## 5 Discussion
### Validation and Comparison with Physical Methodologies  
Table 11 displays the percentage values of the TAC, GFC, and SR indicators for the selected best models in statistical terms, which assess their performance regarding the actual location of slope instabilities triggered by the 2011 Lorca earthquake. Upon analyzing the results provided by the machine learning models, it is observed that the SR coefficient values range from 44% to 47%, except for the RF model, which has the lowest SR score (14.33%). It is worth noting that this model is the best in statistical terms, but it performs the worst in terms of the SR indicator, as its predictions fail to effectively model the co-seismic landslide's location. On the contrary, SVMR achieves the highest score in terms of the SR indicator (47.20%), despite being the third-best model in statistical terms, followed by the ANN model in terms of SR (46.90%), which is the second-best model in statistical terms. Considering both models, the variables that control the co-seismic landslide occurrence are the same as those explained above for the RF model. These results agree with those of [23], in which curvature was selected as a relevant variable. Additionally, [24] emphasized the distance to rivers, whereas [25] highlighted the importance of proper data preparation to avoid biases in predictive models. This finding supports the relevance of these variables in predicting co-seismic landslides. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model** & **\% TAC** & **\% GFC** & **\% GFC-\% TAC** & **\% SR** \\ \hline ann\_cp & 19.85 & 90.27 & 70.42 & 63.57 \\ svmr\_cp & 19.66 & 89.88 & 70.22 & 63.12 \\ lg\_cp & 19.61 & 86.38 & 66.48 & 57.42 \\ svml\_cp & 19.65 & 85.21 & 65.56 & 55.87 \\ rf\_cp & 16.08 & 81.71 & 65.63 & 53.63 \\ svmr & 11.53 & 74.71 & 63.18 & 47.20 \\ ann & 11.22 & 74.32 & 63.10 & 46.90 \\ svml & 13.95 & 74.32 & 60.36 & 44.86 \\ lg & 13.81 & 73.93 & 60.12 & 44.45 \\ rf & 3.57 & 39.69 & 36.12 & 14.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Summary of the comparison of seismic-induced landslide hazard maps of each statistical model and the actual location of the mapped slope instabilities. The models used correspond to the following: ann, artificial neural networks; lg, logistic regression; rf, random forest; svml, support vector machine with linear model; svmr, support vector machine with radial base model. Models identified by the notation “_cp” correspond to models that use principal components. When conducting the same analysis with the machine learning models built from principal components, all models show substantial improvement in terms of the SR indicator, with values exceeding 50%. The principal components retain more information in terms of the explained variance of the features, enabling more accurate predictions throughout the entire map. The model corresponding to ANN (ann_cp) achieves the highest score for the SR indicator (63.57%), although it does not suggest being the best statistically among all the analyzed predictive models. According to this model, the variables that control the co-seismic landslide include orientation, Newmark displacement, landform, curvature, roughness, PGA, Arias intensity, and lithology (alluvial). Comparing the variables selected by the PCA with those in SET-3 shows substantial agreement. However, when comparing with those selected in [68], it is found that both algorithms use similar variables in their predictions as follows: lithology, earthquake intensity parameters, and curvature. These matches in variable selection highlight the importance of these factors in predicting co-seismic landslides. It is important to mention that the second-best model, according to the SR indicator (63.12%) is the SVMR built from the principal components (svmr_cp). This model has values that are very close to those of the best model (ann_cp). Therefore, these two models are considered the best in terms of the SR indicator. In contrast, the third-best model (lg_cp) is quite distant in terms of the SR score (57.42%).