水利水电科技进展2025，Vol.45Issue(6)：85-90,119,7.DOI:10.3880/j.issn.1006-7647.2025.06.012
耦合GAT和TCN的土石坝渗流监测数据异常检测模型
Anomaly detection model for seepage monitoring data of earth-rock dams based on coupled GAT and TCN
摘要
Abstract
Aiming at the problem that existing anomaly detection models for seepage monitoring data of earth-rock dams exhibit weak generalization capability and can only identify specific types of abnormal measurements,leading to low detection accuracy and high false-alarm rates,an anomaly detection model for earth-rock dam seepage monitoring data based on the graph attention network(GAT)mechanism and temporal convolutional network(TCN)is proposed.This model utilizes the GAT mechanism to assign weights to environmental factors,employs the TCN to improve the extraction of temporal features,and identifies abnormal data based on the model's prediction error.Taking a clay-core rockfill dam in northwest China as a case study,six anomalous scenarios were constructed to validate the model.The results show that the proposed model can accurately identify abnormal seepage measurements of earth-rock dams,with average ROC-AUC and PR-AUC values of 0.948 and 0.968,respectively,meeting the requirements of practical engineering applications.Comparative analyses with state-of-the-art anomaly detection models further confirm that the proposed model exhibits superior anomaly detection performance and robustness.关键词
土石坝渗流/大坝安全监测/异常检测/图注意力网络/时域卷积网络Key words
earth-rock dam seepage/dam safety monitoring/anomaly detection/graph attention network/temporal convolutional network分类
建筑与水利引用本文复制引用
廖攀,李晓庆,顾昊,唐新军..耦合GAT和TCN的土石坝渗流监测数据异常检测模型[J].水利水电科技进展,2025,45(6):85-90,119,7.基金项目
国家自然科学基金黄河水科学研究联合基金(U2243223) （U2243223）
新疆农业大学研究生校级科研创新项目(XJAUGRI2022021) （XJAUGRI2022021）
