## 3 Database  
### Data Sources  
The data for this study were obtained from the research of [PERSON] [35] and [PERSON] [36], both focusing on landslide dams formed by natural mass movements such as landslides, rocksslides, or debris avalanches. These dams, resulting from various types of mass movements, exhibit different material compositions, including rock, debris, and soil. These dams were not human-made but are the result of natural processes. [PERSON] [35] compiled a global database of 410 landslide dams, each with a volume greater than 1 million m3, formed since 1900. This database includes detailed information on dam longevity and stability along with key parameters such as landslide type, dam material, and geomorphological features known to influence dam lifespan. The dataset plays a vital role in identifying trends, particularly the relationship between dam material and longevity. [PERSON] [36], in contrast, focused on the full longevity of landslide dams, which was divided into three stages: infilling, overflowing, and breaching. The study developed regression models to estimate the total longevity, incorporating dam characteristics, hydrological conditions, and triggering factors (e.g., rainfall and earthquakes). The dataset used by [PERSON] [36] is enriched with multi-dimensional data on triggering factors, dam materials, and geometric/hydroological parameters, all essential for estimating dam lifespan across the different stages. These comprehensive datasets from both sources provide a solid foundation for modeling dam longevity under varying environmental conditions and offer robust data for assessing dam stability and failure risk. To ensure the inclusion of the most critical features for landslide dam lifespan prediction, the study refers to the method of [PERSON] [20] et al. for scientific selection and the optimization of key indicators; furthermore, a structured feature selection process is employed, integrating correlation analysis, domain expertise, and SHAP analysis. Initially, Pearson correlation analysis was conducted to evaluate the relationships between features and to identify highly correlated or redundant variables, ensuring that selected features contribute uniquely to the model. The detailed results of the correlation analysis are provided in Section 3.4. Expert knowledge from the field of geotechnical and hydrological engineering was also incorporated to retain critical variables that are known to impact dam stability; these indicators cover multiple dimensions such as dam size, upstream catchment area, triggering factors, and dam materials of weir dams, which systematically characterize the core factors affecting the stability and lifetime of weir dams. Following model training, SHAP analysis was subsequently used to quantify the contribution of each selected feature to the model's predictions, offering further insights into the importance of these features and validating the feature selection process. This systematic approach ensures that the final feature set maintains both predictive efficiency and practical significance, enhancing model generalization and interpretability. The dam scale includes dam height, dam length, dam width, dam volume, and weir volume, which directly determine the performance of a weir dam in terms of external pressure and internal stability; among these, the dam height affects the sliding resistance of the dam body, the dam length and dam width reflect the planar scale of the dam body and the contact area of the substrate, the dam volume reveals its ability to withstand gravity and seismic effects, and the weir volume is directly related to the water pressure changes and their potential threat to the dam body. The upstream catchment area, as a determinant of the regional hydrological catchment, directly affects the weir's water reserve and dam water pressure. The larger its value, the higher the hydrodynamic load triggered by rainfall or snowmelt in the watershed, thus increasing the risk of dam instability.