Article(id=1282037431792812438, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282037390130790473, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2025.12.0676, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1752249600000, receivedDateStr=2025-07-12, revisedDate=1760976000000, revisedDateStr=2025-10-21, acceptedDate=null, acceptedDateStr=null, onlineDate=1783591142842, onlineDateStr=2026-07-09, pubDate=1766851200000, pubDateStr=2025-12-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783591142842, onlineIssueDateStr=2026-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783591142842, creator=13701087609, updateTime=1783591142842, updator=13701087609, issue=Issue{id=1282037390130790473, tenantId=1146029695717560320, journalId=1146031787341344770, year='2025', volume='35', issue='12', pageStart='1', pageEnd='259', issueExtLink='null', onlineDate='null', pubDate='1766851200000', pubDateStr='2025-12-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783591132910, creator='13701087609', updateTime=1783593783109, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282048505929777979, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282037390130790473, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282048505929777980, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282037390130790473, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=154, endPage=163, ext={EN=ArticleExt(id=1282037432002527639, articleId=1282037431792812438, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Time-series prediction model for leakage dispersion of buried high-pressure large-diameter gas pipelines, columnId=1149733269173878863, journalTitle=China Safety Science Journal, columnName=Safety engineering technology, runingTitle=null, highlight=null, articleAbstract=

In order to address the challenges of unclear spatial concentration distribution and uncertain future evolution in high-pressure large-diameter gas pipeline leakage scenarios, a predictive model for gas leakage dispersion was proposed by integrating machine learning-based dimensionality reduction and time series forecasting methods. Firstly, a multi-condition dataset of gas leakage concentration fields was generated using computational fluid dynamics simulations. Subsequently, the dimensionality reduction module and time series forecasting module of the predictive model were separately optimized and trained using this dataset. Finally, the model's predictive accuracy was evaluated on an independent test set, and the prediction errors under various forecast horizons were analyzed. The results show that the model achieves a mean absolute error of 0.000 5 and a mean absolute percentage error (mAPE) of 6.82% on the test set, with the mAPE remaining below 14% across different prediction time steps.

, authors=Ziqi HAN¹, Tong XU^{**, 1, 2}, Jitao CAI¹, Siqi ZHANG¹, Li ZHANG¹, authorsList=Ziqi HAN, Tong XU, Jitao CAI, Siqi ZHANG, Li ZHANG, authorCompany=null, correspAuthors=Tong XU, 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=1282037432551981466, articleId=1282037431792812438, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=地下高压大口径输气管道泄漏扩散时序预测模型, columnId=1149733269727526997, journalTitle=中国安全科学学报, columnName=安全工程技术, runingTitle=null, highlight=null, articleAbstract=

为解决地下高压大口径输气管道泄漏后泄漏区域空间浓度信息不明、事故未来态势不清等问题，融合机器学习降维与时序预测方法，构建高压大口径输气管道泄漏扩散预测模型。首先，基于计算流体动力学方法，构建多工况输气管道泄漏扩散浓度场数据集；其次，利用该数据集分别对预测模型中降维模块与时序预测模块进行超参数优化及训练；最后，基于独立测试集，评估高压大口径输气管道泄漏扩散预测模型预测精度，并分析不同预测时长下预测模型误差。结果表明：预测模型在测试集上平均绝对误差为0.000 5，平均绝对百分比误差为6.82%，且平均绝对百分比误差在多种预测时间步长下均低于14%。

, authors=韩子齐¹, 徐童讲师^{**, 1, 2}, 蔡继涛¹, 张思琪¹, 张力¹, authorsList=韩子齐, 徐童讲师, 蔡继涛, 张思琪, 张力, authorCompany=null, correspAuthors=徐童讲师, authorNote=, correspAuthorsNote=, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=XUcx1Butfe/RpORJr6146A==, magXml=Fnsg8FadaSMo1kk2jFWFqg==, pdfUrl=null, pdf=UjfIVsqFAy9ApnYO/nkMSw==, pdfFileSize=9257915, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=cS8YtgZPcRqKhyKaKQQsZg==, mapNumber=null, fund=null)}, authors=[Author(id=1282037432963023266, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282037431792812438, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=ziqihan0610@163.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1282037433038520740, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282037431792812438, authorId=1282037432963023266, language=EN, stringName=Ziqi HAN, firstName=Ziqi, middleName=null, lastName=HAN, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹School of Emergency Management and Safety Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1282037433118212517, tenantId=1146029695717560320, journalId=1146031787341344770, articleId=1282037431792812438, authorId=1282037432963023266, language=CN, stringName=韩子齐, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=¹, address=¹中国矿业大学（北京） 应急管理与安全工程学院，北京 100083, bio={"content":"

韩子齐（2001—），男，河北沧州人，硕士研究生，研究方向为油气管道安全、AI数据驱动模型。E-mail:ziqihan0610@163.com。

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韩子齐（2001—），男，河北沧州人，硕士研究生，研究方向为油气管道安全、AI数据驱动模型。E-mail:ziqihan0610@163.com。

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