## 4 Results
### Model Evaluation Results  
The metrics of f1-measure, recall, and accuracy for the three models were derived from a confusion matrix applied to the test dataset following the training phase. Additionally, the model performance was evaluated using AUC values (Figure 8). The ROC for the SVM, CNN, and ResNet18 models yielded AUC values of 0.9209, 0.9322, and 0.9362, respectively. All exceeded the threshold of 0.9, indicating that these models exhibited an excellent test performance on this dataset. The test data exhibited accuracies of 0.8489, 0.862, and 0.862, recalls of 0.7282, 0.8106, and 0.8788, and f1-measures of 0.7672, 0.7962, and 0.809 for the respective models. The findings demonstrate that the ResNet18 model achieves optimum performance in predicting the probability of landslide susceptibility. Dynamic Evaluation of Landslide Susceptibility and Temporal Probability Analysis of Typical Regions in China  
3.1 Dynamic Evaluation Results and Analysis of Large-Scale Landslide Susceptibility Trends in China from 2001 to 2022  
Using the trained model, we evaluated the probability of landslide events occurring from 2001 to 2022 and generated a dynamic large-scale landslide susceptibility map for  
Figure 8: ROC curve and accuracy evaluation of test data. 22 years. The equal-interval classification technique was employed to categorize susceptibility probabilities into five distinct levels: extremely high (0.8-1), high (0.6-0.8), medium (0.4-0.6), low (0.2-0.4), and extremely low (0-0.2). The area percentage for each landslide susceptibility subzone was calculated for each year (Figure 9). The MK test was conducted on the forecast probability map (Figure 10). The results showed that regions with high and extremely high landslide susceptibility in China were predominantly located in southern Xizang, Yunnan, Guizhou, Sichuan, Chongqing, Hunan, southern Gansu, Shaanxi, Shanxi, western Hebei, western Henan, Hubei, Anhui, Shanghai, Zhejiang, Guangxi, Guangdong, Hainan, Taiwan, on the southeastern coast of Jiangsu, and in the areas along the Tien Shan, Kunlun Mountains, and Qilian Mountains in northern China (Figure 10a). The combined area percentages for high and extremely high susceptibility were recorded at 10.22 percent and 12.97 percent, respectively. Except for a few areas with low precipitation, most areas had high precipitation. Overall, the spatial distribution of landslide susceptibility zones closely correlated with precipitation patterns, as regions with elevated precipitation demonstrated a higher likelihood of landslide occurrences. From the spatial trend test, the probability of landslide susceptibility in the regions of southern Xizang, eastern Sichuan, western Chongqing, the Sichuan-Guizhou border, Heilongjiang, northern Inner Mongolia, and coastal Liaoning, northern Gansu, northern Shanxi, central and western Xinjiang, coastal Guangdong, central Zhejiang, coastal Shandong, central Hunan, southern Anhui, south-central Guangxi, north-central Jiangxi, north-central Qinghai, and along the border of Inner Mongolia and Jilin showed a significant increasing trend (\(p<0.01\)). In contrast, the landslide susceptibility at the border of Gansu and Ningxia and in north-central Shaanxi, northern Shanxi, western Henan, northern Hebei, southern Fujian, and south-central Yunnan showed a significant decreasing trend (\(p<0.01\)) (Figure 10b). Based on the spatial distribution and variation trend of landslide susceptibility, the probability of landslide occurrence in southeastern Xizang, along the border of the Sichuan, Chongqing, and Guizhou provinces, along the coast of Guangdong, on the northern coast of Shandong, and in central Zhejiang, southern Anhui, central Hunan, central Jiangxi, central and southern Guangxi, and Hong Kong was high and showed a significant increasing trend (\(p<0.01\)). The probability of landslide occurrence in southern Gansu, north-central Shaanxi, South Shanxi, west Henan, central Yunnan, south Fujian, Hainan, and Taiwan was high and showed a significant decreasing trend (\(p<0.01\)).