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Article(id=1322907584386179087, tenantId=1146029695717560320, journalId=1225396423026438145, issueId=1322907425740825534, 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=1723392000000, receivedDateStr=2024-08-12, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793335346551, onlineDateStr=2026-10-30, pubDate=1750780800000, pubDateStr=2025-06-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793335346551, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793335346551, creator=13701087609, updateTime=1793335346551, updator=13701087609, issue=Issue{id=1322907425740825534, tenantId=1146029695717560320, journalId=1225396423026438145, year='2025', volume='45', issue='6', pageStart='1', pageEnd='300', issueExtLink='null', onlineDate='null', pubDate='1750780800000', pubDateStr='2025-06-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793335308726, creator='13701087609', updateTime=1793335308726, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=173, endPage=181, ext={EN=ArticleExt(id=1322907586185535504, articleId=1322907584386179087, tenantId=1146029695717560320, journalId=1225396423026438145, language=EN, title=Research on Dust Concentration Prediction Model for Underground Mine Based on NRBO-RF, columnId=null, journalTitle=Mining Research and Development, columnName=null, runingTitle=null, highlight=null, articleAbstract=

In order to realize the accurate prediction of mine dust concentration, based on the field monitoring data of a mining working face in a coal mine in Shaanxi Province, the random forest (RF) model was used to construct a multi-factor dust concentration prediction model by taking temperature, humidity, wind speed and dust concentration at the current moment as input vectors. Four intelligent optimization algorithms (Newton-Raphson-based optimizer (NRBO), sparrow search algorithm, grey wolf optimizer and whale optimization algorithm) were proposed to optimize the parameters of random forest (number of trees and number of leaf nodes). The model was evaluated by determination coefficient(R2), root mean square error (RMSE) and mean absolute error (MAE). The results show that the optimal model for dust concentration prediction is NRBO-RF model. Compared with RF model, the R2, RMSE and MAE of NRBO-RF model are increased by 0.030 3, 0.572 8 and 0.074 9, respectively, and its fitting ability and prediction accuracy are the highest. The research results can provide a reference for intelligent prediction and prevention and control of mine dust.

, authors=Fuming GUO1, Shanshan LI2, Chunshan ZHENG1, authorsList=Fuming GUO, Shanshan LI, Chunshan ZHENG, authorCompany=null, correspAuthors=Shanshan LI, 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=1322907593336823889, articleId=1322907584386179087, tenantId=1146029695717560320, journalId=1225396423026438145, language=CN, title=基于NRBO-RF的井下粉尘浓度预测模型研究, columnId=1226462295417598973, journalTitle=矿业研究与开发, columnName=矿山安全与环保, runingTitle=null, highlight=null, articleAbstract=

为实现矿井粉尘浓度的准确预测，利用陕西某矿井采煤工作面的现场监测数据，采用随机森林(RF)模型，将温度、湿度、风速和当前时刻的粉尘浓度作为输入向量，构建多因素粉尘浓度预测模型；并提出使用4种智能优化算法(牛顿-拉夫逊优化算法(NRBO)、麻雀搜索算法、灰狼优化算法和鲸鱼优化算法)对随机森林的参数(树的数量和叶子节点数)进行寻优，通过决定系数(R2)、均方根误差(RMSE)和平均绝对误差(MAE)评价模型的优劣。结果表明：粉尘浓度预测的最优模型是NRBO-RF模型；与RF模型相比，NRBO-RF模型的R2提高了0.030 3, RMSE降低了0.572 8, MAE降低了0.074 9，其拟合能力和预测精度最高。研究结果可为矿井粉尘的智能预测及防控提供参考。

, authors=郭福明1, 李姗姗2, 郑春山1, authorsList=郭福明, 李姗姗, 郑春山, authorCompany=null, correspAuthors=李姗姗, authorNote=

郭福明（2000—），女，河南周口人，硕士研究生，主要从事粉尘防控与职业健康管理研究。E-mail:

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李姗姗（1992—），女，河北保定人，博士，副教授，主要从事安全管理心理与行为、环境治理理论与方法、“双碳”管理与政策等方面的研究。E-mail:
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郭福明（2000—），女，河南周口人，硕士研究生，主要从事粉尘防控与职业健康管理研究。E-mail:

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郭福明（2000—），女，河南周口人，硕士研究生，主要从事粉尘防控与职业健康管理研究。E-mail:

