## 4 Results
### Comparison of Different LSM Methods
Generally, the more new landslide events locate in the high susceptibility areas, the more accurate the prediction of the model will be. Figure 12(a) presents ROC curves for four LSM methods based on the stack of causative factors. The AUC values for SVM, LR, KNN, and RF are 0.851, 0.874, 0.885, and 0.911, respectively. Among four methods, RF achieves the highest AUC value (0.911) while SVM has the lowest AUC value (0.851). This indicates that LSM results varied in their accuracies depending on the machine learning methods used, and thus the selection of a right machine learning method is critical to obtain a satisfactory LSM for a specific area. Figure 12(b) shows that AUC values for MCC-SVM, MCC-LR, MCC-KNN, and MCC-RF are 0.985, 0.985, 0.930, and 0.975, respectively. Among four MCC methods, MCC-SVM and MCC-LR produce the highest AUC value (0.985) while MCC-KNN achieves the lowest AUC value (0.930). Overall, all MCC based methods improve the LSM performance compared to the same methods based on the stack feature set. The improvement of AUC values ranges from 4.5% to 13.4%. This suggests that data modality has a significant effect on the LSM performance. Fusion of complementary information from different modalities greatly improves LSM  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Susceptibility Level** & **Area (km2)** & **Area Proportion** & **Landslide Area** & \ \begin{tabular}{c} **Landslide** \\ **Percentage** \\ \end{tabular}  & \begin{tabular}{c} **Density** \\ **Percentage** \\ \end{tabular}  \\ \hline \multirow{5}{*}{SVM} & Very low & 1191.12 & 0.30 & 14.15 & 0.04 & 0.01 \\  & Low & 749.00 & 0.19 & 32.03 & 0.09 & 0.04 \\  & Moderate & 746.60 & 0.18 & 54.36 & 0.15 & 0.07 \\  & High & 720.28 & 0.18 & 100.2  
accuracy compared to using the stack feature set. Thus, the presented method provides a simple and straightforward means to combine complementary information from multiple data modalities, which in turn boosts LSM performance. This verification indicates that the data modality is both statistically viable and provides an improved LSM performance versus stack feature set based models. Since LSM is mainly used to evaluate the possibility of subsequent landslides, a new landslide event is also a valuable reference for verifying the performance of LSM generated. Hence, this study collected 10 new landslides that occurred after the Wenchuan earthquake through searching the Internet. Geographic locations of these landslides were determined by the cross-analysis of the news and time series of satellite images from Google Earth, as shown in Table 4. The statistics on landslide susceptibility corresponding to the new landslides and analysis of the reliability of the landslide susceptibility results are presented in Table 5. For the stack feature set, new landslide occurrence in the non-risk areas (i.e., low, and extremely low susceptibility areas) for SVM, LR, KNN and RF was 1, 1, 1, and 3, respectively. For MMC-SVM, MMC-LR, MMC-KNN and MMC-RF, new landslide occurrence in the non-risk areas was 1, 1, 1, and 1, respectively. This suggests that statistical methods by consideration of