基于SBG_XGBoost的煤矿安全应急物资储备中心选址研究
TD77; 煤矿安全应急物资储备中心选址优化是推动煤矿安全应急体系建设的重要基础,为提高煤矿安全应急物资储备中心选址的准确度和合理性,提出利用人口因素、交通因素、经济因素和自然因素,建立融合多源空间数据的煤矿安全应急物资储备中心选址机器学习组合模型,提高煤矿安全应急物资储备中心选址的准确度和科学性.利用ArcGIS分别通过渔网划分、空间链接和投影等任务对多源空间数据进行处理,并利用SMOTEENN算法避免数据不均衡的负面影响,从而构建适用于机器学习模型分析的数据集.通过对不同机器学习算法、不同特征选择方法以及不同参数寻优方法进行比较分析,得出XGBoost机器学习算法、Boruta算法和遗传算...
Saved in:
| Published in | 煤炭学报 Vol. 49; no. 8; pp. 3535 - 3545 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | Chinese |
| Published |
河南理工大学工商管理学院能源经济研究中心,河南焦作 454000
01.08.2024
太行发展研究院,河南焦作 454000%河南理工大学工商管理学院能源经济研究中心,河南焦作 454000 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0253-9993 |
| DOI | 10.13225/j.cnki.jccs.2024.0477 |
Cover
| Abstract | TD77; 煤矿安全应急物资储备中心选址优化是推动煤矿安全应急体系建设的重要基础,为提高煤矿安全应急物资储备中心选址的准确度和合理性,提出利用人口因素、交通因素、经济因素和自然因素,建立融合多源空间数据的煤矿安全应急物资储备中心选址机器学习组合模型,提高煤矿安全应急物资储备中心选址的准确度和科学性.利用ArcGIS分别通过渔网划分、空间链接和投影等任务对多源空间数据进行处理,并利用SMOTEENN算法避免数据不均衡的负面影响,从而构建适用于机器学习模型分析的数据集.通过对不同机器学习算法、不同特征选择方法以及不同参数寻优方法进行比较分析,得出XGBoost机器学习算法、Boruta算法和遗传算法在对煤矿安全应急物资储备中心选址分析中,相较于其他机器学习算法、特征选择方法和参数寻优方法其表现更为优异.故基于各自优势,得到煤矿安全应急物资储备中心选址的机器学习组合模型.最后引入SHAP分析方法,对不同特征影响程度、影响方向等进行分析,定量评估输入数据在决策中的贡献,增强模型可解释性.研究结果表明煤矿安全应急物资储备中心选址组合模型性能优异,准确率、精确率、召回率、F1和AUC分别为0.976、0.966、0.989、0.977、0.996,可为选址决策提供有力支持,模型可解释分析也能够为煤矿安全应急物资储备中心选址提供科学参考. |
|---|---|
| AbstractList | TD77; 煤矿安全应急物资储备中心选址优化是推动煤矿安全应急体系建设的重要基础,为提高煤矿安全应急物资储备中心选址的准确度和合理性,提出利用人口因素、交通因素、经济因素和自然因素,建立融合多源空间数据的煤矿安全应急物资储备中心选址机器学习组合模型,提高煤矿安全应急物资储备中心选址的准确度和科学性.利用ArcGIS分别通过渔网划分、空间链接和投影等任务对多源空间数据进行处理,并利用SMOTEENN算法避免数据不均衡的负面影响,从而构建适用于机器学习模型分析的数据集.通过对不同机器学习算法、不同特征选择方法以及不同参数寻优方法进行比较分析,得出XGBoost机器学习算法、Boruta算法和遗传算法在对煤矿安全应急物资储备中心选址分析中,相较于其他机器学习算法、特征选择方法和参数寻优方法其表现更为优异.故基于各自优势,得到煤矿安全应急物资储备中心选址的机器学习组合模型.最后引入SHAP分析方法,对不同特征影响程度、影响方向等进行分析,定量评估输入数据在决策中的贡献,增强模型可解释性.研究结果表明煤矿安全应急物资储备中心选址组合模型性能优异,准确率、精确率、召回率、F1和AUC分别为0.976、0.966、0.989、0.977、0.996,可为选址决策提供有力支持,模型可解释分析也能够为煤矿安全应急物资储备中心选址提供科学参考. |
| Abstract_FL | The site selection optimization of coal mine safety emergency reserve center is an important foundation for pro-moting the construction of coal mine safety emergency response system.In order to improve the accuracy and reasonable-ness of coal mine safety emergency reserve center site selection,it is proposed to establish a machine learning combina-tion model for coal mine safety emergency reserve center site selection,integrating multi-source spatial data by using demographic,transportation,economic and natural factors to improve the accuracy and scientificity of coal mine