## 1 Introduction  
The Italian territory is hydrogeologically rather fragile, due to its geological, geomorphological and hydrographic characteristics, which make it predisposed to landslide and flood risks. Italy is among the European countries most exposed to the danger of landslides, with 620,808 landslides registered, affecting 23,700 km\({}^{2}\) of territory, or 7.9% of the national territory [1]. In this context, countermeasures are needed that can contribute to the reduction in the landslide risk, through a correct assessment of even those areas that have not shown hydrogeological instability in recent times, but may still be affected by it. In particular, landslide susceptibility measures the probability that a landslide may occur in a given area, without taking into account the time factor that is decisive for the evaluation of landslide hazard [2]. Over the years, the scientific literature, through successive research, has isolated three types of processes common to each susceptibility map, firstly the creation of a model, secondly the choice of factors, and finally the validation of the method used in order to understand its performance [3]. The spatial processes for creating the map and obtaining the results for each factor are usually achieved through GIS-type software [4]. With regard to the model used or the creation of the model itself, many possibilities have been explored by researchers around the world, but these can be grouped into two categories: statistical methods and deterministic methods [5]. Deterministic methods are based on precise mathematical laws, they are the simplest, the variables assume fixed values, these models can take the effect of chance into account to a certain extent; however, they are always deterministic in that the uncertainty associated with the input variables is not taken into account [6]. Deterministic quantitative methods depend on engineering principles of slope instability expressed in terms of the factor of safety [7]; due to the need for exhaustive in situ data from individual slopes, these methods are often effective for mapping only small areas [8]. Among deterministic methods, one method used with some success in southern Italy, in Calabria, has been Transient Rainfall Infiltration and Grid-based Slope-Stability (TRIGRS), in which physical-mechanical factors of soils (unit weight, cohesion, friction angle, soil depth, etc.) are introduced, very often on the basis of existing literature in the area [9]. Another interesting model but one that does not give particularly robust results is the shallow landslide stability model (SHALSTAB), which is a deterministic model used to produce shallow landslide susceptibility maps; the SHALSTAB model is based on two assumptions, on the one hand the stability of an infinite slope with a mobilizing cover on a stable basement, the output of the model is the critical heights of the shallow landslide that generate instability [10]. Sometimes, in order to improve the reliability of these deterministic models based on physical-mechanical factors, statistical methods are also used to give a probabilistic value to the analysis, learning from the experience of previous landslides. This is the case, for example, of a research conducted in Korea, which combined the deterministic SHALSTAB model with the spatial multi-criteria evaluation model (SMCE), which is a semi-quantitative model and produced rather high success rates of close to 80% [11]. Statistical methods operate very differently from deterministic methods, as they aim to correlate information on past landslide phenomena with a set of factors that are assumed to be directly or indirectly responsible for the propensity to trigger landslides and their consequences [12]. These methods are based on the relationships existing between landslide phenomena and predisposing factors of instability, through the attribution of weights that reflect the influence that each of these have on slope stability/instability [13; 14].