Article(id=1222940982795034818, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1222940980886626474, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2023.20231076, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1687968000000, receivedDateStr=2023-06-29, revisedDate=1690646400000, revisedDateStr=2023-07-30, acceptedDate=null, acceptedDateStr=null, onlineDate=1769501451443, onlineDateStr=2026-01-27, pubDate=1700841600000, pubDateStr=2023-11-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769501451443, onlineIssueDateStr=2026-01-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769501451443, creator=13041195026, updateTime=1769501451443, updator=13041195026, issue=Issue{id=1222940980886626474, tenantId=1146029695717560320, journalId=1205116964453384197, year='2023', volume='41', issue='11', pageStart='1', pageEnd='226', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769501450987, creator=13041195026, updateTime=1769501450987, updator=13041195026, preIssue=null, nextIssue=null, ext=null, issueFiles=null}, startPage=77, endPage=81, ext={EN=ArticleExt(id=1222940984174960848, articleId=1222940982795034818, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Multi-Level Data Processing-Based NGO-XGBoost Model for Dam Deformation Prediction, columnId=1222925280734335368, journalTitle=Water Resources and Power, columnName=DAM SAFETY AND MONITORING, runingTitle=null, highlight=null, articleAbstract=

The noise and nonlinear characteristics in the deformation sequence of concrete dam seriously affect the accuracy of dam deformation prediction. In this paper, ensemble empirical modal decomposition (EEMD) was used to decompose the horizontal displacement signal of the dam to mine the effective deformation information. The singular spectrum analysis (SSA) was used to extract features from the high-frequency eigenmodal components (IMF) obtained from the decomposition to reduce the loss of effective information. Considering the complex stochastic and non-linear mapping relationship between effector and environmental variables, extreme gradient boosting (XGBoost) was used to model the prediction of the noise-reduced data. Considering the significant influence of XGBoost hyperparameters on the prediction performance of the model, the Northern Goshawk algorithm (NGO) with better global search capability was introduced to perform parameter search, and an NGO-XGBoost-based dam displacement prediction model was constructed. The calculation results show that the EEMD-SSA can effectively remove the noise from the dam displacement monitoring information, and the dam deformation prediction model based on NGO-XGBoost can significantly improve the prediction accuracy.

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混凝土坝变形序列中存在的噪声和非线性特征严重影响了大坝变形预测的精度。为此，采用集合经验模态分解（EEMD）对坝体水平位移信号进行分解，挖掘其中有效变形信息；并利用奇异谱分析（SSA）对分解所得高频本征模态分量（IMF）进行特征提取，以减少有效信息的丢失。考虑到效应量与环境量之间复杂的随机性和非线性映射关系，采用极限梯度提升（XGBoost）对降噪后的数据建模预测；考虑到XGBoost超参数对模型预测性能的显著影响，引入全局搜索能力较好的北方苍鹰算法（NGO）对其参数寻优，构建了基于NGO-XGBoost的大坝位移预测模型。计算结果表明，EEMD-SSA能有效地去除大坝位移监测信息中的噪声，NGO-XGBoost模型显著提高了大坝位移预测模型的精度。

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^b.State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1222940985638773015, tenantId=1146029695717560320, journalId=1205116964453384197, articleId=1222940982795034818, authorId=1222940985395503363, language=CN, stringName=李晨阳, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^{a, b}, address=^a.河海大学水利水电学院，江苏 南京 210098
^b.河海大学水文水资源与水利工程科学国家重点实验室，江苏 南京 210098, bio={"content":"

李晨阳（1999-），男，硕士研究生，研究方向为水工结构安全监测，E-mail：353831849@qq.com

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李晨阳（1999-），男，硕士研究生，研究方向为水工结构安全监测，E-mail：353831849@qq.com

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