## 5 Discussion
### Comparison of Evaluation Results of Landslide Susceptibility  
Based on the global dynamic landslide susceptibility model, the large-scale landslide susceptibility in China from 2001 to 2022 has been evaluated and its spatial variation trends analyzed. The results indicate that regions with high and extremely high landslide susceptibility are predominantly concentrated in southwest China. A comparative analysis of the landslide susceptibility zones produced in this study with those from two existing national assessments reveals both similarities and differences (Figure 14). [PERSON] [29] suggested that the spatial distribution of landslide risk in China is largely delineated by the Heihe-Tengchong line, with the western regions characterized by predominantly low-risk areas, and the eastern regions exhibiting higher concentrations of medium to high-risk zones (Figure 14a). Similarly, [PERSON] [31] identified areas of high landslide risk in southwest China, as well as in the hilly regions south of the Yangtze River (Figure 14b). In general, the spatial distribution of landslide susceptibility in this study (Figure 14c,d) aligns well with previous assessments (Figure 14a,b), though some regional differences are evident. For instance, [PERSON] [29] categorized the southern Gansu region and the border area between Chongqing and Sichuan as medium- to high-risk zones, whereas this study classifies these areas as very high-risk. Similarly, [PERSON] [31] identified Hunan, Jiangci, and other regions as areas of extreme susceptibility, a finding that is consistent with the results of this study. These  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **Hyperparameter or** & **Training Time 1** & **F1-Measure** & **F1-Measure** \\  & **Network Parameter** & **(Relative)** & **(Global Training)** & **(Local Training)** \\ \hline SVM & 2 & 0.28 h & 0.762 & 0.736 \\ \hline CNN & 67,171,009 & 1.85 h & 0.796 & 0.744 \\ \hline ResNet18 & 11,529,729 & 1.25 h & 0.809 & 0.691 \\ \hline \hline \end{tabular} Notes: The f1-measure represents the f1-measure obtained from testing the global and local training data. 1 Experimental environment: framework—TensorFlow2.10 + cuda118, CPU—Intel (R) Core (TM) i5-13400F, GPU—GeForce RTX 4080 (16 G).  
\end{table}
Table 4: SVM, CNN, and ResNet18 models in global training versus local training comparison.  
discrepancies can likely be attributed to differences in the landslide databases used in each study, which may lead to variations in the identification of landslide-prone areas due to differences in the regional distribution of landslide events.  
Additionally, a trend analysis of large-scale landslide susceptibility in China over the past 22 years reveals a significant increase in susceptibility in certain regions, particularly in Chongqing, Guizhou, and Sichuan, where the probability of landslide occurrence has notably risen. [PERSON] and [PERSON] [65] conducted a time-series analysis of China's recorded catastrophic landslides, showing a clear upward trend in the frequency of such events. [PERSON] [29] also mentioned an increase in landslide risk in China over the next decade, with high-risk areas expected to expand westward beyond the Heihe-Tengchong line. At the same time, [PERSON] and [PERSON] [66] study showed that the areas in Sichuan with high and extremely high landslide susceptibility have increased with the increase in rainfall in recent decades. Therefore, the large-scale landslide occurrence in some areas of China has shown an obvious increasing trend in the past 20 years, and the landslide trend in Sichuan and other places, especially, should be paid attention.  
Figure 14: Comparison analysis of landslide susceptibility maps from previous studies. (**a**) Landslide hazard map developed by [PERSON] [29]. (**b**) LSM prepared by [PERSON] [31]. (**c**) LSM for 2017 based on the ResNet18 from this study. (**d**) LSM for 2020 based on the ResNet18 from this study.