## 1 Introduction
[PERSON] et al. [23] predicted landslide displacement rates in China's Baihetan Reservoir area using progressive deep learning and AdaBoost ensemble algorithms, demonstrating significantly improved prediction accuracy. Although these studies have enriched the theoretical research on landslide dams, several challenges remain in longevity prediction. The diverse influencing factors of landslide dam longevity, such as geological conditions, dam materials, hydrological characteristics, and climate change, pose higher requirements for model feature extraction capabilities due to their complex interactions. Traditional deep-learning models often overfit on small datasets, limiting their adaptability to diverse scenarios. Moreover, deep-learning models are often viewed as \"black boxes\", making it difficult to provide specific feature contributions and prediction rationales, limiting their practical engineering applications due to this lack of interpretability. Addressing these current research challenges, this paper proposes a hybrid IBKA-CNN-Transformer model. This model retains CNN's advantages in local feature extraction while incorporating Transformer's powerful global feature modeling and long-distance dependency capture capabilities, effectively addressing the modeling challenges of complex multidimensional features in landslide dam longevity prediction. Furthermore, the introduction of optimization algorithms for parameter tuning enhances the model's adaptability and generalization capabilities under small-sample conditions. To validate model effectiveness, comprehensive experimental studies were conducted based on multi-source heterogeneous landslide dam longevity datasets, evaluating the framework's performance in multidimensional feature input regression prediction tasks. The experimental outcomes illustrate that the model put forward remarkably surpasses the current methods in terms of prediction performance and robustness. It reveals great potential in the longevity prediction of complex systems. Additionally, this paper employs SHAP methods to interpret model outputs, quantifying feature contributions to prediction results, thereby significantly enhancing model transparency and reliability. This not only helps improve the model's practical application value but also supports engineering managers in identifying key influencing factors and developing targeted management strategies. This research not only provides theoretical references for innovative applications of deep-learning technology in complex system modeling but also pioneers new approaches in landslide dam longevity prediction, offering reliable tools for safety management, risk assessment, and disaster warning. The reliability of the proposed model is assessed based on its predictive performance on a separate test set. To further validate its robustness, we compared the model's performance with baseline methods, including traditional machine-learning models (SVR, MLP, LightGBM) and deep-learning models (CNN-Transformer, BKA-CNN-Transformer). The model achieved R\({}^{2}\) values of 0.99 for the training set and 0.98 for the test set. Additionally, SHAP-based feature importance analysis was conducted to enhance the model's interpretability, providing a deeper understanding of its decision-making process. Meanwhile, the proposed model and methods demonstrate strong generalizability, applicable to engineering problems in other complex geological environments, providing theoretical foundations and practical support for the development of intelligent prediction technologies, with significant implications for both scientific research and engineering practice.