To predict the long-term leakage and seepage volume of a concrete-faced rockfill dam

a new method based on the Seq2Seq-Attention model is presented. First

we adopt a Sequence-to-Sequence (Seq2Seq) model to construct a basic prediction framework. To evaluate the importance of each feature and enhance model interpretability

we calculate feature correlation coefficients by combining the Pearson and Spearman correlation analyses

and obtain feature importance indicators by applying the Extreme Gradient Boosting (XGBoost) algorithm

achieving a comprehensive assessment of the impacts of different features on the leakage. Then

a self-attention mechanism from Transformer is adopted to assign differentiated weights to the features at different time steps

so that the interference from irrelevant information is mitigated and the prediction of long-term leakage sequences is improved. A comprehensive evaluation－using the metrics of R2

MAE

RMSE

and MAPE－shows the model￠ calculations of 0.995

2.23 L/s

3.40 L/s

and 3.93%

respectively

indicating an average improvement of 0.33-1.31% with superior robustness and applicability

compared to the Seq2Seq and Bidirectional Long Short-Term Memory models. Additionally

we apply Depth-based Adaptive Kernel Density Estimation (DAKDE) to calculate the probability density function and probabilistic prediction results of the model residuals. From the results

the 95% confidence interval achieves the best engineering balance

and the coverage probability agrees with its theoretical calculation

with a moderate interval width and a balanced performance across all the metrics

thereby suitable for practical applications.