safety emergency reserve center site selection.The accuracy and scientificity of coal mine safety emergency reserve center site selection are improved.Firstly,the ArcGIS is used to process multi-source spatial data through tasks such as fishing net di-vision,spatial linking and projection respectively,and the SMOTEENN algorithm is utilized to avoid the negative impact of data imbalance,so as to construct the dataset applicable to the analysis of machine learning model.Secondly,by com-paring and analyzing different machine learning algorithms,different feature selection methods and different parameter optimization methods,it is concluded that the XGBoost machine learning algorithm,the Boruta algorithm and genetic al-gorithm have better performance than other machine learning algorithms,feature selection methods and parameter optim-ization methods in the site selection analysis of coal mine safety and emergency reserve center.Therefore,based on the ad-vantages of each algorithm,this paper obtains a combined machine learning model for coal mine safety emergency re-serve center site selection.Finally,the SHAP analysis is introduced to analyze the influence degree and direction of differ-ent features to quantitatively assess the contribution of input data in decision-making and enhance the interpretability of the model.The results show that the combined model of coal mine safety emergency reserve center siting has an excellent performance,with 0.976,0.966,0.989,0.977,0.996 in accuracy,precision,recall,F1 value and Auc,respectively,which can provide a powerful support for siting decision-making,and the model interpretable analysis can also provide a scientif-ic reference for coal mine safety emergency reserve center siting. |
| Author | 张宇飞 刘战豫 |
| AuthorAffiliation | 河南理工大学工商管理学院能源经济研究中心,河南焦作 454000;太行发展研究院,河南焦作 454000%河南理工大学工商管理学院能源经济研究中心,河南焦作 454000 |
| AuthorAffiliation_xml | – name: 河南理工大学工商管理学院能源经济研究中心,河南焦作 454000;太行发展研究院,河南焦作 454000%河南理工大学工商管理学院能源经济研究中心,河南焦作 454000 |
| Author_FL | ZHANG Yufei LIU Zhanyu |
| Author_FL_xml | – sequence: 1 fullname: LIU Zhanyu – sequence: 2 fullname: ZHANG Yufei |
| Author_xml | – sequence: 1 fullname: 刘战豫 – sequence: 2 fullname: 张宇飞 |
| BookMark | eNotjz9Lw0AcQG-oYFv9Cm6Oib_7k0tutFWrUHBQwS3chUSMegFT0TFoi2Rx6iRIHSq0g26KZOmX6Xn6LVR0ett7vAaq6UzHCK1gcDElxFtL3UifHLtpFOUuAcJcYL5fQ3UgHnWEEHQRNfI8BaCMcq-ONsyomld3e61OeNhpZVnes_d9Oxjb0cy8lGYwMdXwo3iy5fTztW-uJ2Z8O39_NrObr6I0D4V9HNrp2xJaSORpHi__s4kOtjb329tOd7ez017vOjkG4A5VgmLGfOIBC5gvhB_jGICpgAjBhZASU-VjLIhkinBIJI8JCyjlTGHBFW2i1T_vpdSJ1Edhml2c659ieNa7Ur-7EADm9BvXqmDp |
