## 5 Discussion  
### Performance and Comparison of Selected Models for Landslide Susceptibility Assessment  
The best selected models were tested on the test dataset to evaluate their final performances (Table 10). The best model is RF, which uses predictor SET-3 and achieves the highest AUC value (92.50%) and the lowest error rate (11.00%). If the results are analyzed with respect to the combined performance criteria, this model is also the best, with a score of 82.30%. The next best models are ANN and SVMR, both using SET-3, with a combined performance of 81.30% and 80.60%, respectively. All in all, RF using SET-3 may be selected as the best model. This means that variables that control co-seismic landslides include distance to faults and rivers, slope angle, orientation, roughness, curvature, critical acceleration, Arias intensity, PGA, lithology, Newmark displacement, and landform (mountain tops, high ridges, and open slopes). Good performance of RF was also obtained by the authors in [22] in their global susceptibility assessment study, with a predictive capacity slightly higher (AUC of 98.50%) when compared to the results obtained in this study (AUC of 92.50%). It is important to note that there is an agreement between both studies that the distance to faults and slope angle are important predictive variables for this algorithm. The second-best model corresponds to the ANN model using an average of the model (avNNet). As in the case of the results obtained by the authors in [23], the most important factors for landslide susceptibility using ANN correspond to profile curvature, topographic moisture index, and slope. Other influential variables related to the distance to roads and rivers have been shown in other studies [24]. In terms of pixel classification, the results obtained using MLP by the other authors and the results of this study are similar, being slightly higher in this study (91.40% AUC), which may indicate that the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Predictor**} & \multirow{2}{*}{**Av. AUC**} & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Testing**} & \multirow{2}{*}{**Comb. per.**} \\  & & & **Av. Error** & & & **Av. AUC** & **Av. Error** & **Comb. per.** \\ \hline ann & SET\_3 & 0.952 & 0.103 & 0.854 & 0.914 & 0.110 & 0.813 \\ ann\_cp & – & 0.954 & 0.101 & 0.858 & 0.901 & 0.115 & 0.797 \\ lg & SET\_2 & 0.942 & 0.127 & 0.822 & 0.879 & 0.155 & 0.743 \\ lg\_cp & – & 0.919 & 0.155 & 0.777 & 0.848 & 0.160 & 0.712 \\ rf & SET\_3 & 0.953 & 0.109 & 0.849 & 0.925 & 0.110 & 0.823 \\ rf\_cp & – & 0.953 & 0.110 & 0.848 & 0.892 & 0.155 & 0.754 \\ svml & SET\_2 & 0.941 & 0.127 & 0.822 & 0.880 & 0.155 & 0.744 \\ svml\_cp & – & 0.918 & 0.158 & 0.773 & 0.848 & 0.163 & 0.710 \\ svmr & SET\_3 & 0.949 & 0.106 & 0.848 & 0.909 & 0.113 & 0.806 \\ svmr\_cp & – & 0.941 & 0.122 & 0.827 & 0.896 & 0.124 & 0.785 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Summary of the metrics obtained in the testing and validation phases of the different models, with each of the available sets of predictors. The models used correspond to the following: ann, artificial neural networks; lg, logistic regression; rf, random forest; svml, support vector machine with linear model. In addition, models that use principal components as predictor variables are considered. These models can be identified by the notation ”_cp” in the “Model” column. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Regularization**} & \multirow{2}{*}{**Kernel Bandwidth**} & \multirow{2}{*}{**Hidden Nodes**} & \multirow{2}{*}{**L2 Penalty Rate**} \\  & & & & \\ \hline lg\_cp & - & - & - & - \\ rf\_cp & 4 & - & - & - \\ ann\_cp & - & - & 10 & 0.1 \\ svml\_cp & 4.80 & - & - & - \\ svmr\_cp & 4.95 & 0.05 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Optimal hyperparameter values for each of the models created using principal components (_cp).