## 1 Introduction
On the other hand, indirect mapping is based on quantitative models, such as statistical and deterministic ones, which aim to find numerical correlations between the various factors that favor the occurrence of landslides and their distribution on the earth's surface and based on information obtained from the interrelation between landslide conditioning factors and the landslide distribution. For large areas, statistical methods are the most widely used [17]. The statistical methods include bivariate statistical analysis [18], logistic regression [19; 20; 21], multivariate regression [22; 23]; multivariate adaptive regression spline [24], statistical index [25; 26], analytical hierarchy process analytical hierarchy process [27; 28], weight of evidence [29; 30], and evidential belief function [31; 32]. In recent times, quantitative approaches have been widely used for landslide susceptibility and hazard evaluation. However, it is mostly very difficult to include temporal probability in this analysis of large areas, due to the heterogeneity of subsoil conditions, the numerous conditioning factors, and the absence of a complete historical record of the events that can determine the occurrence of landslides (for example precipitation, earthquakes, and landslides themselves). Furthermore, these models, however, show a high sensitivity of the results to the quality and accuracy of the thematic data and the specificity of some sensitive factors that could end up not being taken into consideration. Landslide susceptibility and hazard assessments often use multi-criteria decision analysis (MCDA) techniques since in most cases, the types and format of data that are available are qualitative and quantitative, thereby requiring a semi-quantitative method that incorporates both types of data [33]. The semi-quantitative landslide assessment methods can be considered an effective expert's tool for weighting and ranking the chosen factors, which represent the main causes for landslide susceptibility of the study area, in an objectively optimal and simple way. Some qualitative methods become semi-quantitative by incorporating ranking and weighting [34; 35], as is the case of the analytic hierarchy process (AHP), a multi-objective and multi-criteria decision-making methodology which has been widely applied for the solution of decision problems [36]. This method is based on the analytical hierarchy of involved factors and the comparison between the various pairs of them in order to enable the assignment of a relevant ratio for each factor. Thanks to this method, it is therefore possible to estimate the weight of each factor considered, through the linear correlation of each factor with respect to the others. The ability to correlate different factors has made this method a valuable tool for many researchers in compiling landslide susceptibility maps, which are obtained by correlating and comparing a large number of factors [37]. The management of a large number of factors to be correlated and estimated for the definition of landslide susceptibility is therefore carried out, in most cases, through the use of the geographical information system (GIS) [35]. The increasing popularity of GIS and its ease of use over the last decades has led many studies to use indirect susceptibility mapping approaches. GIS, which uses data-integration techniques, is a very suitable tool for landslide susceptibility mapping. In fact, with the increasing availability of high-resolution spatial data sets, GIS, remote sensing, and computers with large and fast processing capacity, it has becoming possible to partially automate the evaluation of landslide hazard and susceptibility mapping process and thus minimize fieldwork. The reliability of these maps depends mostly on the applied methodology as well as on the available data used for the hazard risk estimation. Moreover, GIS is an excellent and useful tool for mapping the susceptibility of an area prone to landslide manifestation.