| ClassificationCodes | TD77 |
| ContentType | Journal Article |
| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| Copyright_xml | – notice: Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| DBID | 2B. 4A8 92I 93N PSX TCJ |
| DOI | 10.13225/j.cnki.jccs.2024.0477 |
| DatabaseName | Wanfang Data Journals - Hong Kong WANFANG Data Centre Wanfang Data Journals 万方数据期刊 - 香港版 China Online Journals (COJ) China Online Journals (COJ) |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| DocumentTitle_FL | Site selection of coal mine safety emergency material reserve center based on SBG XGBoost |
| EndPage | 3545 |
| ExternalDocumentID | mtxb202408016 |
| GrantInformation_xml | – fundername: (河南省高等学校重点科研项目); (河南省专业学位研究生精品教学案例资助项目); (河南理工大学研究生质量工程项目) funderid: (河南省高等学校重点科研项目); (河南省专业学位研究生精品教学案例资助项目); (河南理工大学研究生质量工程项目) |
| GroupedDBID | -02 2B. 4A8 5XA 5XC 92H 92I 93N ABJNI ACGFS ALMA_UNASSIGNED_HOLDINGS CCEZO CDRFL CW9 FIJ GROUPED_DOAJ IPNFZ PSX RIG TCJ TGT U1G U5L |
| ID | FETCH-LOGICAL-s1006-3b9314472504847997e1e004b8299699aa13b71192a4b260fa6e2483364b196b3 |
| ISSN | 0253-9993 |
| IngestDate | Thu May 29 04:05:51 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Issue | 8 |
| Keywords | 煤矿安全 emergency material reserve center 机器学习 选址 特征选择 coal mine safety site selection machine learning 应急物资储备中心 feature selection |
| Language | Chinese |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-s1006-3b9314472504847997e1e004b8299699aa13b71192a4b260fa6e2483364b196b3 |
| PageCount | 11 |
| ParticipantIDs | wanfang_journals_mtxb202408016 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-08-01 |
| PublicationDateYYYYMMDD | 2024-08-01 |
| PublicationDate_xml | – month: 08 year: 2024 text: 2024-08-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationTitle | 煤炭学报 |
| PublicationTitle_FL | Journal of China Coal Society |
| PublicationYear | 2024 |
| Publisher | 河南理工大学工商管理学院能源经济研究中心,河南焦作 454000 太行发展研究院,河南焦作 454000%河南理工大学工商管理学院能源经济研究中心,河南焦作 454000 |
| Publisher_xml | – name: 河南理工大学工商管理学院能源经济研究中心,河南焦作 454000 – name: 太行发展研究院,河南焦作 454000%河南理工大学工商管理学院能源经济研究中心,河南焦作 454000 |
| SSID | ssj0034365 ssib048394982 ssib023167597 ssib012291397 ssib051374103 ssib001105247 ssib046784615 |
| Score | 2.483416 |
| Snippet | TD77; 煤矿安全应急物资储备中心选址优化是推动煤矿安全应急体系建设的重要基础,为提高煤矿安全应急物资储备中心选址的准确度和合理性,提出利用人口因素、交通因素、经济因... |
| SourceID | wanfang |
