## 4 Results
### Comparison of Different LSM Methods  
This study compares the presented model to three benchmark methods, including support vector machine (SVM), linear regression (LR), KNN, and random forest (RF), and Figure 11 illustrates results of these four LSM methods. The LSM results produced by different methods vary from each other. In the LSM obtained by the MMC model, the landslide risk areas are mainly distributed along the water system, mostly concentrated in the eastern and northeastern part of the study area, and a small amount are distributed in the southeast and western regions; see Figure 12. Among the four LSM methods, the MMC result is the closest to the actual spatial distribution of landslides induced by the Wenchuan earthquake. Figure 11: Landslide susceptibility maps produced by different methods: (**a**) SVM, (**b**) LR, (**c**) KNN, and (**d**) RF. Figure 10: The effect of (**a**) k and (**b**) T on the presented method. To quantitatively assess the reliability of the model, we analyzed and verified the reliability of the landslide susceptibility assessment model through the complete landslide inventory data in the study area. Table 4 shows the area of each susceptibility subdivision in the LSM derived from different models, and counts the area of historical landslides in the different subdivisions. In this paper, the area of historical landslides developed in the dangerous zone (containing both very high and high susceptibility areas) is used as the accuracy of model predictions. The statistical results showed that the proportion of landslide area in the hazard zone reached 84% in the MMC modeling, 75% in the KNN model, 72% in the SVM model, and 74% in the LR model. This result shows that the MMC mode has the best performance. Model evaluation can be used to verify the evaluation performance of LSM. There are two commonly used model evaluation methods, namely, superposition analysis based on complete landslide inventory and comprehensive model evaluation based on independent data sets. The independent data set is mainly established in the preliminary data preparation stage. In this paper, the performance of the model is verified by the above two evaluation methods. Figure 12: Landslide susceptibility maps produced by different methods considering data modality: (**a**) MMC-SVM, (**b**) MMC-LR, (**c**) MMC-KNN, and (**d**) MMC-RF. In general, landslide inventory mapped after an earthquake is important reference data to verify the model performance. Accurate and reliable LSM should meet the following two requirements: (1) as many landslides as possible should fall into the high susceptibility area, that is, the higher the value, the more accurate the LSM; (2) The proportion of high susceptibility areas in the study area should be as small as possible, that is, the lower the proportion, the more is the reliability of the LSM. The model is considered to have good performance when it meets both requirements. However, this method relies on the classification of susceptibility level, that is, the determination of threshold, and depends more on human subjective factors. Therefore, this method can only represent the reliability of LSM under specific threshold conditions. In contrast, the receiver operating characteristic (ROC) curve is not affected by threshold setting, which is a commonly used method in traditional classification models. By calculating the true positive rate and false positive rate under different thresholds, a curve with true positive rate as \(X\)-axis and false positive rate as \(Y\)-axis is established. AUC value of ROC is a quantitative indicator of model performance. the higher the AUC value is, the more reliable the model performance is. To further evaluate the prediction accuracy of the model, data on new landslide events after the 2008 Wenchuan earthquake were collected, and the landslide susceptibility level corresponding to the new landslide events was taken as the evaluation index of the model.