Article(id=1282029975549219803, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282029960869167928, articleNumber=null, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2025.07.1486, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1741536000000, receivedDateStr=2025-03-10, revisedDate=1747324800000, revisedDateStr=2025-05-16, acceptedDate=null, acceptedDateStr=null, onlineDate=1783589365136, onlineDateStr=2026-07-09, pubDate=1753632000000, pubDateStr=2025-07-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1783589365136, onlineIssueDateStr=2026-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1783589365136, creator=13701087609, updateTime=1783589365136, updator=13701087609, issue=Issue{id=1282029960869167928, tenantId=1146029695717560320, journalId=1146031787341344770, year='2025', volume='35', issue='7', pageStart='1', pageEnd='252', issueExtLink='null', onlineDate='null', pubDate='1753632000000', pubDateStr='2025-07-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1783589361636, creator='13701087609', updateTime=1783589710614, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1282031424651903975, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282029960869167928, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1282031424656098280, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1282029960869167928, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=40, endPage=47, ext={EN=ArticleExt(id=1282029975805072348, articleId=1282029975549219803, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Prediction model and interpretability analysis of wind temperature in mine water-drenched shaft based on KOA-BiLSTM, columnId=1149733269173878863, journalTitle=China Safety Science Journal, columnName=Safety engineering technology, runingTitle=null, highlight=null, articleAbstract=
This study aims to improve the accuracy, stability and interpretability of the model for the prediction of the air temperature in the mine water-drenched shaft. Firstly, characteristic variables were analyzed by Pearson correlation coefficient. Secondly, BiLSTM model was optimized by KOA, and the prediction model of mine shaft air temperature based on KOA-BiLSTM was established. Then, under the same sample conditions, the algorithm was compared with back propagation (BP), random forest (RF), least squares boosting (LSBoost) and support vector machine (SVM). Finally, interpretability analysis was conducted using the shapley additive explanations (SHAP) algorithm, which was verified by an example. The results show that the absolute error range of KOA-BiLSTM model is -1.24-0.5 ℃, which is 3.98% higher than the prediction accuracy of the unoptimized model. Compared with the other four models, the average absolute error (MAE), average absolute percentage error (MAPE) and mean square error (MSE) of the proposed model are the smallest, indicating that the model has the best prediction effect and generalization ability. The SHAP analysis shows that the wellhead air flow temperature has the greatest impact on the prediction results, while the surface pressure has the least impact. The absolute error range of KOA-BiLSTM model example verification is -0.49~0.38 ℃, and the prediction accuracy can meet the work needs.
, authors=Yueping QIN, Fei TANG
**, Hairong WANG, Peng WANG, Mingyan GUO, Shibin WANG, authorsList=Yueping QIN, Fei TANG, Hairong WANG, Peng WANG, Mingyan GUO, Shibin WANG, authorCompany=null, correspAuthors=Fei TANG, 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=1282029976274834399, articleId=1282029975549219803, tenantId=1146029695717560320, journalId=1146031787341344770, language=CN, title=基于KOA-BiLSTM的矿井淋水井筒风温预测模型及可解释性分析, columnId=1149733269727526997, journalTitle=中国安全科学学报, columnName=安全工程技术, runingTitle=null, highlight=null, articleAbstract=
为提高矿井淋水井筒风温预测的准确性、稳定性及模型的可解释性,首先,通过皮尔逊相关性系数分析特征变量;其次,采用开普勒优化算法(KOA)优化双向长短期记忆网络(BiLSTM)模型,建立基于KOA-BiLSTM的矿井淋水井筒风温预测模型;然后,在相同样本条件下,与反向传播(BP)、随机森林(RF)、最小二乘增强(LSBoost)和支持向量机(SVM)算法进行综合对比;最后,利用沙普利可加性特征解释算法(SHAP)进行可解释性分析及实例验证。研究结果表明:KOA-BiLSTM模型的绝对误差范围为-1.24 ~0.5 ℃,比优化前模型的预测精度提高3.98%;与另外4个模型相比,该模型的平均绝对误差(MAE)、平均绝对百分比误差(MAPE)和均方误差(MSE)等均为最佳,表明该模型具有最优的预测效果和泛化能力;SHAP分析表明:井口风流温度对预测结果影响最大,而地面压力影响最小;KOA-BiLSTM模型实例验证的绝对误差范围为-0.49 ~0.38 ℃,预测精度可满足实际工作需要。
, authors=秦跃平
教授, 唐飞
**, 王海蓉, 王鹏, 郭铭彦, 王世斌, authorsList=秦跃平
教授, 唐飞, 王海蓉, 王鹏, 郭铭彦, 王世斌, authorCompany=null, correspAuthors=唐飞, authorNote=
秦跃平(1964—),男,山西夏县人,博士,教授,主要从事矿井火灾及瓦斯灾害防治、矿井热害防治以及数值模拟方法等方面的研究。E-mail: qyp@cumtb.edu.cn。
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秦跃平(1964—),男,山西夏县人,博士,教授,主要从事矿井火灾及瓦斯灾害防治、矿井热害防治以及数值模拟方法等方面的研究。E-mail: qyp@cumtb.edu.cn。
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秦跃平(1964—),男,山西夏县人,博士,教授,主要从事矿井火灾及瓦斯灾害防治、矿井热害防治以及数值模拟方法等方面的研究。E-mail: qyp@cumtb.edu.cn。
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