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url=null, language=null, rfNumber=[1], rfOrder=0, authorNames=袁亮, journalName=中国煤炭, refType=null, unstructuredReference=袁亮.我国煤炭工业高质量发展面临的挑战与对策[J].中国煤炭，2020，46(1):6-12., articleTitle=我国煤炭工业高质量发展面临的挑战与对策, refAbstract=null), Reference(id=1322907597937975436, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2020, volume=46, issue=1, pageStart=6, pageEnd=12, url=null, language=null, rfNumber=[1], rfOrder=1, authorNames=YUAN Liang, journalName=China Coal, refType=null, unstructuredReference=YUAN Liang. Challenges and countermeasures for high quality development of China's coal industry[J].China Coal, 2020, 46(1):6-12., articleTitle=Challenges and countermeasures for high quality development of China's coal industry, refAbstract=null), Reference(id=1322907598030250125, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2022, volume=47, issue=1, pageStart=1, pageEnd=15, url=null, language=null, rfNumber=[2], rfOrder=2, authorNames=刘峰, 郭林峰, 赵路正, journalName=煤炭学报, refType=null, unstructuredReference=刘峰，郭林峰，赵路正.双碳背景下煤炭安全区间与绿色低碳技术路径[J].煤炭学报，2022，47(1):1-15., articleTitle=双碳背景下煤炭安全区间与绿色低碳技术路径, refAbstract=null), Reference(id=1322907598130913422, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2022, volume=47, issue=1, pageStart=1, pageEnd=15, url=null, language=null, rfNumber=[2], rfOrder=3, authorNames=LIU Feng, GUO Linfeng, ZHAO Luzheng, journalName=Journal of China Coal Society, refType=null, unstructuredReference=LIU Feng, GUO Linfeng, ZHAO Luzheng. Research on coal safety range and green low-carbon technology path under the dual-carbon background[J]. Journal of China Coal Society, 2022, 47(1): 1-15., articleTitle=Research on coal safety range and green low-carbon technology path under the dual-carbon background, refAbstract=null), Reference(id=1322907598210605199, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2018, volume=43, issue=5, pageStart=1198, pageEnd=1209, url=null, language=null, rfNumber=[3], rfOrder=4, authorNames=王金华, 谢和平, 刘见中, journalName=煤炭学报, refType=null, unstructuredReference=王金华，谢和平，刘见中，等.煤炭近零生态环境影响开发利用理论和技术构想[J].煤炭学报，2018，43(5):1198-1209., articleTitle=煤炭近零生态环境影响开发利用理论和技术构想, refAbstract=null), Reference(id=1322907598277714064, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2018, volume=43, issue=5, pageStart=1198, pageEnd=1209, url=null, language=null, rfNumber=[3], rfOrder=5, authorNames=WANG Jinhua, XIE Heping, LIU Jianzhong, journalName=Journal of China Coal Society, refType=null, unstructuredReference=WANG Jinhua, XIE Heping, LIU Jianzhong, et al. Coal development and utilization theory and technical system of near-zero ecological environment impact[J]. Journal of China Coal Society, 2018, 43(5):1198-1209., articleTitle=Coal development and utilization theory and technical system of near-zero ecological environment impact, refAbstract=null), Reference(id=1322907598344822929, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2019, volume=46, issue=6, pageStart=1, pageEnd=7+13, url=null, language=null, rfNumber=[4], rfOrder=6, authorNames=李德文, 隋金君, 刘国庆, journalName=矿业安全与环保, refType=null, unstructuredReference=李德文，隋金君，刘国庆，等.中国煤矿粉尘危害防治技术现状及发展方向[J].矿业安全与环保，2019，46(6):1-7+13., articleTitle=中国煤矿粉尘危害防治技术现状及发展方向, refAbstract=null), Reference(id=1322907598416126098, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2019, volume=46, issue=6, pageStart=1, pageEnd=7+13, url=null, language=null, rfNumber=[4], rfOrder=7, authorNames=LI Dewen, SUI Jinjun, LIU Guoqing, journalName=Mining Safety & Environmental Protection, refType=null, unstructuredReference=LI Dewen, SUI Jinjun, LIU Guoqing, et al. Technical status and development direction of coal mine dust hazard prevention and control technology in China[J]. Mining Safety & Environmental Protection, 2019, 46(6): 1-7+13., articleTitle=Technical status and development direction of coal mine dust hazard prevention and control technology in China, refAbstract=null), Reference(id=1322907598479040659, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2021, volume=null, issue=1, pageStart=135, pageEnd=153, url=null, language=null, rfNumber=[5], rfOrder=8, authorNames=蒋仲安, 曾发镔, 王亚朋, journalName=金属矿山, refType=null, unstructuredReference=蒋仲安，曾发镔，王亚朋.我国金属矿山采运过程典型作业场所粉尘污染控制研究现状与展望[J].金属矿山，2021(1): 135-153., articleTitle=我国金属矿山采运过程典型作业场所粉尘污染控制研究现状与展望, refAbstract=null), Reference(id=1322907598537760916, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2021, volume=null, issue=1, pageStart=135, pageEnd=153, url=null, language=null, rfNumber=[5], rfOrder=9, authorNames=JIANG Zhong'an, ZENG Fabin, WANG Yapeng, journalName=Metal Mine, refType=null, unstructuredReference=JIANG Zhong'an, ZENG Fabin, WANG Yapeng. Research status and prospect of dust pollution control in typical working places during mining and transportation of metal mines in China[J]. Metal Mine, 2021(1):135-153., articleTitle=Research status and prospect of dust pollution control in typical working places during mining and transportation of metal mines in China, refAbstract=null), Reference(id=1322907598604869781, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2024, volume=49, issue=7, pageStart=2973, pageEnd=2985, url=null, language=null, rfNumber=[6], rfOrder=10, authorNames=江丙友, 王一凡, 林汉毅, journalName=煤炭学报, refType=null, unstructuredReference=江丙友，王一凡，林汉毅，等.煤矿综掘工作面长压短抽除尘性能与尘源移动路径关系研究[J].煤炭学报，2024，49（7）：2973-2985., articleTitle=煤矿综掘工作面长压短抽除尘性能与尘源移动路径关系研究, refAbstract=null), Reference(id=1322907598671978646, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2024, volume=49, issue=7, pageStart=2973, pageEnd=2985, url=null, language=null, rfNumber=[6], rfOrder=11, authorNames=JIANG Bingyou, WANG Yifan, LIN Hanyi, journalName=Journal of China Coal Society, refType=null, unstructuredReference=JIANG Bingyou, WANG Yifan, LIN Hanyi, et al. Relationship between long pressure short extraction dust removal performance and dust source movement path in comprehensive heading face of coal mines[J]. Journal of China Coal Society, 2024, 49(7):2973-2985., articleTitle=Relationship between long pressure short extraction dust removal performance and dust source movement path in comprehensive heading face of coal mines, refAbstract=null), Reference(id=1322907598747476119, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2020, volume=178, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[7], rfOrder=12, authorNames=GUO L D, NIE W, YIN S, journalName=Building and Environment, refType=null, unstructuredReference=GUO L D, NIE W, YIN S, et al. The dust diffusion modeling and determination of optimal airflow rate for removing the dust generated during mine tunneling[J]. Building and Environment, 2020, 178:106846., articleTitle=The dust diffusion modeling and determination of optimal airflow rate for removing the dust generated during mine tunneling, refAbstract=null), Reference(id=1322907598827167896, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2023, volume=54, issue=11, pageStart=42, pageEnd=47, url=null, language=null, rfNumber=[8], rfOrder=13, authorNames=王德强, 滕海旭, 王明钦, journalName=煤矿安全, refType=null, unstructuredReference=王德强，滕海旭，王明钦，等.煤矿主提升井内气固两相流运动特性研究[J].煤矿安全，2023，54(11):42-47., articleTitle=煤矿主提升井内气固两相流运动特性研究, refAbstract=null), Reference(id=1322907598919442585, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2023, volume=54, issue=11, pageStart=42, pageEnd=47, url=null, language=null, rfNumber=[8], rfOrder=14, authorNames=WANG Deqiang, TENG Haixu, WANG Mingqin, journalName=Safety in Coal Mines, refType=null, unstructuredReference=WANG Deqiang, TENG Haixu, WANG Mingqin, et al. Study on movement characteristics of gas-solid two-phase flow in main lifting shaft of coal mine[J]. Safety in Coal Mines, 2023, 54(11):42-47., articleTitle=Study on movement characteristics of gas-solid two-phase flow in main lifting shaft of coal mine, refAbstract=null), Reference(id=1322907598994940058, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2024, volume=49, issue=2, pageStart=664, pageEnd=694, url=null, language=null, rfNumber=[9], rfOrder=15, authorNames=薛生, 郑晓亮, 袁亮, journalName=煤炭学报, refType=null, unstructuredReference=薛生，郑晓亮，袁亮，等.基于机器学习的煤与瓦斯突出预测研究进展及展望[J].煤炭学报，2024，49(2):664-694., articleTitle=基于机器学习的煤与瓦斯突出预测研究进展及展望, refAbstract=null), Reference(id=1322907599066243227, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2024, volume=49, issue=2, pageStart=664, pageEnd=694, url=null, language=null, rfNumber=[9], rfOrder=16, authorNames=XUE Sheng, ZHENG Xiaoliang, YUAN Liang, journalName=Journal of China Coal Society, refType=null, unstructuredReference=XUE Sheng, ZHENG Xiaoliang, YUAN Liang, et al. A review on coal and gas outburst prediction based on machine learning[J]. Journal of China Coal Society, 2024, 49(2): 664-694., articleTitle=A review on coal and gas outburst prediction based on machine learning, refAbstract=null), Reference(id=1322907599141740700, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2024, volume=44, issue=5, pageStart=98, pageEnd=105, url=null, language=null, rfNumber=[10], rfOrder=17, authorNames=乔威豪, 安葳鹏, 赵雪菡, journalName=矿业研究与开发, refType=null, unstructuredReference=乔威豪，安葳鹏，赵雪菡，等.基于改进极限学习机的煤与瓦斯突出预测研究[J].