## 1 Introduction  
Landslide dams, formed by natural disasters (e.g., earthquakes, landslides) [1, 2, 3] or human activities, possess unique formation mechanisms and complex internal structures. While they play crucial roles in water resource utilization, ecological protection, and disaster prevention, their stability and longevity are influenced by multiple factors [4, 5], including dam material properties, environmental conditions, and hydrological variations. The failure or breach of landslide dams can trigger catastrophic floods [6], severely threatening downstream communities' lives and property [7, 8]. Therefore, accurate prediction of landslide dam longevity is vital for hydraulic engineering management, disaster prevention, and risk assessment [9, 10, 11]. Traditional stability analyses of landslide dams primarily rely on empirical formulas or numerical simulation methods [12], which typically conduct univariate or multivariate analyses based on geometric characteristics, material composition, or river hydrological conditions. [PERSON] et al. [13] investigated the three-dimensional geometric evolution and influencing factors of landslide dams using Smoothed Particle Hydrodynamics (SPH), developing a predictive model for rapid dam scale calculation. [PERSON] et al. [14] studied dam failure mechanisms through the Tangiashan landslide dam case and model experiments, developing a numerical model for overtopping-induced failures to precisely simulate water-soil coupling processes and breach evolution. [PERSON] et al. [15] developed an event-based physical model for the unified simulation of earthquake-induced landslides, multi-hazard chains, and subsequent processes, achieving significant progress in multi-hazard chain simulation applicability. However, due to the heterogeneity and dynamic evolution of landslide dam formation environments, these methods show significant limitations in handling data complexity and uncertainty, often requiring extensive field monitoring data and complex modeling processes, increasing prediction costs and difficulties. Recent advances in artificial intelligence have introduced new solutions for landslide dam longevity prediction. AI-based models demonstrate powerful modeling potential by analyzing large-scale historical data and uncovering nonlinear relationships between longevity and key parameters. [PERSON] et al. [16] developed a novel method for rapid stability prediction using logistic regression, incorporating morphological characteristics, grain size composition, and upstream lake hydrodynamic conditions, validating its rationality through comparison with typical methods. [PERSON] et al. [17] constructed a global landslide dam case database and utilized XGBoost models to predict stability, addressing missing data prediction challenges. [PERSON] et al. [18] developed a Random Forest model based on geometric parameters and attribute characteristics to predict landslide dam longevity, significantly improving prediction accuracy. [PERSON] et al. [19] studied landslide displacement prediction in the Three Gorges Dam region, comparing LSTM, Random Forest (RF), and GRU algorithms across different landslide types, with results indicating LSTM and GRU algorithms performed best in predicting step-type landslide displacements. [PERSON] et al. [20] developed classification and regression models based on numerous landslide dam cases, providing crucial support for longevity assessment and prediction through high-precision predictions and key factor analysis. [PERSON] et al. [21] proposed the dimensionless blockage index as a geomorphological tool for the accurate assessment of landslide dam behavior and stability, providing a scientific basis for reducing upstream inundation risks and downstream breach flood risks. [PERSON] et al. [22] developed a model for predicting landslide dam failure probability based on Japanese datasets, validating the model's reliability through multiple earthquake and typhoon-induced cases, providing important references for risk assessment and disaster response.