水力发电2026，Vol.52Issue(1)：74-80,7.
融合动态图卷积与时序卷积的多序列渗流压力预测方法研究
A Multi-sequence Seepage Pressure Prediction Method Based on Dynamic Graph Convolution and Temporal Convolution
CHENG Zhengfei 1WU Guohua 2YU Jialin 1PU Guoqing 3YU Hongling4
作者信息
- 1. China Renewable Energy Engineering Institute,Beijing 100120,China
- 2. National Key Laboratory of Intelligent Construction and Operation of Hydraulic Engineering,Tianjin University,Tianjin 300072,China
- 3. Sichuan Huadian Luding Hydropower Co.,Ltd.,Chengdu 610041,Sichuan,China
- 4. College of Water Resources and Civil Engineering,China Agricultural University,Beijing 100083,China
- 折叠
摘要
Abstract
The evolution of seepage conditions is directly related to the long-term operation safety of earth-rock dams.To enhance the perception and early warning capability of seepage pressure trends,a multi-sequence seepage pressure prediction method that integrates dynamic graph convolution and temporal convolution is proposed.A sliding correlation mechanism is used to construct a dynamic adjacency matrix,capturing time-varying spatial dependencies among monitoring points,and the graph convolutional network(GCN)is employed to extract structural features,while the temporal convolutional network(TCN)is introduced to capture long-term dependencies,thereby enabling accurate prediction of seepage trends.Based on multi-year measured seepage pressure data from a large earth-rock dam in Southwest China,a series of comparative experiments are conducted.The results show that incorporating dynamic graph structures can improve model performance by approximately 18%,and when replacing the TCN with a multilayer perceptron(MLP),the MAE increased to 1.26 and the MAPE rose to 9.59%,confirming the critical role of TCN in capturing temporal dependencies.关键词
土石坝/渗流压力预测/滑动相关性/图卷积网络/时序卷积网络Key words
earth-rock dam/seepage pressure prediction/sliding correlation/graph convolutional network/temporal convolutional network分类
建筑与水利引用本文复制引用
CHENG Zhengfei,WU Guohua,YU Jialin,PU Guoqing,YU Hongling..融合动态图卷积与时序卷积的多序列渗流压力预测方法研究[J].水力发电,2026,52(1):74-80,7.