矿业研究与开发，2024，44(5):98-105., articleTitle=基于改进极限学习机的煤与瓦斯突出预测研究, refAbstract=null), Reference(id=1322907599213043869, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2024, volume=44, issue=5, pageStart=98, pageEnd=105, url=null, language=null, rfNumber=[10], rfOrder=18, authorNames=QIAO Weihao, AN Weipeng, ZHAO Xuehan, journalName=Mining Research and Development, refType=null, unstructuredReference=QIAO Weihao, AN Weipeng, ZHAO Xuehan, et al. Research on coal and gas outburst prediction based on improved extreme learning machine[J]. Mining Research and Development, 2024, 44(5):98-105., articleTitle=Research on coal and gas outburst prediction based on improved extreme learning machine, refAbstract=null), Reference(id=1322907599301124254, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2024, volume=50, issue=3, pageStart=333, pageEnd=347, url=null, language=null, rfNumber=[11], rfOrder=19, authorNames=刘希亮, 赵俊杰, 张羽民, journalName=北京工业大学学报, refType=null, unstructuredReference=刘希亮，赵俊杰，张羽民，等.基于时空认知膨胀卷积网络与多源影响因素的PM2.5细粒度预测模型[J].北京工业大学学报，2024，50(3):333-347., articleTitle=基于时空认知膨胀卷积网络与多源影响因素的PM2.5细粒度预测模型, refAbstract=null), Reference(id=1322907599393398943, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2024, volume=50, issue=3, pageStart=333, pageEnd=347, url=null, language=null, rfNumber=[11], rfOrder=20, authorNames=LIU Xiliang, ZHAO Junjie, ZHANG Yumin, journalName=Journal of Beijing University of Technology, refType=null, unstructuredReference=LIU Xiliang, ZHAO Junjie, ZHANG Yumin, et al. PM2.5fine-grained air quality prediction model based on spatial-temporal cognitive dilated convolution network and multi-source influencing factors[J]. Journal of Beijing University of Technology, 2024, 50(3):333-347., articleTitle=PM2.5fine-grained air quality prediction model based on spatial-temporal cognitive dilated convolution network and multi-source influencing factors, refAbstract=null), Reference(id=1322907599464702112, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2024, volume=16, issue=2, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[12], rfOrder=21, authorNames=CAI S J, FANG F X, TANG X, journalName=Journal of Advances in Modeling Earth Systems, refType=null, unstructuredReference=CAI S J, FANG F X, TANG X, et al. A hybrid data-driven and data assimilation method for spatiotemporal forecasting: PM2.5 forecasting in China[J]. Journal of Advances in Modeling Earth Systems, 2024, 16(2): e2023MS003789., articleTitle=A hybrid data-driven and data assimilation method for spatiotemporal forecasting: PM2.5 forecasting in China, refAbstract=null), Reference(id=1322907599523422369, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2024, volume=44, issue=2, pageStart=629, pageEnd=637, url=null, language=null, rfNumber=[13], rfOrder=22, authorNames=王子倩, 郝炜伟, 董玲池, journalName=中国环境科学, refType=null, unstructuredReference=王子倩，郝炜伟，董玲池，等.重庆大气中多环芳香类物质气粒分配模型预测[J].中国环境科学，2024，44(2):629-637., articleTitle=重庆大气中多环芳香类物质气粒分配模型预测, refAbstract=null), Reference(id=1322907599607308450, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2024, volume=44, issue=2, pageStart=629, pageEnd=637, url=null, language=null, rfNumber=[13], rfOrder=23, authorNames=WANG Ziqian, HAO Weiwei, DONG Lingchi, journalName=China Environmental Science, refType=null, unstructuredReference=WANG Ziqian, HAO Weiwei, DONG Lingchi, et al. Modeling prediction of particle distribution of polycyclic aromatic compounds in the winter atmosphere over Chongqing urban area[J]. China Environmental Science, 2024, 44(2):629-637., articleTitle=Modeling prediction of particle distribution of polycyclic aromatic compounds in the winter atmosphere over Chongqing urban area, refAbstract=null), Reference(id=1322907599674417315, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2022, volume=39, issue=5, pageStart=786, pageEnd=794, url=null, language=null, rfNumber=[14], rfOrder=24, authorNames=李天秀, 石磊, 王俊辉, journalName=量子电子学报, refType=null, unstructuredReference=李天秀，石磊，王俊辉，等.基于深度学习的量子信号大气衰减系数预测[J].量子电子学报，2022，39(5):786-794., articleTitle=基于深度学习的量子信号大气衰减系数预测, refAbstract=null), Reference(id=1322907599741526180, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2022, volume=39, issue=5, pageStart=786, pageEnd=794, url=null, language=null, rfNumber=[14], rfOrder=25, authorNames=LI Tianxiu, SHI Lei, WANG Junhui, journalName=Chinese Journal of Quantum Electronics, refType=null, unstructuredReference=LI Tianxiu, SHI Lei, WANG Junhui, et al. Prediction of atmospheric attenuation coefficient of quantum signal based on deep learning[J]. Chinese Journal of Quantum Electronics, 2022, 39(5):786-794., articleTitle=Prediction of atmospheric attenuation coefficient of quantum signal based on deep learning, refAbstract=null), Reference(id=1322907599800246437, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2023, volume=43, issue=4, pageStart=675, pageEnd=785, url=null, language=null, rfNumber=[15], rfOrder=26, authorNames=肖双双, 马亚洁, 李卫炎, journalName=西安科技大学学报, refType=null, unstructuredReference=肖双双，马亚洁，李卫炎，等.基于CiteSpace知识图谱分析的露天矿粉尘浓度预测研究进展与展望[J].西安科技大学学报，2023，43(4):675-785., articleTitle=基于CiteSpace知识图谱分析的露天矿粉尘浓度预测研究进展与展望, refAbstract=null), Reference(id=1322907599867355302, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2023, volume=43, issue=4, pageStart=675, pageEnd=785, url=null, language=null, rfNumber=[15], rfOrder=27, authorNames=XIAO Shuangshuang, MA Yajie, LI Weiyan, journalName=Journal of Xi'an University of Science and Technology, refType=null, unstructuredReference=XIAO Shuangshuang, MA Yajie, LI Weiyan, et al. Prediction of dust concentration in open-pit mine based on CiteSpace knowledge graph analysis[J]. Journal of Xi'an University of Science and Technology, 2023, 43(4):675-785., articleTitle=Prediction of dust concentration in open-pit mine based on CiteSpace knowledge graph analysis, refAbstract=null), Reference(id=1322907599942852775, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2024, volume=44, issue=1, pageStart=74, pageEnd=83, url=null, language=null, rfNumber=[16], rfOrder=28, authorNames=金磊, 杨晓伟, 张浩, journalName=西安科技大学学报, refType=null, unstructuredReference=金磊，杨晓伟，张浩，等.基于HA-RF-SHAP的露天煤矿粉尘浓度预测模型[J].西安科技大学学报，2024，44(1):74-83., articleTitle=基于HA-RF-SHAP的露天煤矿粉尘浓度预测模型, refAbstract=null), Reference(id=1322907600014155944, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2024, volume=44, issue=1, pageStart=74, pageEnd=83, url=null, language=null, rfNumber=[16], rfOrder=29, authorNames=JIN Lei, YANG Xiaowei, ZHANG Hao, journalName=Journal of Xi'an University of Science and Technology, refType=null, unstructuredReference=JIN Lei, YANG Xiaowei, ZHANG Hao, et al. Prediction model for dust concentration in open-pit coal mines based on HA-RF-SHAP[J]. Journal of Xi'an University of Science and Technology, 2024, 44(1):74-83., articleTitle=Prediction model for dust concentration in open-pit coal mines based on HA-RF-SHAP, refAbstract=null), Reference(id=1322907600106430633, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2022, volume=54, issue=增刊1, pageStart=157, pageEnd=161, url=null, language=null, rfNumber=[17], rfOrder=30, authorNames=赵耀忠, 严俊龙, 任吉凯, journalName=煤炭工程, refType=null, unstructuredReference=赵耀忠，严俊龙，任吉凯，等.基于机器学习的露天煤矿粉尘浓度预测[J].煤炭工程，2022，54（增刊1）：157-161., articleTitle=基于机器学习的露天煤矿粉尘浓度预测, refAbstract=null), Reference(id=1322907600194511018, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2022, volume=54, issue=Suppl. 1, pageStart=157, pageEnd=161, url=null, language=null, rfNumber=[17], rfOrder=31, authorNames=ZHAO Yaozhong, YAN Junlong, REN Jikai, journalName=Coal Engineering, refType=null, unstructuredReference=ZHAO Yaozhong, YAN Junlong, REN Jikai, et al. Dust concentration prediction of open-pit coal mine based on machine learning[J]. Coal Engineering, 2022, 54 (Suppl. 1): 157-161., articleTitle=Dust concentration prediction of open-pit coal mine based on machine learning, refAbstract=null), Reference(id=1322907600270008491, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2022, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[18], rfOrder=32, authorNames=罗怀廷, journalName=null, refType=null, unstructuredReference=罗怀廷.寒区露天煤矿粉尘污染特征及预测[D].徐州：中国矿业大学，2022., articleTitle=寒区露天煤矿粉尘污染特征及预测, refAbstract=null), Reference(id=1322907600362283180, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2022, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[18], rfOrder=33, authorNames=LUO Huaiting, journalName=null, refType=null, unstructuredReference=LUO Huaiting. Characteristics and prediction of dust pollution in open-pit coal mines in cold regions[D]. Xuzhou: China University of Mining and Technology, 2022., articleTitle=Characteristics and prediction of dust pollution in open-pit coal mines in cold regions, refAbstract=null), Reference(id=1322907600433586349, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2023, volume=75, issue=6, pageStart=88, pageEnd=93, url=null, language=null, rfNumber=[19], rfOrder=34, authorNames=周昌微, 谢贤平, 都喜东, journalName=有色金属（矿山部分）, refType=null, unstructuredReference=周昌微，谢贤平，都喜东.基于GA-BP神经网络的矿井粉尘浓度预测[J].有色金属（矿山部分），2023，75(6):88-93., articleTitle=基于GA-BP神经网络的矿井粉尘浓度预测, refAbstract=null), Reference(id=1322907600509083822, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2023, volume=75, issue=6, pageStart=88, pageEnd=93, url=null, language=null, rfNumber=[19], rfOrder=35, authorNames=ZHOU Changwei, XIE Xianping, DU Xidong, journalName=Nonferrous Metals (Mining Section), refType=null, unstructuredReference=ZHOU Changwei, XIE Xianping, DU Xidong. Prediction of mine dust concentration based on GA-BP neural network[J]. Nonferrous Metals (Mining Section), 2023, 75(6):88-93., articleTitle=Prediction of mine dust concentration based on GA-BP neural network, refAbstract=null), Reference(id=1322907600576192687, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=1, pageEnd=9, url=null, language=null, rfNumber=[20], rfOrder=36, authorNames=龚晓燕, 邹浩, 刘壮壮, journalName=煤炭科学技术, refType=null, unstructuredReference=龚晓燕，邹浩，刘壮壮，等.