## 3 Materials and Method
### Distance of Road and River Factor  
As it is obvious, the artificial and natural parts of the slopes around a road are more sensitive in landslide manifestation. In addition, some lifeline roads have to guarantee effectiveness and efficiency in the immediate aftermath of a natural disaster; in some cases, they have to be maintained also during the event to allow rapid and efficient access and assistance and rescue, guaranteeing evacuation and, more in general, maintaining access for all emergency services. Therefore, the road network was chosen as a principal parameter that consists of the road network of the province of Reggio Calabria, composed of 1850 km of roads. Buffer zones were created around road lifelines at distances of 100, 200, 300, 400 and 500 m.  
Similar to the road network, the hydrographic network was digitized and saved as line layers in the GIS database, using the topographic sheets as a data source, and buffer zones were created around the bed of the rivers and the streams of the area, at distances of 50, 100, 200, and 250 m. These distances of river are measured from the river's bed boundaries from both sides. Each factor was characterized in classes whose weight (**W**i,**L**) was determined on the basis of the portion of the territory occupied by the class of the factor in the entire metropolitan area of Reggio Calabria (Table 1). This method was used to identify the amount of territory that the factor class occupies to then be put into relation with the weights of the same calculated with other methods (**W**i,**PAI, **W**i,**AHP) and to be able to determine the degree of landslide susceptibility. The analysis was carried out by means of GIS devices, which allow to easily analyze a considerable amount of data and to convert the map pixels into data sets. The liability and accuracy of the collected data also influence the success of the applied methodology. For this reason, raster images were used with an accuracy of a pixel equal to 5 m. The images obtained are shown in Figure 1.  
Figure 1: _Cont._  
Figure 1: _Cont_. Figure 1: _Cont._  
Figure 1: \(Cont\). Figure 1: _Cont_. Figure 1: _Cont._  
So, the weight of each class was determined by the examination of the value attained by each factor class in the landslide areas identified by the PAI, with respect to the past landslide events (\(\mathbf{W_{i,PAI}}\)) that occurred in the territory of the metropolitan area (Table 2). This parameter allows to determine the importance of each individual class based on historical data relating to landslides that occurred in the past. Figure 1: Topographical parameter maps: (**a**) slope degree; (**b**) land use; (**c**) elevation; (**d**) rainfall; (**e**) geology; (**f**) river distance; (**g**) road distance, (**h**) PAI landslide risk areas. Later using the AHP method, we were able to obtain a relative significance of the relevant factors after the pairwise comparison matrix was constructed. The pairwise comparison method was developed by [PERSON] in the context of the analytical hierarchy process. The AHP approach allows assessing the relative weight of multiple elements in an initiative manner [39]. After defining the weight of each factor, with respect to the past landslide events identified by the PAI, its relative weight was calculated with the AHP method. The rating score of relative significance was set from 1 to 9, indicating less important to much more important factors. The pairwise comparison matrix is shown in Table 3 using a 7 \(\times\) 7 matrix, where diagonal elements are equal to 1. The row describes the importance of factor, and the values of each row are compared with each column to define the relative importance to obtain a rating score. For example, the slope is significantly more important than the elevation and therefore assigned the value 8. Conversely, the importance of the elevation with respect to the slope is equal to 1/8.