| SourceType | Aggregation Database |
| StartPage | 3535 |
| Title | 基于SBG_XGBoost的煤矿安全应急物资储备中心选址研究 |
| URI | https://d.wanfangdata.com.cn/periodical/mtxb202408016 |
| Volume | 49 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals issn: 0253-9993 databaseCode: DOA dateStart: 20100101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.doaj.org/ omitProxy: true ssIdentifier: ssj0034365 providerName: Directory of Open Access Journals |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV07bxQxELaS0ECBeIpnlAJX6MKuH2u79N7tJUKChkRKF-3u7fGSLhJJmlQRJEJpqCIKJBSKICUFdCCUJn8my8G_YMa73PmUk3g0ltc7Ho9nbM_n1XpMyJ1uR5k0MmEjF1kOGxQYxqZgeSMvmOxy8E8djYeTHzyM5hfF_SW5NDH51vtraX0tm803xp4r-R-rQhnYFU_J_oNlB0yhAPJgX0jBwpD-lY1pIqlp09jSRGCqk0fx3PLSXLyCRzkSRQ2UCcxoSa3LIHkb69mEaoMZfKUxAwwM0ET484OVrpahFmg0jaXjAymriYGbVq5ZTW3LVW9TzWlisHrF2bQwD3xs4Dgr5BZHPh4ekQ2ZO1aQ2shJYkGS32PCta-p0e5NlQHRQmpjnyRuYnt1DxUKZDk1if95g4nBz3XVgESOMaMxd220qFFOV9CRyLFUTiFOTqs8Cb1XRjpihc3acKR6TWyoAVkYCg2KiluuUYtkQBzHVLddCfBJTittjKpZc7zYYClb0UNJ8y4GPwwCb8FnkjcAsHPfO1UBXetZqD1Xw2UV56WGLVxWYTlPuURcsZ1PzHvPn84-y3MMUs_EbCDq-3NGw43DIM-YC3wHm4FJcoaBtwy8TxUOZgMoZ8NtaMgYBpcdPDOMriCHz-CLAeUOYacAUC7MMKaSDDng2mCAoLjg7grYgT7qk_3Yk3tj--FO3PW6ae-xBw4XLpDz9a5uxlZT9CKZ2HhyiZzzYn1eJq1y7-jk6I03Qfvvtvrb-_294_LzTrl9UB7tft_82N85_PFlq3x5UO6_Pvn2qTx-9XNzp3y_2f-w2z_8eoUstpOF5nyjvsKksRritzqeGR4KoTBQIOBAY1QRFrCCZRpgYGRMmoY8UyFss1KRsSjoplHBQD88Ehn4xoxfJVO9lV5xjcx0Oh28fIKnQVaIjtIpYJxIhakMukVuOL9OpmsdLNdL1OryiDFv_IngJjk7nIG3yNTai_XiNkDutWza2f8X7SmcJw |
| linkProvider | Directory of Open Access Journals |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=%E5%9F%BA%E4%BA%8ESBG_XGBoost%E7%9A%84%E7%85%A4%E7%9F%BF%E5%AE%89%E5%85%A8%E5%BA%94%E6%80%A5%E7%89%A9%E8%B5%84%E5%82%A8%E5%A4%87%E4%B8%AD%E5%BF%83%E9%80%89%E5%9D%80%E7%A0%94%E7%A9%B6&rft.jtitle=%E7%85%A4%E7%82%AD%E5%AD%A6%E6%8A%A5&rft.au=%E5%88%98%E6%88%98%E8%B1%AB&rft.au=%E5%BC%A0%E5%AE%87%E9%A3%9E&rft.date=2024-08-01&rft.pub=%E6%B2%B3%E5%8D%97%E7%90%86%E5%B7%A5%E5%A4%A7%E5%AD%A6%E5%B7%A5%E5%95%86%E7%AE%A1%E7%90%86%E5%AD%A6%E9%99%A2%E8%83%BD%E6%BA%90%E7%BB%8F%E6%B5%8E%E7%A0%94%E7%A9%B6%E4%B8%AD%E5%BF%83%2C%E6%B2%B3%E5%8D%97%E7%84%A6%E4%BD%9C+454000&rft.issn=0253-9993&rft.volume=49&rft.issue=8&rft.spage=3535&rft.epage=3545&rft_id=info:doi/10.13225%2Fj.cnki.jccs.2024.0477&rft.externalDocID=mtxb202408016 |
| thumbnail_s | http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fwww.wanfangdata.com.cn%2Fimages%2FPeriodicalImages%2Fmtxb%2Fmtxb.jpg |