综掘面风流调控下风速瓦斯及粉尘融合预测模型研究[J/OL].煤炭科学技术：1-9[2024-09-10]. http://kns.cnki.net/kcms/detail/11.2402.TD.20240308.1159.001.html., articleTitle=综掘面风流调控下风速瓦斯及粉尘融合预测模型研究, refAbstract=null), Reference(id=1322907600664273072, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=1, pageEnd=9, url=null, language=null, rfNumber=[20], rfOrder=37, authorNames=GONG Xiaoyan, ZOU Hao, LIU Zhuangzhuang, journalName=Coal Science and Technology, refType=null, unstructuredReference=GONG Xiaoyan, ZOU Hao, LIU Zhuangzhuang, et al. Research on the fusion prediction model of wind speed, gas and dust under the wind flow control in fully mechanized heading face[J/OL]. Coal Science and Technology: 1-9[2024-09-10]. http://kns.cnki.net/kcms/detail/11.2402.TD.20240308.1159.001.html., articleTitle=Research on the fusion prediction model of wind speed, gas and dust under the wind flow control in fully mechanized heading face, refAbstract=null), Reference(id=1322907600748159153, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2001, volume=45, issue=1, pageStart=5, pageEnd=32, url=null, language=null, rfNumber=[21], rfOrder=38, authorNames=BREIMAN L, journalName=Machine Learning, refType=null, unstructuredReference=BREIMAN L. Random forests[J]. Machine Learning, 2001, 45(1):5-32., articleTitle=Random forests, refAbstract=null), Reference(id=1322907600848822450, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2023, volume=39, issue=14, pageStart=89, pageEnd=96, url=null, language=null, rfNumber=[22], rfOrder=39, authorNames=师翊, 王应宽, 赵龙, journalName=农业工程学报, refType=null, unstructuredReference=师翊，王应宽，赵龙，等.基于CART重要度排序和混合ELM模型的蒸散预测[J].农业工程学报，2023，39(14):89-96., articleTitle=基于CART重要度排序和混合ELM模型的蒸散预测, refAbstract=null), Reference(id=1322907600936902835, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2023, volume=39, issue=14, pageStart=89, pageEnd=96, url=null, language=null, rfNumber=[22], rfOrder=40, authorNames=SHI Yi, WANG Yingkuan, ZHAO Long, journalName=Transactions of the Chinese Society of Agricultural Engineering, refType=null, unstructuredReference=SHI Yi，WANG Yingkuan，ZHAO Long，et al. Evapotranspiration prediction using CART importance ranking and hybrid ELM models[J]. Transactions of the Chinese Society of Agricultural Engineering, 2023, 39(14):89-96., articleTitle=Evapotranspiration prediction using CART importance ranking and hybrid ELM models, refAbstract=null), Reference(id=1322907601008206004, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2024, volume=128, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[23], rfOrder=41, authorNames=SOWMYA R, PREMKUMAR M, JANGIR P, journalName=Engineering Applications of Artificial Intelligence, refType=null, unstructuredReference=SOWMYA R, PREMKUMAR M, JANGIR P. Newton-Raphson-based optimizer: a new population-based metaheuristic algorithm for continuous optimization problems[J]. Engineering Applications of Artificial Intelligence, 2024, 128: 107532., articleTitle=Newton-Raphson-based optimizer: a new population-based metaheuristic algorithm for continuous optimization problems, refAbstract=null), Reference(id=1322907601121452213, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2020, volume=8, issue=1, pageStart=22, pageEnd=34, url=null, language=null, rfNumber=[24], rfOrder=42, authorNames=XUE J K, SHEN B, journalName=Systems Science & Control Engineering, refType=null, unstructuredReference=XUE J K, SHEN B. A novel swarm intelligence optimization approach: sparrow search algorithm[J]. Systems Science & Control Engineering, 2020, 8(1):22-34., articleTitle=A novel swarm intelligence optimization approach: sparrow search algorithm, refAbstract=null), Reference(id=1322907601222115510, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2024, volume=45, issue=3, pageStart=1, pageEnd=8+115, url=null, language=null, rfNumber=[25], rfOrder=43, authorNames=周凯红, 叶高威, 蒋青谷, journalName=河南科技大学学报（自然科学版）, refType=null, unstructuredReference=周凯红，叶高威，蒋青谷.基于SSA-GA-BP神经网络的数显千分表非线性误差补偿[J].河南科技大学学报（自然科学版），2024，45(3):1-8+115., articleTitle=基于SSA-GA-BP神经网络的数显千分表非线性误差补偿, refAbstract=null), Reference(id=1322907601301807287, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2024, volume=45, issue=3, pageStart=1, pageEnd=8+115, url=null, language=null, rfNumber=[25], rfOrder=44, authorNames=ZHOU Kaihong, YE Gaowei, JIANG Qinggu, journalName=Journal of Henan University of Science and Technology (Natural Science), refType=null, unstructuredReference=ZHOU Kaihong, YE Gaowei, JIANG Qinggu. Nonlinear error compensation of digital dial indicator based on SSA-GA-BP neural network[J]. Journal of Henan University of Science and Technology (Natural Science), 2024, 45(3):1-8+115., articleTitle=Nonlinear error compensation of digital dial indicator based on SSA-GA-BP neural network, refAbstract=null), Reference(id=1322907601389887672, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2014, volume=69, issue=null, pageStart=46, pageEnd=61, url=null, language=null, rfNumber=[26], rfOrder=45, authorNames=MIRJALILI S, MIRJALILI S M, LEWIS A, journalName=Advances in Engineering Software, refType=null, unstructuredReference=MIRJALILI S, MIRJALILI S M, LEWIS A. Grey wolf optimizer[J]. Advances in Engineering Software, 2014, 69: 46-61., articleTitle=Grey wolf optimizer, refAbstract=null), Reference(id=1322907601482162361, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2022, volume=39, issue=1, pageStart=56, pageEnd=63, url=null, language=null, rfNumber=[27], rfOrder=46, authorNames=王鲁明, 程静, 王维庆, journalName=现代电力, refType=null, unstructuredReference=王鲁明，程静，王维庆.基于改进灰狼算法的含分布式电源配电网重构研究[J].现代电力，2022，39(1):56-63., articleTitle=基于改进灰狼算法的含分布式电源配电网重构研究, refAbstract=null), Reference(id=1322907601553465530, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2022, volume=39, issue=1, pageStart=56, pageEnd=63, url=null, language=null, rfNumber=[27], rfOrder=47, authorNames=WANG Luming, CHENG Jing, WANG Weiqing, journalName=Modern Electric Power, refType=null, unstructuredReference=WANG Luming, CHENG Jing, WANG Weiqing. Research on distribution network reconfiguration with distributed generation based on improved grey wolf optimizer[J]. Modern Electric Power, 2022, 39(1):56-63., articleTitle=Research on distribution network reconfiguration with distributed generation based on improved grey wolf optimizer, refAbstract=null), Reference(id=1322907601637351611, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2016, volume=95, issue=null, pageStart=51, pageEnd=67, url=null, language=null, rfNumber=[28], rfOrder=48, authorNames=MIRJALILI S, LEWIS A, journalName=Advances in Engineering Software, refType=null, unstructuredReference=MIRJALILI S, LEWIS A. The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95: 51-67., articleTitle=The whale optimization algorithm, refAbstract=null), Reference(id=1322907601712849084, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2023, volume=51, issue=12, pageStart=151, pageEnd=157, url=null, language=null, rfNumber=[29], rfOrder=49, authorNames=高永涛, 朱强, 吴顺川, journalName=华中科技大学学报（自然科学版）, refType=null, unstructuredReference=高永涛，朱强，吴顺川，等.基于AVOA-XGBoost模型的岩爆预测研究[J].华中科技大学学报（自然科学版），2023，51(12):151-157., articleTitle=基于AVOA-XGBoost模型的岩爆预测研究, refAbstract=null), Reference(id=1322907601809318077, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, doi=null, pmid=null, pmcid=null, year=2023, volume=51, issue=12, pageStart=151, pageEnd=157, url=null, language=null, rfNumber=[29], rfOrder=50, authorNames=GAO Yongtao, ZHU Qiang, WU Shunchuan, journalName=Journal of Huazhong University of Science and Technology（Natural Science Edition）, refType=null, unstructuredReference=GAO Yongtao，ZHU Qiang，WU Shunchuan，et al.Rockburst prediction study based on AVOA-XGBoost model[J]. Journal of Huazhong University of Science and Technology（Natural Science Edition），2023，51（12）：151-157., articleTitle=Rockburst prediction study based on AVOA-XGBoost model, refAbstract=null)], funds=[Fund(id=1322907597573070984, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, awardId=52304195, language=CN, fundingSource=国家自然科学基金项目(52304195), fundOrder=null, country=null), Fund(id=1322907597652762761, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, awardId=2022YFC2503201, language=CN, fundingSource=国家重点研发计划项目(2022YFC2503201), fundOrder=null, country=null), Fund(id=1322907597728260234, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, awardId=EC2023004, language=CN, fundingSource=煤炭安全精准开采国家地方联合工程研究中心项目(EC2023004), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1322907593559122002, tenantId=1146029695717560320, journalId=1225396423026438145, 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journalId=1225396423026438145, articleId=1322907584386179087, language=CN, label=图8, caption=NRBO-RF预测结果对比, figureFileSmall=Gh7PvnJpLmldsWhWOgff4Q==, figureFileBig=Ml3ocKqibCCCY+kXNjAMVQ==, tableContent=null), ArticleFig(id=1322907596654518400, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, language=EN, label=Table 1, caption=

