## 1 Introduction
Therefore, conducting systematic assessments of major disaster risks, such as catastrophic landslides, over large regions and extended time periods is of paramount importance. Furthermore, investigating landslide susceptibility by analyzing factor-induced landslides and integrating dynamic factors is an important area of current disaster early warning,prevention, and mitigation research [17]. [PERSON] [18] developed a web-based platform for assisting decision making to assess the potential landslide activity in real-time. [PERSON] [19] created a system dynamics model to examine the complex relationship between rainfall patterns and spatial vulnerability. By incorporating real-time rainfall data and adjusting previous rainfall periods, it enhances the accuracy of predicting landslide warning times and potential locations. [PERSON] [20] unveiled a fresh approach for the early detection of landslides in dynamic susceptibility analysis. The approach utilizes a blend of SBAS-InSAR data based on time series along with static conditional variables to establish an LSM model. This produced various LSM tools over time, enabling the spatiotemporal assessment of landslide susceptibility within the research area. [PERSON] [21] introduced a strategy for conducting a dynamic analysis of landslide susceptibility within a research region by focusing on the dynamic influence of land use. Meanwhile, neural networks have been widely applied in temporal landslide prediction. For example, [PERSON] [22] used DL for predicting shallow landslides triggered by rainfall. [PERSON] [23] employed artificial neural networks to study the impact of rainfall and soil moisture on the landslide trigger threshold. Additionally, [PERSON] [24] used ML for spatiotemporal landslide probability assessment in the Kvam (Norway). Therefore, it is important to use neural networks for landslide temporal prediction and evaluation. In conclusion, these studies provide valuable insights into both large-scale and dynamic LSM. However, most current studies on large-scale dynamic landslide susceptibility focus on a single dynamic factor and employ a simple ML method [21; 25; 26]. Therefore, the study uses the ResNet18 to develop a global dynamic landslide susceptibility model that includes precipitation, land cover type, and the NDVI (Normalized Differential Vegetation Index) as dynamic condition factors, combined with elevation, aspect, slope, profile curvature, the TRI (Terrain Roughness Index), plane curvature, distance to faults, lithology, and surface soil taxonomy. China, recognized as one of the nations with the highest incidence of landslides globally, was selected as the focus of the study. The contributions of the study are organized into three components. Firstly, a dynamic landslide susceptibility model was employed to assess large-scale landslide susceptibility across China over a period of 22 years, from 2001 to 2022. Secondly, the landslide susceptibility trends in China during this timeframe were tested using the MK (Mann-Kendall) test method. A time series analysis of representative regional points was conducted to clarify the evolving trends of landslide hazards that have occurred in the country over the past 22 years. Thirdly, this study explained the model through SHAP (Shapley Additive Explanations) and revealed the effect of conditional factors on dynamic landslide susceptibility. This research provides a scientific basis for decision makers to create effective adaptation strategies and serves as a valuable reference for landslide hazard warning, prevention, and mitigation.