## 3 Methodology
### Landslide Susceptibility Classification Systems  
Single-zone susceptibility maps typically use landslide data to categorize areas into five levels of susceptibility: \"very low\", \"low\", \"moderate\", \"high\", and \"very high\" [20; 35]. It should be noted that classifying landslide susceptibility is a complex task, and there are no established guidelines for determining the number of classes, their characteristics, or specifications [35]. To create a classification system that is as clear as possible, we base our approach on the density of landslide occurrences, even though our sample of landslides is biased and incomplete. We believe that this classification method is suitable for identifying landslide susceptibility on a continental scale and can be easily understood by end users. Four classification types have been used. Two of the methods are popular, widely applied in this type of work, and offered by GIS software. These are natural breaks (NB) and quantiles (Q). The other two are based on diagnostic tests, such as ELSUS and generalised Youden [22]. By working in this way, it is possible to make broader comparisons and stronger conclusions, as well asto guarantee the improvements achieved by applying a given classification method. It should be noted that although natural breaks is a commonly used classification, it is often overly optimistic for a susceptibility analysis, i.e., many of the cells in the study area are classified as low susceptibility [22]. This is because the term natural breaks seeks the greatest variance between classes, and so tends to set the class boundaries at the lowest frequency LSI intervals--meaning the steepest FR value jumps (which are dominant in the medium- and high-susceptibility sections). In the ELSUS methodology, the classification of maps in mountainous areas (zone Z5) is based on a percentage of landslide-affected pixels (LSP): 50-25-10-3% [26], i.e., the percentage of pixels affected by landslides (LSP) in each section. In terms of sensitivity (Sen) or true positive rate (TPR: success rate), the following percentages are established: 97-90-75-50% (see Table 7). Note again that this methodology does not consider specificity (Spe). The generalised Youden (GY) approach sets the boundaries between susceptibility classes based on the class-specific cost ratio of false positives to false negatives. The specificity value (Spe) or TNR (true negative rate) is used to avoid the high costs of false negatives in high susceptibility areas. For a better explanation of \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Factor** & **wELSUSv1** & **wELSUSv2** & **wLSM** \\ \hline Slope & 0.58 & 0.58 & 0.70 \\ Lithology & 0.28 & 0.29 & 0.19 \\ Land use & 0.14 & 0.13 & 0.11 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Weight (“w”) assigned to factors according to ELSUS v1 and v2 and the approach adopted in this article (wLSM). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **ELSUS** & **GYouden (1)** \\ \hline Very High (VH) & 0.50 \(\times\) Sen & max [Sen + 4 \(\times\) Spe] (2) \\ High (H) & 0.75 \(\times\) Sen & max [Sen + Spe] (3) \\ Moderate (M) & 0.90 \(\times\) Sen & max [Sen + 3/8 \(\times\) Spe] \\ Low (L) & 0.97 \(\times\) Sen & max [Sen + 1/8 \(\times\) Spe] \\ Very Low (VL) & 1 & min [LSI] \\ \hline \hline \end{tabular} (1) LSI value that maximises the function. (2) The highest value is obviously max [LSI]. (3) Equal to Youden index, the point where Sen and Spe are at their highest.  
\end{table} 
Table 7: Inferior class limits for ELSUS and GYouden. article by [PERSON] et al. (2019) [PERSON] et al. (2019) in which the development and application of the method is described in detail. Table 6 depicts, in the column wLSM, the weights assigned to each factor according to the AHP method [PERSON] et al. (2019). A consistency ratio of less than 0.10 in all cases in the comparison matrices (CR) indicates that the AHP analysis can proceed. CR represents the likelihood of the decision matrix being generated randomly.