Pearson correlation coefficient of each factor

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因素湿度风速温度粉尘浓度
湿度10.077**–0.081**0.089**
风速0.077**10.0340.080**
温度–0.081**0.0341–0.110**
粉尘浓度0.089**0.080**–0.110**1
), ArticleFig(id=1322907596809707649, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, language=CN, label=表1, caption=

各因素的皮尔逊相关系数

, figureFileSmall=null, figureFileBig=null, tableContent=
因素湿度风速温度粉尘浓度
湿度10.077**–0.081**0.089**
风速0.077**10.0340.080**
温度–0.081**0.0341–0.110**
粉尘浓度0.089**0.080**–0.110**1
), ArticleFig(id=1322907596956508290, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, language=EN, label=Table 2, caption=

Hyperparameter value range of RF model

, figureFileSmall=null, figureFileBig=null, tableContent=
超参数取值范围
n_trees1~200
min_samples_leaf1~20
), ArticleFig(id=1322907597082337411, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, language=CN, label=表2, caption=

RF 模型的超参数取值范围

, figureFileSmall=null, figureFileBig=null, tableContent=
超参数取值范围
n_trees1~200
min_samples_leaf1~20
), ArticleFig(id=1322907597178806404, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, language=EN, label=Table 3, caption=

Parameter settings for different algorithmic models

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模型n_estimatorsmin_samples_leaf
GWO-RF658
WOA-RF282
SSA-RF978
NRBO-RF10113
), ArticleFig(id=1322907597258498181, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, language=CN, label=表3, caption=

不同算法模型的参数设置

, figureFileSmall=null, figureFileBig=null, tableContent=
模型n_estimatorsmin_samples_leaf
GWO-RF658
WOA-RF282
SSA-RF978
NRBO-RF10113
), ArticleFig(id=1322907597329801350, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, language=EN, label=Table 4, caption=

Evaluation results of different algorithmic models

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模型RMSEMAER2
RF0.792 40.085 40.967 2
GWO-RF0.227 80.012 60.996 9
WOA-RF0.293 20.018 40.995 5
SSA-RF0.498 30.026 40.985 5
NRBO-RF0.219 60.010 50.997 5
), ArticleFig(id=1322907597447241863, tenantId=1146029695717560320, journalId=1225396423026438145, articleId=1322907584386179087, language=CN, label=表4, caption=

不同算法模型的评估结果

, figureFileSmall=null, figureFileBig=null, tableContent=
模型RMSEMAER2
RF0.792 40.085 40.967 2
GWO-RF0.227 80.012 60.996 9
WOA-RF0.293 20.018 40.995 5
SSA-RF0.498 30.026 40.985 5
NRBO-RF0.219 60.010 50.997 5
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基于NRBO-RF的井下粉尘浓度预测模型研究
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郭福明 1 , 李姗姗 2 , 郑春山 1
矿业研究与开发 | 矿山安全与环保 2025,45(6): 173-181
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矿业研究与开发 |矿山安全与环保 2025 , 45 (6) : 173 -181
基于NRBO-RF的井下粉尘浓度预测模型研究
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郭福明1 , 李姗姗2 , 郑春山1
作者信息
  • 1.安徽理工大学 安全科学与工程学院，安徽　淮南市　232001
  • 2.安徽理工大学 经济与管理学院，安徽　淮南市　232001
通讯作者:
李姗姗（1992—），女，河北保定人，博士，副教授，主要从事安全管理心理与行为、环境治理理论与方法、“双碳”管理与政策等方面的研究。E-mail:
作者简介:

郭福明（2000—），女，河南周口人，硕士研究生，主要从事粉尘防控与职业健康管理研究。E-mail:

Research on Dust Concentration Prediction Model for Underground Mine Based on NRBO-RF
Fuming GUO1 , Shanshan LI2 , Chunshan ZHENG1
Affiliations
  • 1.School of Safety Science and Engineering, Anhui University of Science and Technology, Anhui, Huainan 232001, China
  • 2.School of Economics and Management, Anhui University of Science and Technology, Anhui, Huainan 232001, China
出版时间: 2025-06-25
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为实现矿井粉尘浓度的准确预测，利用陕西某矿井采煤工作面的现场监测数据，采用随机森林(RF)模型，将温度、湿度、风速和当前时刻的粉尘浓度作为输入向量，构建多因素粉尘浓度预测模型；并提出使用4种智能优化算法(牛顿-拉夫逊优化算法(NRBO)、麻雀搜索算法、灰狼优化算法和鲸鱼优化算法)对随机森林的参数(树的数量和叶子节点数)进行寻优，通过决定系数(R2)、均方根误差(RMSE)和平均绝对误差(MAE)评价模型的优劣。结果表明：粉尘浓度预测的最优模型是NRBO-RF模型；与RF模型相比，NRBO-RF模型的R2提高了0.030 3, RMSE降低了0.572 8, MAE降低了0.074 9，其拟合能力和预测精度最高。研究结果可为矿井粉尘的智能预测及防控提供参考。

矿井粉尘浓度  /  随机森林  /  牛顿-拉夫逊优化算法  /  预测模型

In order to realize the accurate prediction of mine dust concentration, based on the field monitoring data of a mining working face in a coal mine in Shaanxi Province, the random forest (RF) model was used to construct a multi-factor dust concentration prediction model by taking temperature, humidity, wind speed and dust concentration at the current moment as input vectors. Four intelligent optimization algorithms (Newton-Raphson-based optimizer (NRBO), sparrow search algorithm, grey wolf optimizer and whale optimization algorithm) were proposed to optimize the parameters of random forest (number of trees and number of leaf nodes). The model was evaluated by determination coefficient(R2), root mean square error (RMSE) and mean absolute error (MAE). The results show that the optimal model for dust concentration prediction is NRBO-RF model. Compared with RF model, the R2, RMSE and MAE of NRBO-RF model are increased by 0.030 3, 0.572 8 and 0.074 9, respectively, and its fitting ability and prediction accuracy are the highest. The research results can provide a reference for intelligent prediction and prevention and control of mine dust.

