## 5 Results and Analysis
### Landslide Modeling and Evaluation Process  
After selecting significant conditioning factors, we performed the modeling process on the training dataset using SVM, BLR, and ADTree and then tested the results. The goodness-of-fit analysis indicates that all landslide models predict the spatial distribution of landslides well (Table 4), although the SVM model performed best with values of sensitivity, specificity, accuracy, kappa, RMSE, and AUC of, respectively, 0.943, 0.981, 0.962, 0.924, 0.192, and 0.986. The sensitivity and specificity values show that 94.3% of landslides were correctly classified as landslides and 98.1% of non-landslide locations were correctly classified as non-landslide locations. In comparison, the BLR model results yielded values of sensitivity, specificity, accuracy, kappa, RMSE, and AUC of, respectively, 0.906, 0.981, 0.943, 0.886, 0.237, and 0.943, and corresponding values for ADTree are 0.8887, 0.943, 0.915, 0.846, 0.245, and 0.912.  
Next, we assessed the predictive power of the models using the validation dataset (Table 5). The SVM model yielded the highest sensitivity (85.7%), followed by the BLR (78.6%) and ADTree (71.4%) models. The SVM model also provided the highest specificity value (91.7%), followed by the BLR (83.3%) and ADTree (81.8%) models. Accuracy values for the three models are 88.5% (SVM model), 80.8% (BLR), and 76.0% (ADTree). Kappa (0.869) and AUC (0.976) for the SVM model are greater than corresponding values for the BLR and ADTree models. Finally, the SVM model has the lowest RMSE (0.251), followed by the BLR (0.277) and ADTree (0.343) modes. Overall, the results from both the training and validation datasets show that the SVM model outclassed the BLR and ADTree models in predicting the locations of landslides in the study area.  
In addition to the comparison of the performance of the models, based on the statistical-indexed base metrics, we assessed the efficiency of the three algorithms based on the CPU time during the modeling implementation. We concluded that in the SVM algorithm the CPU time to process by the training and validation datasets were 0.03 s; however, in the BLR this time for training dataset was 0.05 s and for validation dataset was 0.03 s. Moreover, the ADTree that had the lowest goodness-of-fit and prediction accuracy had 0.09 s and 0.06 s based on the training and the validation datasets, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **SVM** & **BLR** & **ADTree** \\ \hline TP & 50 & 48 & 47 \\ TN & 52 & 52 & 50 \\ FP & 1 & 1 & 3 \\ FN & 3 & 5 & 6 \\ Sensitivity & 0.943 & 0.906 & 0.887 \\ Specificity & 0.981 & 0.981 & 0.943 \\ Accuracy & 0.962 & 0.943 & 0.915 \\ Kappa & 0.924 & 0.886 & 0.846 \\ RMSE & 0.192 & 0.237 & 0.245 \\ AUC & 0.986 & 0.943 & 0.912 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performances of the SVM, ADTree, and BLR algorithms on the training dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **SVM** & **BLR** & **ADTree** \\ \hline TP & 12 & 11 & 11 \\ TN & 11 & 10 & 9 \\ FP & 1 & 2 & 2 \\ FN & 2 & 3 & 4 \\ Sensitivity & 0.857 & 0.786 & 0.714 \\ Specificity & 0.917 & 0.833 & 0.818 \\ Accuracy & 0.885 & 0.808 & 0.760 \\ Kappa & 0.869 & 0.846 & 0.751 \\ RMSE & 0.251 & 0.277 & 0.343 \\ AUC & 0.976 & 0.923 & 0.910 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performances of the SVM, ADTree, and BLR algorithms on the validation dataset.