## 3 Results
### Landslide Susceptibility Analysis  
Once the optimal hyperparameter values were established for the algorithm execution with the established data sets and sampling, LSM of the study area were generated. As in previous studies, pixels that do not correspond to any susceptibility level (mainly belong to water bodies) were discarded. In addition, five susceptibility levels, described in Section 2.3.7 were defined. The six maps obtained have differences based on each data set and sampling applied (Figure 5). All the maps have uniformity in the number of pixels assigned to the susceptibility levels since all the models present values very close to 20% of pixels for each level, as corresponds to the use of the quantile method for classification. Figure 4: ROC curves of ML LSM algorithms implemented. Figure 5: \(Cont.\)Table 8 shows the number of landslides assigned to each susceptibility level. In all cases the highest number of landslides accumulated at the high and very high levels: 92.8% in the set of six factors (sampling 1) and 73.5% for the same set of factors but with sampling 2; 87.2% in the set of DEM-dependent factors (sampling 1) and 59.6% for the same set with sampling 2; finally, 86.2% in the set of non-DEM-dependent factors and 68.4% for the same set but with sampling 2. Another remarkable aspect is the minimal number of landslides (less than 5% in all cases) that fall in the very low susceptibility level: 0.9% in the set of six factors with the two samplings; 1.1% for the set of DEM-dependent factors (sampling 1) and 2.3 for the same set with sampling 2; 1.7% for the set of non-DEM-dependent factors (sampling 1) and 4.7% for the same set of factors with sampling 2. The distribution of susceptibility levels for each model generated is shown in Figure 6. It is important to point out that these levels not only provide ease of interpretation but also show in a specific way the areas with greater susceptibility, which affects a better planning strategy, especially when landslides fall in the levels that are apparently more susceptible [77]. Figure 5: Landslide Susceptibility Maps (LSM) obtained with XGBoost algorithm and factors selection: (**a**) 6 Factors (sample 1), (**b**) 6 Factors (sample 2), (**c**) DEM Factors (sample 1), (**d**) DEM Factors (sample 2), (**e**) Not DEM Factors (sample 1), (**f**) Not DEM Factors (sample 2). Figure 6: Distribution of susceptibility levels per each LSM. \begin{table}
\begin{tabular}{l c c c} \hline \hline  & \multicolumn{3}{c}{**6 Factors (Sampling 1)**} \\ \hline
**Susceptibility** & **Pixel Amount** & **Pixels (\%)** & **Landslides (\%)** \\ \hline Very low & 8,494,988 & 19.98 & 0.9 \\ Low & 8,553,872 & 20.12 & 0.8 \\ Medium & 8,522,070 & 20.04 & 5.5 \\ High & 8,466,887 & 19.91 & 15.3 \\ Very high & 8,485,999 & 19.96 & 77.5 \\ No data & 19,736,409 & - & - \\ \hline \hline \multicolumn{3}{c}{**6 Factors (Sampling 2)**} \\ \hline
**Susceptibility** & **Pixel Amount** & **Pixels (\%)** & **Landslides (\%)** \\ \hline Very low & 8,467,426 & 19.91 & 0.9 \\ Low & 8,566,863 & 20.15 & 7.2 \\ Medium & 8,556,299 & 20.12 & 18.3 \\ High & 8,414,341 & 19.79 & 30.2 \\ Very high & 8,518,887 & 20.03 & 43.3 \\ No data & 19,736,409 & - & - \\ \hline \hline \multicolumn{3}{c}{**DEM Factors (Sampling 1)**} \\ \hline
**Susceptibility** & **Pixel Amount** & **Pixels (\%)** & **Landslides (\%)** \\ \hline Very low & 8,456,062 & 19.90 & 2.3 \\ Low & 8,501,778 & 20.01 & 13.4 \\ Medium & 8,510,982 & 20.03 & 24.8 \\ High & 8,580,816 & 20.19 & 26.7 \\ Very high & 8,447,410 & 19.88 & 32.9 \\ No data & 19,728,184 & - & - \\ \hline \hline \multicolumn{3}{c}{**Not DEM Factors (Sampling 1)**} \\ \hline
**Susceptibility** & **Pixel Amount** & **Pixels (\%)** & **Landslides (\%)** \\ \hline Very low & 8,581,382 & 20.23 & 1.7 \\ Low & 8,510,254 & 20.07 & 3.0 \\ Medium & 8,481,102 & 20.00 & 9.1 \\ High & 8,509,505 & 20.06 & 18.5 \\ Very high & 8,330,078 & 19.64 & 67.7 \\ No data & 19,051,719 & - & - \\ \hline \hline \end{tabular}
\end{table}