Mine dust concentration  /  Random forest  /  Newton-Raphson-based optimizer  /  Prediction model
郭福明, 李姗姗, 郑春山. 基于NRBO-RF的井下粉尘浓度预测模型研究. 矿业研究与开发, 2025 , 45 (6) : 173 -181 .
Fuming GUO, Shanshan LI, Chunshan ZHENG. Research on Dust Concentration Prediction Model for Underground Mine Based on NRBO-RF[J]. Mining Research and Development, 2025 , 45 (6) : 173 -181 .
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0　引言
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煤炭在我国能源体系中占据主体地位，是中国经济发展的关键[1]，在未来很长一段时间内，仍将起到兜底保障作用[2]。近年来，由于浅层矿产资源逐渐匮乏，深层矿井开发工作开始不断推进[3]。随着开采机械的不断升级，矿尘产出量不断增加，矿井内的粉尘危害日益加重，严重威胁矿井工作人员的身心健康[4]，增加人员患尘肺病的风险，同时，机器长时间工作在高粉尘浓度环境下会受到严重磨损，增加矿井开采成本；并且，当矿井内的粉尘达到一定浓度时易引起爆炸[5]，严重时可能会造成重大安全事故。粉尘监测-预警系统的介入能够快速准确监测粉尘浓度并发出警报，以实现矿井内粉尘浓度的精准预测，对保护矿井工作人员人身健康，降低矿井工作的开采成本，保障矿井生产安全等具有重要意义。
研究发现，随着计算机技术的高速发展，我国矿山开采逐渐智能化，而当前有关计算机的粉尘防治研究以数值模拟为主，这种方法能够准确分析粉尘的扩散规律和尘源位置的动态变化情况[6-8]，但在粉尘浓度的精准预测方面有所欠缺。机器学习具有高准确性和鲁棒性强的优点，具有强泛化能力，能够有效处理高维数据。目前，机器学习和深度学习已广泛应用于瓦斯预测[9-10]、PM2.5预测[11-12]以及大气环境预测[13-14]。机器学习的出现也为粉尘浓度的精准预测提供了新思路。从2019年开始，基于机器学习的智能预测的相关发文量开始显著增加[15]，其中，金磊等[16]采用SHAP可解释模型分析了露天煤矿粉尘浓度的影响因素，并构建随机森林模型，对不同算法优化的RF模型进行对比，最后，建立了适用于露天矿粉尘浓度预测的HA-RF-SHAP模型。赵耀忠等[17]采用机器学习方法，建立了基于支持向量机算法的模型，实现在非线性关系的影响因素下进行有关粉尘浓度的预测，并验证了该方法的可行性。罗怀廷[18]采用粒子群算法优化的随机森林模型，建立露天煤矿粉尘浓度RF-PSO混合预测模型，并确定粉尘浓度各影响因素显著性的大小关系。有关矿井粉尘浓度预测的研究较少，其中周昌微等[19]通过对比CNN、BP、GA-BP三种模型的预测效果，最终确定GA-BP预测模型对矿井工作面的粉尘浓度具有较好的预测能力。龚晓燕等[20]引入差分进化算法对风流调控下的风速、瓦斯浓度及粉尘浓度的多源数据融合神经网络预测模型进行了研究。目前矿井粉尘研究主要集中于露天矿，对于矿井内的粉尘浓度研究较少，但矿井开采时的产尘量依旧超出国家相关卫生标准限值，存在一定的安全隐患。
本研究以陕西某矿井内采煤工作面的现场监测数据为基础，分析矿井内粉尘浓度的影响因素，利用随机森林回归模型建立基于多因素的矿井粉尘浓度预测模型，并对矿井内的粉尘浓度进行预测，训练模型时，选择4种智能算法优化随机森林回归模型的参数，以提高模型的预测精度，以期为矿井的智能化粉尘防治提供参考。
1　模型方法
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1.1　随机森林
随机森林(Random Forest, RF)回归是一种基于决策树的集成学习算法，于2001年由BREIMAN提出[21]，该方法通过构建多个决策树，对其预测结果进行整合，以完成回归分析。随机森林模型中，决策树间互相独立，随机选取子样本并学习[22]，能够有效降低过拟合的可能性。随机森林是对多棵树的预测值取平均值或加权平均值，得出最后的回归结果。随机森林原理[16]如图1所示。
随机森林回归作为一种强大的机器学习方法，在不同领域均能取得良好的预测结果，具有以下优点：（1）能够高效处理复杂、高维、非线性的数据，预测精度更高；（2）处理大规模数据集时，能够在较短的时间内输出预测结果，在大数据场景下具有较好的应用潜力；（3）具有较好的泛化性能，能有效降低过拟合风险；（4）对呈现非线性关系的数据，具有较强的拟合能力；（5）能够处理缺失值和异常值。
1.2　牛顿-拉夫逊优化算法
牛顿-拉夫逊优化算法（Newton-Raphson-Based Optimizer, NRBO）[23]是一种新的元启发式算法，其受到牛顿-拉夫逊方法的启发，使用牛顿-拉夫逊搜索规则（Newton-Raphson Search Rule, NRSR）和陷阱避免算子（Trap Avoidance Operator, TAO)以及几组矩阵来探索整个搜索过程，提高NRBO的探测能力，提高收敛率，达到更好的搜索空间位置。
1.2.1　种群初始化
与其他元启发式算法一样，NRBO通过在候选解的边界内产生初始随机种群来启动对最优解的搜索。基于存在N个种群的事实，并且每个种群由模糊决策变量/向量组成。因此，使用公式(1)生成随机种群：
式中：表示第n个总体的第j个维度的位置；u表示上限；l表示下限；rand表示(0, 1)之间的随机数；M表示问题维度。
公式(2)给出了一个可以描述所有维度种群的种群矩阵：
式中，Xn表示总体矩阵。
1.2.2　牛顿-拉夫逊搜索规则
NRSR可以促进勘探趋势和加速收敛，其相关计算式如下：
式中：randn表示均值为0、方差为1的正态分布随机数；Xw表示最差位置；Xb表示最佳矢量位置；yw和yb是使用Zn+1和xn生成的两个向量的位置；指当前矢量位置；是通过更新而得到的新矢量位置；Mean为均值运算符；a、b为权重系数。
1.2.3　陷阱避免算子
纳入TAO是为了提高NRBO处理实际问题的有效性，避免局部最优。使用TAO可以显著改变的位置，通过组合最佳位置Xb和当前矢量位置来产生具有增强质量的解决方案。相关计算见公式(8)和公式(9)。
式中：θ1和θ2分别表示区间(−1, 1)和区间(−0.5, 0.5)的均匀随机数；δ为勘探开发平衡适应系数；μ1和μ2为随机数。
1.3　麻雀搜索算法
麻雀搜索算法（Sparrow Search Algorithm, SSA)于2020年被提出[24]，该算法受麻雀的觅食行为和反捕食行为的启发，将全部麻雀种群分成发现者、加入者，在种群个体中，发现者和加入者的身份是动态变化的。发现者通常能源储备量较大，在整个种群中的任务是搜索具有丰富食物的地区，为加入者提供觅食的区域和方向；加入者则是根据发现者提供的信息获取食物。SSA的核心思想是利用麻雀群体在觅食过程中个体间的互相影响以搜寻最优解[25]。此方法是一种新的优化方法，具有搜索性能好、收敛速度快等特点。
1.4　灰狼优化算法
灰狼优化算法(Grey Wolf Optimizer, GWO)是于2014年提出的一种种群智能优化算法[26]，是在对灰狼社群行为进行模拟的基础上提出的启发式优化算法，其灵感来源于灰狼群体的捕食行为，包含了灰狼的社会等级分层、跟踪、包围和狩猎等4个步骤。其中，每个灰狼的位置是解空间中的一种可行解。狼群中，3只灰狼(狼王及其两个护卫)占据最佳位置，狩猎时带领着狼群呈蛇形移动，捕捉猎物，直至找到最优解[27]。该算法具有收敛速度快、参数少、易于实现等特点。
1.5　鲸鱼优化算法
鲸鱼优化算法（Whale Optimization Algorithm, WOA）是于2016年提出的一种新的群体智能优化搜索方法[28]。该算法的核心思路是模仿鲸类群体的合作捕食行为，并以此为基础进行迭代搜索寻优。鲸的搜寻策略可以分为两种：一种是螺旋式的，另一种是线性的；群协作行为通过不断修正目标函数及控制参数，实现对寻优空间的动态调节。它具有良好的全局寻优能力，稳健性好，收敛速度快。
本研究分别使用NRBO、SSA、GWO和WOA等4种智能优化算法对RF模型中的TreeBagger和Leaf两种参数进行寻优，并对优化后的RF模型进行训练，建立最佳粉尘浓度预测模型。本研究主要探究矿井内温度、湿度、风速等环境因素对粉尘浓度的影响，因此选取矿井内的温度、湿度、风速和当前时刻的粉尘浓度作为模型的输入向量，下一时刻的粉尘浓度作为输出向量，模型构建流程如图2所示。具体步骤如下：（1）对原始数据(温度、湿度、风速、当前时刻的粉尘浓度)进行预处理，以4∶1的比例分为训练集和测试集；（2）建立RF模型，初始化参数；（3）分别采用NRBO、SSA、GWO和WOA等4种智能算法优化参数，确定参数的最优解，并训练优化后的RF模型；（4）评估所构建预测模型的准确率；（5）判定是否满足结束条件；（6）确定最终粉尘浓度预测模型。
2　模型试验与分析
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2.1　数据获取
研究地点为陕西某矿井的25212采煤工作面，收集呼吸性粉尘浓度的现场监测数据，25212工作面平面图如图3所示。
研究选取2022年8月8日至2022年8月19日的粉尘浓度、温度、湿度、风速等现场监测数据作为研究数据。原始监测数据受矿井环境影响，存在缺失和异常的可能性较大，为减少数据对预测精度的影响，提高预测精度，构建模型时，首先对原始数据进行预处理。
2.2　数据处理
为确定模型应用的可行性，采用SPSS软件通过皮尔逊系数分析不同样本特征间的相关性。
式中：R为皮尔逊相关系数；ai、bi为当前样本数据；ā、为样本数据平均值。
在SPSS相关性分析结果中，若皮尔逊相关系数的绝对值大于0.05，则表明变量间具有显著的相关性。风速、温度、湿度和粉尘浓度等数据的皮尔逊相关系数见表1。
由表1可知，温度、湿度和风速三个因素与粉尘浓度间的相关性显著，为解决量纲的差异性问题，对所有特征的属性进行统一处理，将所有数据归一化至[0, 1]内，以提高模型的精确程度。归一化计算公式如下：
式中：为归一化后的值；xmax和xmin分别为样本数据的最大值和最小值。
在实际测量中，由于粉尘检测仪在测量时存在滞后性，从而造成测量结果的丢失，为了避免丢失的数据对模型的训练造成影响，必须对缺失的数据进行填充。本研究选取机器学习和深度学习算法在数据预处理中较为常用的均值插值法对缺失数据进行填充。对填充后的完整数据进行异常值分析，分析结果如图4所示。
根据图4得出温度、湿度的数据中存在异常值，以每个样本数据的平均值替换异常值后的数据变化如图5所示。
2.3　模型评价
评价指标是评估模型预测性能的一种重要手段，是对模型进行优化和提高预测精度的关键[29]。本研究中为判定模型的准确性，采用决定系数R2作为各模型的效果判定指标，用以判断不同量纲下模型的效果，R2的取值范围为[0, 1]，其值越趋近1, 代表该模型具有较好的预测效果。以均方根误差 (RMSE) 和平均绝对误差(MAE) 为模型预测精度的判断标准，用以判定模型预测效果，模型的预测精度与其值呈负相关。R2、RMSE 和 MAE 的计算公式如下：
