Article(id=1281608167989690979, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608117335068953, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2026.03.1821, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1760371200000, receivedDateStr=2025-10-14, revisedDate=1767456000000, revisedDateStr=2026-01-04, acceptedDate=null, acceptedDateStr=null, onlineDate=1783488798377, onlineDateStr=2026-07-08, pubDate=1774627200000, pubDateStr=2026-03-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783488798377, onlineIssueDateStr=2026-07-08, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783488798377, creator=13701087609, updateTime=1783488798377, updator=13701087609, issue=Issue{id=1281608117335068953, tenantId=1146029695717560320, journalId=1146031787341344770, year='2026', volume='36', issue='3', pageStart='1', pageEnd='270', issueExtLink='null', onlineDate='null', pubDate='1774627200000', pubDateStr='2026-03-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783488786300, creator='13701087609', updateTime=1783588670896, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282027063758533259, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608117335068953, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282027063758533260, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1281608117335068953, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=144, endPage=152, ext={EN=ArticleExt(id=1281608168220377700, articleId=1281608167989690979, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Prediction method of support load in coal mining face based on MTAM-LSTM, columnId=1277328337617941059, journalTitle=China Safety Science Journal, columnName=Safety Technology and Engineering, runingTitle=null, highlight=null, articleAbstract=

In order to effectively predict hydraulic support loads and evaluate the operational status of supports, a hydraulic support load prediction model based on MTAM-LSTM was proposed. The CEEMDAN algorithm was employed to decompose the load data of supports and extract intrinsic mode functions. Redundant components in the intrinsic mode functions were eliminated according to K-L divergence criterion, thereby forming the input sequence for load prediction. An MTAM was constructed to capture the variation characteristics of hydraulic support loads. Static attention generated attention weights for feature information of data, while dynamic attention optimized the focus on different sequence features. Residual learning was introduced to maintain the integrity of feature signals. LSTM networks were then utilized to establish deep dependencies between feature information and hydraulic support loads, enabling advanced prediction of support load data. Field data from the 402102 working face of a rockburst-prone coal mine in Shaanxi were used for empirical validation. RMSE, R², and MAE were used as evaluation metrics for comparison among different models. The results show that the RMSE and MAE of the MTAM-LSTM model are significantly lower than those of the comparison models, with RMSE reduced by 0.16-0.45 and MAE reduced by 0.16-0.45, while the coefficient of determination R² reaches 0.91 under different scenarios, thereby validating the prediction accuracy and generalization capability of MTAM-LSTM model.

, authors=Jie ZHANG^{1, 2}, Ke YANG^{1, 2}, Chaochen FAN^{1, 2}, authorsList=Jie ZHANG, Ke YANG, Chaochen FAN, authorCompany=null, correspAuthors=null, 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=1281608168455258725, articleId=1281608167989690979, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于MTAM-LSTM的采煤工作面支架载荷预测方法, columnId=1277328337940902469, journalTitle=中国安全科学学报, columnName=安全技术与工程, runingTitle=null, highlight=null, articleAbstract=

为有效预测液压支架载荷、评估支架运行状态，提出一种基于多尺度卷积时间注意力模块（MTAM）-长短时记忆（LSTM）神经网络的液压支架载荷预测模型。首先，采用自适应噪声完备集合经验模态分解（CEEMDAN）算法分解支架载荷数据获取本征模态分量，基于K-L散度准则剔除本征模态分量中的冗余分量形成支架载荷预测输入序列；其次，建立MTAM捕捉支架载荷变化特征，静态注意力生成数据特征信息的注意力权重，动态注意力优化不同序列特征的关注度，并引入残差学习保持特征信号的完整性；然后，利用LSTM构建特征信息与支架载荷之间的深层依赖关系，实现支架载荷数据的超前预测；最后，选取陕西某冲击地压矿井402102工作面液压支架载荷数据进行实证分析，对比不同模型均方根误差、决定系数和平均绝对误差3种评价指标，结果表明：MTAM-LSTM模型的均方根误差（RMSE）和平均绝对误差（MAE）均明显小于对比模型，RMSE整体降低0.16~0.45，MAE降低0.16~0.45，不同场景下决定系数R²达到0.91，验证了MTAM-LSTM的预测准确率和模型泛化能力。

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²School of Mining Engineering, Anhui University of Science and Technology, Huainan Anhui 232001, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1281608170527244913, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1281608167989690979, authorId=1281608170363667053, language=CN, stringName=张杰, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=^{1, 2}, address=¹安徽理工大学 深部煤炭安全开采与环境保护全国重点实验室，安徽 淮南 232001
²安徽理工大学 矿业工程学院，安徽 淮南 232001, bio={"content":"

张杰（1995—），男，安徽六安人，博士研究生，主要研究方向为煤岩动力灾害预测预警。E-mail: jiezhang617@163.com。

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张杰（1995—），男，安徽六安人，博士研究生，主要研究方向为煤岩动力灾害预测预警。E-mail: jiezhang617@163.com。

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