## 4 Results  
### Performance and Comparison of Conventional and Advanced Algorithms  
Before modeling, the data were normalized with Z-scores to eliminate the effects of different dimensions. Additionally, a correlation analysis was conducted to test the collinearity among the independent variables using the variance inflation factor (VIF) [44]. There is severe collinearity between the selected variables if the VIF value exceeds 10. Table 2 showed the VIF values of the chosen independent variables, and the factor elevation has the highest VIF value (5.711). The result indicates that no severe collinearity problems exist among the chosen variables, and thus 11 conditioning factors were applied to the modeling.  
The performance of the three models using the confusion matrix is shown in Table 3. As for training, the CNN model achieved the highest value of sensitivity (84.21%), followed by the CatBoost model (sensitivity = 82.96%) and the LR model (sensitivity = 81.45%). As for specificity, the CNN model performed best with 93.52%, followed by the CatBoost model at 86.63% and the LR model at 84.79%. The CNN model had the best accuracy and ROC values of 88.88% and 0.944, while the LR model had the worst values of 83.13% and 0.897. As well, CatBoost performed well, scoring 86.63% and 0.930, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & **Actual Values** & **Accuracy** & **Sensitivity** & **Specificity** \\ \hline \multirow{4}{*}{Predicted Values} & \multicolumn{2}{c}{Positive (1)} & Negative (0) & & \\  & \multicolumn{2}{c}{Prositive (1)} & \multicolumn{2}{c}{False Negative (TP + TN)/} & & \\  & \multicolumn{2}{c}{Positive (1)} & \multicolumn{2}{c}{False Negative (FN)} & (TP + TN + FP + FN) & TN/(TN + FP) & TN/(TN + FP) \\ \cline{2-6}  & \multicolumn{2}{c}{Negative (0)} & \multicolumn{2}{c}{False Positive (FP)} & True Negative (TN) & & \\ \hline \end{tabular}
\end{table}
Table 1: Confusion matrix analysis with evaluation measures.  
Model performance declined in verification, particularly for the LR and CatBoost models (the accuracy value decreased by about 6%), which indicated that the models were overfitting and generalizability was suspicious. Meanwhile, the performance of the CNN model was stable as the value of AUC reached 0.908, and the accuracy value was close to the training data set.  
\begin{table}
\begin{tabular}{c c} \hline
**Variables** & **VIF** \\ Elevation & 5.117 \\ Slope & 3.426 \\ MED & 5.726 \\ Plan curvature & 1.499 \\ Profile curvature & 1.291 \\ TWI & 6.071 \\ Distance to fault & 2.641 \\ Distance to stream & 4.492 \\ Distance to road & 4.302 \\ Average annual precipitation & 1.763 \\ NDVI & 2.697 \\ \hline \end{tabular}
\end{table}
Table 2: Multicollinearity diagnosis indexes for variables.  
Figure 6: Analysis of ROC curve for the landslide susceptibility map: (**a**) success rate curve of landslide using the training data set; (**b**) prediction rate curve of landslide using the validation dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & & **Training** & \multicolumn{3}{c}{**Validation**} \\ \hline Parameter & LR & CatBoots & CNN & LR & CatBoots & CNN \\ Sensitivity (\%) & 81.45 & 82.96 & 84.21 & 79.38 & 76.28 & 79.38 \\ Specificity (\%) & 84.79 & 90.27 & 93.52 & 76.00 & 85.00 & 91.00 \\ Accuracy (\%) & 83.13 & 86.63 & 88.88 & 77.66 & 80.71 & 85.28 \\ AUC & 0.897 & 0.930 & 0.944 & 0.838 & 0.893 & 0.908 \\ \hline \end{tabular}
\end{table}
Table 3: Models’ performance using related indices.