式中：m表示预测总次数；ŷi表示在i时刻的预测值；为平均值；yi为原始数据的真实值。
2.4　试验与分析
将监测的温度、湿度、风速和当前时刻的粉尘浓度作为输入向量，以4:1的比例划分为训练集和测试集，选择RF模型，运行环境为Matlab 2021a，以决策树(trees)和最小叶子数(leaf)为参数，其中RF模型的超参数取值范围见表2。
为验证RF模型的可行性和准确性，以该模型结果与卷积神经网络模型（CNN）、极限学习机(ELM)模型和支持向量机回归预测模型(SVR)分别进行对比，以4:1的划分原则分为训练集和测试集，不同模型预测对比如图6所示。
根据图6可以清晰地看出RF模型的R2是4个模型中最高的，为0.967 2，证明采用RF模型的预测效果较好，且该模型的RMSE和MAE也在4个模型中数值最低，证明RF模型在预测过程中误差最小，预测精度较高。因此，本文选用RF模型对陕西某煤矿的温度、湿度、风速以及当前时刻的粉尘浓度进行预测。根据测试集预测结果绘制不同模型的误差分布图，4种模型的误差分布图如图7所示。
从图7中可以看出，RF模型测试集的误差值大多处于[−10, 10]，其他3组模型的误差结果处于[−15, 15]，且RF模型的误差分布较为均匀，由此更能证明RF模型的准确性。
采用NRBO、GWO、WOA和SSA等4种智能算法对RF模型进行参数寻优，对比分析得出预测效果较好的RF模型。不同算法寻找的最优参数有所不同，参数寻优结果见表3。
将研究数据按照训练集和测试集4:1的划分原则，选取前1 196组数据作为训练集，最后通过RMSE、MAE和R2等3种指标对模型进行评估，模型对比评估结果见表4。
根据表3得出，以上4种启发式算法对于RF模型的优化均起到良好的效果，其中由NRBO优化后的RF模型，RMSE降低了0.572 8, MAE降低了0.074 9, R2提高了0.030 3。而GWO、WOA和SSA智能算法优化后的RF模型RMSE分别降低了0.564 6, 0.499 2和0.294 1；MAE分别降低了0.072 8, 0.067 0和0.059 0；R2分别提高了0.029 7, 0.028 3和0.018 3。由此可以看出，NRBO的优化结果最好，其次是GWO和WOA, SSA的优化效果相对较差。因此，本文选择NRBO算法对RF模型进行优化，而后结合影响因素对矿井粉尘浓度进行预测研究。
采用构建的基于NRBO-RF模型对t+1时刻粉尘浓度进行预测，对比t+1时刻的真实值和预测值如图8所示。根据图9可以直观地看出，NRBO-RF模型的粉尘浓度预测结果与真实值的贴合度较高，预测效果较好。
3　结论
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基于陕西某煤矿井下采煤工作面的温度、湿度、风速和当前粉尘浓度等影响因素，采用4种不同智能算法优化后的随机森林模型，对矿井粉尘浓度进行预测研究，得出的主要结论如下。
（1）通过RF、ELM、CNN、SVR等4种不同模型在矿井粉尘浓度多因素预测研究中的对比结果可知，RF模型的预测效果最佳，其决定系数R2高达0.967 2。
（2）采用GWO、WOA、SSA、NRBO等4种不同算法对RF模型进行寻优，结果表明，预测粉尘浓度的最佳模型是NRBO-RF模型，与RF模型相比，NRBO-RF模型的决定系数提高了0.030 3，均方根误差降低了0.572 8，平均绝对误差降低了0.074 9。
基金
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  • 国家自然科学基金项目(52304195)
  • 国家重点研发计划项目(2022YFC2503201)
  • 煤炭安全精准开采国家地方联合工程研究中心项目(EC2023004)
文献
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参考文献 引证文献
排序方式：
[1]
袁亮.我国煤炭工业高质量发展面临的挑战与对策[J].中国煤炭，2020，46(1):6-12.
YUAN Liang. Challenges and countermeasures for high quality development of China's coal industry[J].China Coal, 2020, 46(1):6-12.
[2]
刘峰，郭林峰，赵路正.双碳背景下煤炭安全区间与绿色低碳技术路径[J].煤炭学报，2022，47(1):1-15.
LIU Feng, GUO Linfeng, ZHAO Luzheng. Research on coal safety range and green low-carbon technology path under the dual-carbon background[J]. Journal of China Coal Society, 2022, 47(1): 1-15.
[3]
王金华，谢和平，刘见中，等.煤炭近零生态环境影响开发利用理论和技术构想[J].煤炭学报，2018，43(5):1198-1209.
WANG Jinhua, XIE Heping, LIU Jianzhong, et al. Coal development and utilization theory and technical system of near-zero ecological environment impact[J]. Journal of China Coal Society, 2018, 43(5):1198-1209.
[4]
李德文，隋金君，刘国庆，等.中国煤矿粉尘危害防治技术现状及发展方向[J].矿业安全与环保，2019，46(6):1-7+13.
LI Dewen, SUI Jinjun, LIU Guoqing, et al. Technical status and development direction of coal mine dust hazard prevention and control technology in China[J]. Mining Safety & Environmental Protection, 2019, 46(6): 1-7+13.
[5]
蒋仲安，曾发镔，王亚朋.我国金属矿山采运过程典型作业场所粉尘污染控制研究现状与展望[J].金属矿山，2021(1): 135-153.
JIANG Zhong'an, ZENG Fabin, WANG Yapeng. Research status and prospect of dust pollution control in typical working places during mining and transportation of metal mines in China[J]. Metal Mine, 2021(1):135-153.
[6]
江丙友，王一凡，林汉毅，等.煤矿综掘工作面长压短抽除尘性能与尘源移动路径关系研究[J].煤炭学报，2024，49（7）：2973-2985.
JIANG Bingyou, WANG Yifan, LIN Hanyi, et al. Relationship between long pressure short extraction dust removal performance and dust source movement path in comprehensive heading face of coal mines[J]. Journal of China Coal Society, 2024, 49(7):2973-2985.
[7]
GUO L D, NIE W, YIN S, et al. The dust diffusion modeling and determination of optimal airflow rate for removing the dust generated during mine tunneling[J]. Building and Environment, 2020, 178:106846.
[8]
王德强，滕海旭，王明钦，等.煤矿主提升井内气固两相流运动特性研究[J].煤矿安全，2023，54(11):42-47.
WANG Deqiang, TENG Haixu, WANG Mingqin, et al. Study on movement characteristics of gas-solid two-phase flow in main lifting shaft of coal mine[J]. Safety in Coal Mines, 2023, 54(11):42-47.
[9]
薛生，郑晓亮，袁亮，等.基于机器学习的煤与瓦斯突出预测研究进展及展望[J].煤炭学报，2024，49(2):664-694.
XUE Sheng, ZHENG Xiaoliang, YUAN Liang, et al. A review on coal and gas outburst prediction based on machine learning[J]. Journal of China Coal Society, 2024, 49(2): 664-694.
[10]
乔威豪，安葳鹏，赵雪菡，等.基于改进极限学习机的煤与瓦斯突出预测研究[J].矿业研究与开发，2024，44(5):98-105.
QIAO Weihao, AN Weipeng, ZHAO Xuehan, et al. Research on coal and gas outburst prediction based on improved extreme learning machine[J]. Mining Research and Development, 2024, 44(5):98-105.
[11]
刘希亮，赵俊杰，张羽民，等.基于时空认知膨胀卷积网络与多源影响因素的PM2.5细粒度预测模型[J].北京工业大学学报，2024，50(3):333-347.
LIU Xiliang, ZHAO Junjie, ZHANG Yumin, et al. PM2.5fine-grained air quality prediction model based on spatial-temporal cognitive dilated convolution network and multi-source influencing factors[J]. Journal of Beijing University of Technology, 2024, 50(3):333-347.
[12]
CAI S J, FANG F X, TANG X, et al. A hybrid data-driven and data assimilation method for spatiotemporal forecasting: PM2.5 forecasting in China[J]. Journal of Advances in Modeling Earth Systems, 2024, 16(2): e2023MS003789.
