## 5 Discussion
### Analysis of Limitations  
Although this study achieved significant results, it has some limitations. First, the inherent bias in the underlying landslide data and possible incompleteness of the single global landslide database used in this study may have impacted the landslide evaluation. At the same time, this study reflects only the trend of large-scale landslides in large regions, and further research is needed on the trend of small regional landslides. In terms of dataset biases, while we used a global landslide database, it is important to acknowledge the inherent limitations and biases that may arise from the database itself. These include the potential underreporting of landslides in certain regions, the reliance on historical landslide records, which may miss recent events, and variations in reporting standards across different countries and regions. Such biases could impact the accuracy and representativeness of the landslide data, particularly in areas with sparse data or where landslides are not systematically recorded.  
Second, uncertainties and limitations exist in the selected LCFs. Although DEMs nowadays are highly accurate, this study employed a spatial resolution of 1 km, which resulted in some small slopes not being reflected, thereby affecting the landslide evaluation.  
Third, the main objective of this study is to assess landslide susceptibility on a large scale and in the long term. However, some landslides are triggered by extreme events (e.g., rainfall). In order to smooth out the effects of short-term extreme events and to avoid single extreme events causing frequent local landslides and, thus, affecting the overall trend analysis, an annual temporal resolution is used in this paper. In addition, the annual temporal resolution is more appropriate for the analytical framework of this study, given the time span of the data. Therefore, the annual scale trend analysis in this paper does not further investigate the specific impact of extreme events on large-scale landslide hazard susceptibility. Existing studies [19; 25] have shown that the impact of short-term extreme rainfall events on landslides can be more accurately captured using data with a higher temporal resolution. Therefore, future studies should consider introducing finer temporal resolution data to gain a deeper understanding of the interaction between short-term extreme weather events and landslides.  
This study used interpretable techniques to conduct an in-depth analysis of the DL models, revealing the crucial role of precipitation in landslide occurrence. Although SHAP was used to explain the model, the results were not fed back into the variable selection process to demonstrate the benefits of SHAP. In light of the limitations identified in this study, future research should focus on refining data collection by integrating more localized, real-time, and high-resolution datasets to overcome biases and improve the representativeness of landslide data. Additionally, selecting a broader range of dynamic factors and employing higher temporal resolutions, such as monthly or daily records, would allow for more accurate modeling of landslide susceptibility, especially in areas with rapid environmental changes. Enhancing model interpretation techniques, such as incorporating real-time monitoring systems and advanced machine learning methods, will further improve the precision and reliability of landslide predictions. This will ultimately strengthen the overall dependability of susceptibility assessments.