## 3 Result
### The Preliminary LSM  
In terms of landslide susceptibility, the study considered the conditional probability of a landslide as the landslide susceptibility. The conditional probability of a landslide was calculated from the voting score using the Softmax function, and therefore ranges from 0 to 1. Based on previous experience [63; 64], landslide susceptibility had been classified into five classes: (a) low (0-0.1), (b) low-moderate (0.1-0.3), (c) moderate (0.3-0.5), (d) moderate-high (0.5-0.75) and (e) high (more than 0.75).  
For the LR model and RF model, Table 5 reports the results of significant drivers for each model. According to the coefficients and important scores, we considered rainfall, fault, river, and lithology to be important factors influencing landslides in the area. Table 6 illustrates the results of the performance evaluation of each model, with RF showing good results in each metric. The area under the receiver operating characteristic curve (AUC) was used in the statistical assessment of the overall performance of the landslide model to quantitatively compare the performance of various models. The AUC values were generated for the training and test datasets, respectively. The AUC values in the training data showed the ability to map landslide susceptibility. Meanwhile, the AUC values in the test data represent the accuracy of future landslide predictions. In Figure 9, the AUC (0.9665) indicates that the random forest algorithm used achieved good results in the test set, outperforming the SVM algorithm (0.9419) and the LR algorithm (0.8769).  
As shown in Figure 10, brief statistical analysis showed that 8.212% of cells were classified as high susceptibility, 9.778% cells as moderate to high susceptibility, 33.385% cells as moderate susceptibility, 18.899% cells as low to moderate susceptibility, and 29.725% cells as low susceptibility. Comparison of the results with the landslide inventory shows a prediction accuracy of 90.18% for landslide occurrence, indicating that the model results are reliable.  
In addition, the data related to landslide distribution are very different in the pre-earthquake and post-earthquake time periods due to the large number of landslides induced by the earthquake in the study area. We analyzed the difference in LSM distribution before and after the earthquake. According to Figure 11, before the earthquake, only 23% of the area was identified as high susceptibility before the earthquake, but after the earthquake, the area increased to 53% of high susceptibility. Overall, 20% of the earthquake-induced landslides occurred in areas of pre-existing high susceptibility, while 52% occurred in areas of medium and medium-high susceptibility. Only a few landslides occurred in areas of low and low to medium susceptibility, but we attributed this phenomenon to the uncertainty of the earthquake. The different distributions of earthquake-induced landslide susceptibility suggested that the pre-earthquake predictions were valid.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Accuracy** & **Precision** & **F1 Score** & **Recall** & **AUC ROC** \\ \hline LR & 0.7951 & 0.7469 & 0.6953 & 0.7201 & 0.8769 \\ SVM & 0.8724 & 0.8175 & 0.8543 & 0.8355 & 0.9419 \\ RF & 0.9018 & 0.8259 & 0.9375 & 0.8772 & 0.9665 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Evaluation of each model.  
Figure 9: The ROC of preliminary LSM models (random forest, LR and SVM are represented by orange, blue and green, respectively).