Article(id=1311351230371299753, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1311351152067830064, articleNumber=null, orderNo=null, doi=10.20040/j.cnki.1000-7709.2026.20250085, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1736524800000, receivedDateStr=2025-01-11, revisedDate=1742054400000, revisedDateStr=2025-03-16, acceptedDate=1742832000000, acceptedDateStr=2025-03-25, onlineDate=1790580097048, onlineDateStr=2026-09-28, pubDate=1769270400000, pubDateStr=2026-01-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1790580097048, onlineIssueDateStr=2026-09-28, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1790580097048, creator=13701087609, updateTime=1790580097048, updator=13701087609, issue=Issue{id=1311351152067830064, tenantId=1146029695717560320, journalId=1205116964453384197, year='2026', volume='44', issue='1', pageStart='1', pageEnd='246', issueExtLink='null', onlineDate='null', pubDate='1769270400000', pubDateStr='2026-01-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=1, specialIssue=null, createTime=1790580078378, creator='13701087609', updateTime=1790588459421, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1311386304772927717, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1311351152067830064, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1311386304772927718, tenantId=1146029695717560320, journalId=1205116964453384197, issueId=1311351152067830064, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=177, endPage=181, ext={EN=ArticleExt(id=1311351230547460522, articleId=1311351230371299753, tenantId=1146029695717560320, journalId=1205116964453384197, language=EN, title=Study on Time Series Prediction of Slope Deformation in Reservoir Area Based on SSA-BP Neural Network, columnId=null, journalTitle=Water Resources and Power, columnName=null, runingTitle=null, highlight=null, articleAbstract=

An improved hybrid prediction model SSA-BP based on intelligent optimization algorithms was developed to address the complex nonlinearities and uncertainties inherent in slope displacement prediction in reservoir areas. This methodology effectively overcomes the limitations of the traditional BP networks, such as slow training speed and susceptibility to local optimum so as to improve the accuracy and robustness of slope displacement prediction. Through the global optimization of initial weights and thresholds using the Sparrow Search Algorithm (SSA), convergence and adaptability of the model was improved. The proposed model was trained using five-month real-time displacement monitoring data obtained from the Zhangjiawan slope. A comparative performance analysis was conducted between the SSA-BP model and BP networks optimized by genetic algorithm (GA) and particle swarm optimization (PSO). The results demonstrate that the SSA-BP model achieves rapid convergence within 24 iterations, significantly outperforming the comparative models. Evaluation metrics including root mean square error, mean absolute percentage error , and coefficient of determination (R²) reveal superior performance of the SSA-BP model. This research presents a reliable and efficient intelligent approach for accurate and robust slope displacement prediction in reservoir area.

, authors=Yi-min WU, Cheng-liang ZHANG, Huan-xiong ZHANG, authorsList=Yi-min WU, Cheng-liang ZHANG, Huan-xiong ZHANG, authorCompany=null, correspAuthors=Cheng-liang ZHANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1311351230845256107, articleId=1311351230371299753, tenantId=1146029695717560320, journalId=1205116964453384197, language=CN, title=基于SSA-BP神经网络的库区边坡变形时序预测研究, columnId=1207271181661589640, journalTitle=水电能源科学, columnName=水利枢纽、水利建筑物, runingTitle=null, highlight=null, articleAbstract=

针对库区边坡位移预测中存在的复杂非线性及不确定性难题，构建了一种基于智能优化算法的混合预测模型SSA-BP，旨在克服传统BP网络训练速度慢、易陷入局部最优的局限，从而提升边坡位移预测的精度和鲁棒性。通过麻雀搜索算法SSA对BP神经网络的初始权值和阈值进行全局优化，增强其收敛效率和适应性，并基于张家湾边坡历时5个月的真实位移监测数据进行训练。为验证模型优势，将SSA-BP模型与基于遗传算法（GA）和粒子群算法（PSO）优化的BP网络进行性能比对。研究表明，模型在24次迭代内快速收敛，显著优于对比模型，其均方根误差（R_RMSE）、平均绝对百分比误差（M_MAPE）、决定系数（R²）等评价指标均表现最佳。SSA-BP模型为库区边坡位移预测提供了一种可靠且高效的智能方法。

, authors=武益民, 张成良, 张焕雄, authorsList=武益民, 张成良, 张焕雄, authorCompany=null, correspAuthors=张成良, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=x6tjg/arPP+HDkmp2HsDGA==, magXml=SKEnZPfr2q2CXqgdkd59wQ==, pdfUrl=null, pdf=zeP8olSL5pUng6UNDF2v8g==, pdfFileSize=2102796, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=KIkNfJ4yYEBe+2NGAxwU4w==, mapNumber=null, fund=null)}, authors=[Author(id=1311351234502689201, tenantId=1146029695717560320, journalId=1205116964453384197, articleId=1311351230371299753, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=759041030@qq.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1311351234586575283, tenantId=1146029695717560320, journalId=1205116964453384197, articleId=1311351230371299753, authorId=1311351234502689201, language=EN, stringName=Yi-min WU, firstName=Yi-min, middleName=null, lastName=WU, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1311351234674655668, tenantId=1146029695717560320, journalId=1205116964453384197, articleId=1311351230371299753, authorId=1311351234502689201, language=CN, stringName=武益民, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=昆明理工大学国土资源工程学院，云南 昆明 650093, bio={"content":"

武益民（2000-），男，硕士研究生，研究方向为水库边坡稳定性，E-mail: 759041030@qq.com

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武益民（2000-），男，硕士研究生，研究方向为水库边坡稳定性，E-mail: 759041030@qq.com

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