Article(id=1321893337188127465, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893331622286028, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1751385600000, receivedDateStr=2025-07-02, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793093531176, onlineDateStr=2026-10-27, pubDate=1784908800000, pubDateStr=2026-07-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793093531176, onlineIssueDateStr=2026-10-27, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793093531176, creator=13701087609, updateTime=1793093531176, updator=13701087609, issue=Issue{id=1321893331622286028, tenantId=1146029695717560320, journalId=1225396423026438145, year='2026', volume='46', issue='7', pageStart='1', pageEnd='320', issueExtLink='null', onlineDate='null', pubDate='1784908800000', pubDateStr='2026-07-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793093529849, creator='13701087609', updateTime=1793094587578, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321897768357048353, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893331622286028, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321897768357048354, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1321893331622286028, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=97, endPage=107, ext={EN=ArticleExt(id=1321893337544643307, articleId=1321893337188127465, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Multi-Feature Prediction of Water Inflow in Mine Working Face Based on Deep Learning Model, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

During the mining process of the mine working face, the disturbance of the aquifer can easily lead to an increase in the water inflow of the working face, which threatens the safe production of the mine. Accurate prediction of the water inflow of the working face can effectively prevent water disaster accidents. In this study, the multi-feature prediction method of water inflow in working face based on deep learning model was studied. Combined with the characteristics of water inflow data and the actual situation of the site, the "double aquifer water level+maximum height of microseismic event" was selected as the characteristic data, and the multi-feature prediction model of water inflow in working face of mine based on VMD-iCHOA-GRU was established. At the same time, the prediction effect of multi-feature model was further verified under different feature combinations, single feature conditions and different working face data. The results show that the evaluation indexes MAE, RMSE and MAPE of the water inflow prediction results of the VMD-iCHOA-GRU model are 53.56 m³/d, 62.98 m³/d and 3.1%, respectively, which are better than other control models, and its prediction accuracy is the highest. The prediction accuracy of the multi-feature model is higher than that of the single-factor model and other single-feature and double-feature models in various combinations, and the stronger the correlation between the feature data and the research target, the more conducive it is to improving the prediction accuracy. The prediction effect of the model is good under different working face data, which verifies that the model has relative stability and certain scalability. The research results provide new ideas and new methods for the prediction of water inflow in mine working faces, and have scientific guiding significance and reference value for mine water prevention and control.

, authors=Chengxiang Zhang¹, Kun Zhang², Wenfeng Tan¹, Mingjin Fan³, Juntao Chen^{3, 4, 5}, Zhuowen Wei⁶, authorsList=Chengxiang Zhang, Kun Zhang, Wenfeng Tan, Mingjin Fan, Juntao Chen, Zhuowen Wei, authorCompany=null, correspAuthors=Mingjin Fan, 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=1321893338014405356, articleId=1321893337188127465, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=基于深度学习模型的矿井回采工作面涌水量多特征预测, columnId=1226462295417598973, journalTitle=矿业研究与开发, columnName=矿山安全与环保, runingTitle=null, highlight=null, articleAbstract=

矿井工作面回采过程中对含水层产生扰动后易导致工作面涌水量增大，威胁矿井安全生产，对工作面涌水量进行准确预测，可以有效防止水害事故发生。对基于深度学习模型的工作面涌水量多特征预测方法进行了研究，结合涌水量数据特点及现场实际情况，选择“双含水层水位+微震事件最大发生高度”作为特征数据，建立基于VMD-iCHOA-GRU的矿井回采工作面涌水量多特征预测模型，同时在不同数量特征组合下、单目标情况下和不同工作面数据下进一步验证多特征模型的预测效果。结果表明：VMD-iCHOA-GRU模型的涌水量预测结果的评价指标MAE、RMSE、MAPE分别为53.56 m³/d、62.98 m³/d、3.1%，均优于其他对照模型，其预测精度最高；多特征模型的预测精度均高于单因素模型和其他各类组合的单特征、双特征模型，且特征数据与研究目标间的相关性越强，越有利于提高预测精度；在不同工作面数据下该模型的预测效果均较好，验证了该模型具有相对稳定性和一定扩展性。研究成果为矿井回采工作面涌水量预测提供了新思路和新方法，对矿山防治水工作具有科学的指导意义和参考价值。

, authors=张呈祥¹, 张坤², 谭文峰¹, 范铭今³, 陈军涛^{3, 4, 5}, 韦卓汶⁶, authorsList=张呈祥, 张坤, 谭文峰, 范铭今, 陈军涛, 韦卓汶, authorCompany=null, correspAuthors=范铭今, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=r2hkRGl74RnTcE3SuD1P2g==, magXml=WxGhGtjWLJeIhn9n6l2uDQ==, pdfUrl=null, pdf=/v6KFwnWWZx+BDSqcZGMUQ==, pdfFileSize=2599795, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=Npe5OliXid34+tj2Zbi4KA==, mapNumber=null, fund=null)}, authors=[Author(id=1321893340992361221, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893337188127465, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=247553895@qq.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1321893341097218825, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893337188127465, authorId=1321893340992361221, language=EN, stringName=Chengxiang Zhang, firstName=Chengxiang, middleName=null, lastName=Zhang, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1.Shandong Energy Group Xibei Mining Co., Ltd., Xi'an, Shaanxi 710018, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1321893341172716298, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1321893337188127465, authorId=1321893340992361221, language=CN, stringName=张呈祥, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=^1．山东能源集团西北矿业有限公司，陕西 西安　710018, bio={"content":"

张呈祥（1988—），男，山东临沂人，高级工程师，主要从事矿井水害防治工作。E-mail:247553895@qq.com

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张呈祥（1988—），男，山东临沂人，高级工程师，主要从事矿井水害防治工作。E-mail:247553895@qq.com

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^4．煤炭资源高效开采与洁净利用国家重点实验室，北京　100013
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