## 6 Conclusions  
This study provides an efficient, accurate, and interpretable solution for weir life prediction by improving the model, introducing the SHAP method, and implementing multi-scale feature modeling. The following conclusions were drawn:  
(1) The study collected and organized 297 sets of data on weir dam lifespan, constructing a comprehensive sample database. By selecting 12 key factors affecting weir dam lifespan as predictive indicators, the correlations between these factors were analyzed, and the degree of correlation was quantified. The results reveal a significant correlation between the geometric characteristics of weir dams (e.g., dam height, dam length, dam width) and hydrological conditions (e.g., water storage volume, upstream catchment area), with correlation coefficients of 0.73, 0.62, and 0.68, respectively. This analysis provides a scientific basis for the selection and optimization of features in subsequent models. (2) An Improved Black-Winged Kite Algorithm (IBKA) is proposed to optimize the hyperparameters of the CNN-Transformer hybrid model. The IBKA algorithm significantly enhances the global search ability and local optimization accuracy by introducing a dynamic adaptive mechanism, population clustering strategy, and dynamic migration strategy. Compared with the traditional BKA algorithm, the IBKA dynamically adjusts the learning rate and migration probability based on the adaptation degree during the iteration process, effectively avoiding the loss of population diversity and local optimization issues. Experimental results demonstrate that the IBKA algorithm exhibits superior adaptability and stability in complex, high-dimensional optimization problems. Specifically, when handling nonlinear features in weir life prediction, it rapidly converges to the global optimal solution, significantly improving the prediction accuracy and generalization ability of the model. (3) Based on the improved IBKA algorithm, the CNN-Transformer hybrid model constructed in this study demonstrates exceptional performance in the weir life prediction task. Experimental results indicate that the model achieves a coefficient of determination (R\({}^{2}\)) of 0.99 and 0.98 on the training and test sets, respectively, significantly outperforming traditional machine-learning models (e.g., LightGBM, MLP, and SVR) and other deep-learning models (e.g., CNN-Transformer and BKA-CNN-Transformer). The superiority of this model is primarily attributed to its combination of the local feature extraction capability of CNN and the global feature modeling capability of Transformer. The CNN module efficiently captures local patterns of weir geometric features through convolution and pooling operations, while the Transformer module captures complex interactions among global features through the multi-head self-attention mechanism. Furthermore, the optimization of the model hyperparameters by the IBKA algorithm further enhances its adaptability and generalization ability in complex nonlinear data. This improved model provides an efficient and accurate solution for weir life prediction with broad application potential. (4) By introducing the SHAP (SHapley Additive exPlanations) method, this study significantly improves the interpretability of the model and quantifies the contribution of key features to weir life prediction. The analysis results indicate that the geometric characteristics and hydrological conditions of weir dams are the primary drivers of their lifespan. Specifically, dam length contributes the most to the prediction, at 26.9%, followed by dam lake volume, at 20.7%. Upstream catchment area and dam volume contribute 11.3% and 9.6%, respectively, highlighting the significant influence of hydrological conditions and dam structure on lifespan. In contrast, triggers (e.g., earthquake and rainfall) contribute less, with the earthquake trigger and the rainfall and snowmelt trigger contributing only 3.4% and 2.7%, respectively.