical methods to assess slope instability, such as bivariate statistical analyses, logistic regressions, multiple linear regressions, fuzzy logic and neural networks [15]. An example of a bivariate statistics model is the Statistical Index, based on the correlation between landslides and prediction layers, but also the weight of evidence (WoE), which calculates the weight of each factor in terms of the presence or absence of landslides and then returns a contrast value; both of these have been found in the literature to have good prediction performance, particularly the WoE [16; 17; 18]. Logistic regression is probably the most widely used method in the study of landslide susceptibility. It involves the use of a binary variable (of the yes/no type) as the dependent variable and one or more variables, whether continuous or categorical, as the independent variables, and yields good results, in some cases correctly classifying more than 80% of the landslides [19]. As far as multiple linear regression is concerned, it is not among the most widely used methods, and as far as success rates are concerned, it is also not among the most consistent methods because the thresholds for discriminating between prediction of the presence of a possible landslide and prediction of its absence are rather subjective [20]. In fuzzy logic, on the other hand, there are many decisions that are left to the experience of the operator, with the result that performance can be variable and dependent on the choices made [21]. Neural networks currently represent the final frontier in the assessment of landslide susceptibility, having the ability to simulate brain connections and allowing through the technique of deep learning to learn from the layers used. There is a great deal of research that shows that artificial neural networks (ANNs) can predict landslide susceptibility very well compared to testing databases, reaching accuracies of up to 90% [22]. However, the model is not the only discriminating factor in drawing up a landslide susceptibility map. In fact, the factors that are to be considered play a decisive role and are identified as predisposing factors whose weight is verified by entering them into the model. there are various types of factors that can be considered as triggering or predisposing factors for landslide movement, they can be topographic, hydrological, soil, geological, vegetation, land use or climatic factors [23; 24; 25]. While geology, topography and land use are the most commonly used variables for analyzing landslide susceptibility, others are less frequent and very often do not show clear correlations with landslide movements [26]. Some studies use the NDVI (normalized vegetation index) as the absence of vegetation can lead to less resistance to movement, others the TWI (Topographic Wetness Index), which calculates the moisture in the soil, and still others, although fewer in number, which consider extreme precipitation [27; 28]. In the case of the present research, the aim of this study was to create a rather extensive landslide susceptibility map using climatic-environmental factors to understand their actual influence on slope stability, in a rather large area corresponding to the Adriatic slope in Central Italy. In this context, given the size of the area and the lack of in situ surveys, deterministic methods were discarded and a model based on statistical methods was chosen. Within these, the weight-of-evidence method has been shown by numerous studies to be one of the best performing methods in terms of prediction in discriminating the probability of landslide events spatially [29; 30].