[13]
王子倩，郝炜伟，董玲池，等.重庆大气中多环芳香类物质气粒分配模型预测[J].中国环境科学，2024，44(2):629-637.
WANG Ziqian, HAO Weiwei, DONG Lingchi, et al. Modeling prediction of particle distribution of polycyclic aromatic compounds in the winter atmosphere over Chongqing urban area[J]. China Environmental Science, 2024, 44(2):629-637.
[14]
李天秀，石磊，王俊辉，等.基于深度学习的量子信号大气衰减系数预测[J].量子电子学报，2022，39(5):786-794.
LI Tianxiu, SHI Lei, WANG Junhui, et al. Prediction of atmospheric attenuation coefficient of quantum signal based on deep learning[J]. Chinese Journal of Quantum Electronics, 2022, 39(5):786-794.
[15]
肖双双，马亚洁，李卫炎，等.基于CiteSpace知识图谱分析的露天矿粉尘浓度预测研究进展与展望[J].西安科技大学学报，2023，43(4):675-785.
XIAO Shuangshuang, MA Yajie, LI Weiyan, et al. Prediction of dust concentration in open-pit mine based on CiteSpace knowledge graph analysis[J]. Journal of Xi'an University of Science and Technology, 2023, 43(4):675-785.
[16]
金磊，杨晓伟，张浩，等.基于HA-RF-SHAP的露天煤矿粉尘浓度预测模型[J].西安科技大学学报，2024，44(1):74-83.
JIN Lei, YANG Xiaowei, ZHANG Hao, et al. Prediction model for dust concentration in open-pit coal mines based on HA-RF-SHAP[J]. Journal of Xi'an University of Science and Technology, 2024, 44(1):74-83.
[17]
赵耀忠，严俊龙，任吉凯，等.基于机器学习的露天煤矿粉尘浓度预测[J].煤炭工程，2022，54（增刊1）：157-161.
ZHAO Yaozhong, YAN Junlong, REN Jikai, et al. Dust concentration prediction of open-pit coal mine based on machine learning[J]. Coal Engineering, 2022, 54 (Suppl. 1): 157-161.
[18]
罗怀廷.寒区露天煤矿粉尘污染特征及预测[D].徐州：中国矿业大学，2022.
LUO Huaiting. Characteristics and prediction of dust pollution in open-pit coal mines in cold regions[D]. Xuzhou: China University of Mining and Technology, 2022.
[19]
周昌微，谢贤平，都喜东.基于GA-BP神经网络的矿井粉尘浓度预测[J].有色金属（矿山部分），2023，75(6):88-93.
ZHOU Changwei, XIE Xianping, DU Xidong. Prediction of mine dust concentration based on GA-BP neural network[J]. Nonferrous Metals (Mining Section), 2023, 75(6):88-93.
[20]
龚晓燕，邹浩，刘壮壮，等.综掘面风流调控下风速瓦斯及粉尘融合预测模型研究[J/OL].煤炭科学技术：1-9[2024-09-10]. http://kns.cnki.net/kcms/detail/11.2402.TD.20240308.1159.001.html.
GONG Xiaoyan, ZOU Hao, LIU Zhuangzhuang, et al. Research on the fusion prediction model of wind speed, gas and dust under the wind flow control in fully mechanized heading face[J/OL]. Coal Science and Technology: 1-9[2024-09-10]. http://kns.cnki.net/kcms/detail/11.2402.TD.20240308.1159.001.html.
[21]
BREIMAN L. Random forests[J]. Machine Learning, 2001, 45(1):5-32.
[22]
师翊，王应宽，赵龙，等.基于CART重要度排序和混合ELM模型的蒸散预测[J].农业工程学报，2023，39(14):89-96.
SHI Yi，WANG Yingkuan，ZHAO Long，et al. Evapotranspiration prediction using CART importance ranking and hybrid ELM models[J]. Transactions of the Chinese Society of Agricultural Engineering, 2023, 39(14):89-96.
[23]
SOWMYA R, PREMKUMAR M, JANGIR P. Newton-Raphson-based optimizer: a new population-based metaheuristic algorithm for continuous optimization problems[J]. Engineering Applications of Artificial Intelligence, 2024, 128: 107532.
[24]
XUE J K, SHEN B. A novel swarm intelligence optimization approach: sparrow search algorithm[J]. Systems Science & Control Engineering, 2020, 8(1):22-34.
[25]
周凯红，叶高威，蒋青谷.基于SSA-GA-BP神经网络的数显千分表非线性误差补偿[J].河南科技大学学报（自然科学版），2024，45(3):1-8+115.
ZHOU Kaihong, YE Gaowei, JIANG Qinggu. Nonlinear error compensation of digital dial indicator based on SSA-GA-BP neural network[J]. Journal of Henan University of Science and Technology (Natural Science), 2024, 45(3):1-8+115.
[26]
MIRJALILI S, MIRJALILI S M, LEWIS A. Grey wolf optimizer[J]. Advances in Engineering Software, 2014, 69: 46-61.
[27]
王鲁明，程静，王维庆.基于改进灰狼算法的含分布式电源配电网重构研究[J].现代电力，2022，39(1):56-63.
WANG Luming, CHENG Jing, WANG Weiqing. Research on distribution network reconfiguration with distributed generation based on improved grey wolf optimizer[J]. Modern Electric Power, 2022, 39(1):56-63.
[28]
MIRJALILI S, LEWIS A. The whale optimization algorithm[J]. Advances in Engineering Software, 2016, 95: 51-67.
[29]
高永涛，朱强，吴顺川，等.基于AVOA-XGBoost模型的岩爆预测研究[J].华中科技大学学报（自然科学版），2023，51(12):151-157.
GAO Yongtao，ZHU Qiang，WU Shunchuan，et al.Rockburst prediction study based on AVOA-XGBoost model[J]. Journal of Huazhong University of Science and Technology（Natural Science Edition），2023，51（12）：151-157.
2025年第45卷第6期
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  • 接收时间：2024-08-12
  • 首发时间：2026-10-30
  • 出版时间：2025-06-25
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  • 收稿日期：2024-08-12
基金
国家自然科学基金项目(52304195)
国家重点研发计划项目(2022YFC2503201)
煤炭安全精准开采国家地方联合工程研究中心项目(EC2023004)
作者信息
    1.安徽理工大学 安全科学与工程学院，安徽　淮南市　232001
    2.安徽理工大学 经济与管理学院，安徽　淮南市　232001

通讯作者:

李姗姗（1992—），女，河北保定人，博士，副教授，主要从事安全管理心理与行为、环境治理理论与方法、“双碳”管理与政策等方面的研究。E-mail:
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2种不同金属材料的力学参数
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Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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