## 3 Results and Accuracy Analysis
### Accuracy Statistic  
In order to validate the modelling accuracy of the used models, the landslide distribution in each susceptibility level was statistically analyzed, and the results are shown in Table 7.  
In the SVM model, 88.69% of landslides were located in areas of high susceptibility level, whereas the results of ANN, LR, and CART models were 69.79%, 68.78%, and 62.51%, respectively. Furthermore, the area of high level in SVM model accounted for 20.01% of the total area, but the area of landslide accounted for 88.69% of the entire landslide area, and its frequency ratio was as high as 4.432. The frequency ratios of the other three models were lower than that of the SVM model. ANN and LR models were 3.517 and 3.503, respectively, and the CART model was the lowest of 3.309.  
In practical engineering applications, if the area of very low level is misclassified into the area of high level, it will limit effective land-use. However, if the area of high level is misclassified into the area of very low level, it may bring economic losses and casualties in the area. However, the effects of these two cases on the accuracy statistics are the same. Further analysis showed that the area of very  
\begin{table}
\begin{tabular}{l c c c c c c} \hline
**Susceptibility Level** & **Friends in Landalide** & **Friends in Domain** & **Proportion of LD** & **Proportion of LTL** & **Proportion of DTD** & **Frequency Ratios** \\ \hline SVM & 6 & 154.275 & 0.00\% & 0.02\% & 38.83\% & 0.001 \\ Low & 210 & 88.697 & 0.25\% & 0.83\% & 21.07\% & 0.040 \\ Moderate & 2366 & 79.817 & 3.30\% & 10.46\% & 20.09\% & 0.520 \\ High & 22,360 & 79.500 & 28.13\% & 88.69\% & 20.01\% & 4.432 \\ ANN & \multirow{2}{*}{Very low} & 409 & 160.378 & 0.26\% & 1.62\% & 40.37\% & 0.040 \\ Low & 1741 & 79.155 & 2.20\% & 6.91\% & 19.92\% & 0.347 \\ Moderate & 5479 & 78.975 & 6.94\% & 21.73\% & 19.88\% & 1.093 \\ High & 17,583 & 78.781 & 22.32\% & 69.79\% & 19.83\% & 3.517 \\ LR & \multirow{2}{*}{Very low} & 393 & 161.746 & 0.24\% & 1.56\% & 40.71\% & 0.038 \\ Low & 1838 & 79.127 & 2.32\% & 7.29\% & 19.92\% & 0.366 \\ Moderate & 5640 & 78.411 & 7.19\% & 22.37\% & 19.74\% & 1.133 \\ High & 17,341 & 78.005 & 22.23\% & 68.78\% & 19.63\% & 3.503 \\ CART & \multirow{2}{*}{Very low} & 491 & 160.378 & 0.31\% & 1.95\% & 40.37\% & 0.048 \\ Low & 1341 & 79.419 & 1.69\% & 5.32\% & 19.99\% & 0.266 \\ Moderate & 7621 & 82.440 & 9.24\% & 30.23\% & 20.75\% & 1.457 \\ High & 15,759 & 75.052 & 21.00\% & 62.51\% & 18.99\% & 3.309 \\ \hline \end{tabular} Note: LD = landslide in domain, LTL = landslide in total landslide, DTD = domain in total domain.  
\end{table}
Table 7: Accuracy statistics of the SVM, ANN, LR, and CART models.  
Figure 4: Landslide susceptibility maps obtained from (**a**) ANN model, (**b**) logistic regression (LR) model, (**c**) SVM model, and (**d**) classification and regression tree (CART) model.  
low level of SVM model accounted for 40% of the total study area, but its landslide only accounted for 0.02% of the entire landslide area. Its frequency ratio was the lowest of 0.001, which was much lower than those of ANN, LR, and CART models, with those being 0.040, 0.038, and 0.048, respectively.  
By comparing the accuracy statistics of the four models, we can see that the SVM model had the highest classification accuracy in the area of high level and the lowest misclassification in the area of very low level, showing better prediction performance.