灌溉排水学报2025，Vol.44Issue(7)：70-79,10.DOI:10.13522/j.cnki.ggps.2024213
基于深层挖掘变形时间序列的大坝预测模型
Prediction of dam deformation using adaptive noise CEEMDAN and BiGRU time series modeling
摘要
Abstract
[Background and Objective]Accurate prediction of dam deformation is crucial for ensuring the safety of dam structures in engineering monitoring.Dam deformation is influenced by multiple factors,including water pressure,temperature,and material aging,which often exhibit nonlinear and dynamic relationships.During monitoring,system noise and observation errors frequently interfere with data quality,posing additional challenges for analysis.To address the challenges posed by system noise and strong nonlinear effects in dam deformation,this paper proposes a dam deformation monitoring model based on multi-layer integrated signal processing technology.[Method]The model uses sample entropy reconstruction and the K-means clustering algorithm to optimize the adaptive noise complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN)process,generating multiple intrinsic mode functions(IMF).High-frequency modal components undergo secondary decomposition using variational mode decomposition(VMD)to extract the optimal intrinsic mode function.Finally,an improved symbiotic biological search algorithm combined with a Bidirectional Gated Recurrent Unit(BiGRU)is used to accurately predict dam deformation.[Result]Case analysis demonstrates that,compared to traditional prediction models,the proposed model achieves a root mean square error(RMSE)of 0.031 9 mm,mean absolute error(MAE)of 0.015 3 mm,mean absolute percentage error(MAPE)of 2.51%,and determination coefficient(R2)of 0.971 2.[Conclusion]The results verify that the proposed model captures and simulates the dam deformation process more accurately,exhibiting higher prediction accuracy and stronger generalization ability.关键词
大坝变形/自适应噪声完全集合经验模态分解/样本熵重构/K-means聚类算法/共生生物搜索算法/变分模态分解Key words
dam deformation/complete ensemble empirical mode decomposition of adaptive noise/sample entropy reconstruction/K-means clustering algorithm/symbiotic search algorithm/variational mode decomposition分类
建筑与水利引用本文复制引用
王子轩,欧斌,陈德辉,杨石勇,赵定柱,傅蜀燕..基于深层挖掘变形时间序列的大坝预测模型[J].灌溉排水学报,2025,44(7):70-79,10.基金项目
国家自然科学基金项目(52069029,52369026) （52069029,52369026）
水灾害防御全国重点实验室2023年度"一带一路"水与可持续发展科技基金项目(2023490411) （2023490411）
云南省农业基础研究联合专项面上项目(202401BD070001-071) （202401BD070001-071）
