水力发电学报2026，Vol.45Issue(9)：83-96,14.DOI:10.11660/slfdxb.20260908
面板堆石坝渗漏量预测的Seq2Seq-Attention模型
Seq2Seq-Attention model for leakage volume prediction of concrete-faced rockfill dams
摘要
Abstract
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.关键词
面板堆石坝/渗漏量预测/Seq2Seq-Attention/DAKDEKey words
concrete-faced rockfill dam/leakage volume prediction/Seq2Seq-Attention/DAKDE分类
建筑与水利引用本文复制引用
杨吉健,李典庆,唐小松,柯琴..面板堆石坝渗漏量预测的Seq2Seq-Attention模型[J].水力发电学报,2026,45(9):83-96,14.基金项目
国家自然科学基金重点项目(52439007) （52439007）
