Discriminant analysis of mine water inrush sources with multi-aquifer based on multivariate statistical analysis
Accurate and effective identification of the source mine water inrush water is vital for warning system implementation and post-disaster rescue decision-making and is crucial for mine water disaster prevention plans. To fully excavate the hydrogeological information carried by the water samples of d...
Gespeichert in:
Veröffentlicht in: | Environmental earth sciences 2021-02, Vol.80 (4), Article 144 |
---|---|
Hauptverfasser: | , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | |
---|---|
container_issue | 4 |
container_start_page | |
container_title | Environmental earth sciences |
container_volume | 80 |
creator | Bi, Yaoshan Wu, Jiwen Zhai, Xiaorong Wang, Guangtao Shen, Shuhao Qing, Xianbin |
description | Accurate and effective identification of the source mine water inrush water is vital for warning system implementation and post-disaster rescue decision-making and is crucial for mine water disaster prevention plans. To fully excavate the hydrogeological information carried by the water samples of different water sources, and fully consider the influence of the correlation between the ions and the existence of multi-collinearity on the discrimination results, so as to improve the accuracy of the inrush water source discrimination, this study conducted multivariate statistical analysis and discriminant analysis on 37 water samples of three types of water sources in Xutuan coal mine, and established the Fisher discriminant model for mine water inrush sources based on fuzzy cluster analysis and factor analysis. The discriminant accuracy of the model was tested using re-substitution and cross-validation, and compared with the discriminant result of traditional Fisher discriminant model. The results showed that the discriminant accuracies of the re-substitution and cross-validation were 91.9% and 89.2%, respectively, while the discrimination accuracy of cross-validation of the traditional Fisher discrimination model was 86.5%. The discrimination accuracy of this model was higher than that of the traditional Fisher discrimination model. Therefore, the Fisher discriminant model for mine water inrush sources based on fuzzy cluster analysis and factor analysis established in this study can improve the accuracy of a water source discrimination model, and can provide a useful reference for mine water disaster prevention. |
doi_str_mv | 10.1007/s12665-021-09450-8 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2487153605</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2487153605</sourcerecordid><originalsourceid>FETCH-LOGICAL-a342t-df0901850fd47a31c25935436c95411289008ac7efe5c0e0aa71e95dc0483d2f3</originalsourceid><addsrcrecordid>eNp9kE9LAzEQxYMoWGq_gKeA59VJstlNjlL_QsGLnkPMZm1Ku9tmspZ-e6Mr9eZcZhjeezx-hFwyuGYA9Q0yXlWyAM4K0KWEQp2QCVNVVVRc69PjreCczBBXkEcwoaGakO1dQBfDJnS2S9R2dn3AgLRvaX55urfJRxq6OOCSYj9E55HuQ1rSzbBOobC7IbRZ8W7RN7TvxvenjSEbKSabAqbg7PoYfUHOWrtGP_vdU_L2cP86fyoWL4_P89tFYUXJU9G0oIEpCW1T1lYwx6UWshSV07JkjCsNoKyrfeulAw_W1sxr2TgolWh4K6bkaszdxn43eExmlevnEmh4qWomRQUyq_iocrFHjL412wzDxoNhYL7hmhGuyXDND1yjskmMJszi7sPHv-h_XF_qOH5K</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2487153605</pqid></control><display><type>article</type><title>Discriminant analysis of mine water inrush sources with multi-aquifer based on multivariate statistical analysis</title><source>SpringerNature Journals</source><creator>Bi, Yaoshan ; Wu, Jiwen ; Zhai, Xiaorong ; Wang, Guangtao ; Shen, Shuhao ; Qing, Xianbin</creator><creatorcontrib>Bi, Yaoshan ; Wu, Jiwen ; Zhai, Xiaorong ; Wang, Guangtao ; Shen, Shuhao ; Qing, Xianbin</creatorcontrib><description>Accurate and effective identification of the source mine water inrush water is vital for warning system implementation and post-disaster rescue decision-making and is crucial for mine water disaster prevention plans. To fully excavate the hydrogeological information carried by the water samples of different water sources, and fully consider the influence of the correlation between the ions and the existence of multi-collinearity on the discrimination results, so as to improve the accuracy of the inrush water source discrimination, this study conducted multivariate statistical analysis and discriminant analysis on 37 water samples of three types of water sources in Xutuan coal mine, and established the Fisher discriminant model for mine water inrush sources based on fuzzy cluster analysis and factor analysis. The discriminant accuracy of the model was tested using re-substitution and cross-validation, and compared with the discriminant result of traditional Fisher discriminant model. The results showed that the discriminant accuracies of the re-substitution and cross-validation were 91.9% and 89.2%, respectively, while the discrimination accuracy of cross-validation of the traditional Fisher discrimination model was 86.5%. The discrimination accuracy of this model was higher than that of the traditional Fisher discrimination model. Therefore, the Fisher discriminant model for mine water inrush sources based on fuzzy cluster analysis and factor analysis established in this study can improve the accuracy of a water source discrimination model, and can provide a useful reference for mine water disaster prevention.</description><identifier>ISSN: 1866-6280</identifier><identifier>EISSN: 1866-6299</identifier><identifier>DOI: 10.1007/s12665-021-09450-8</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Accuracy ; Aquifers ; Biogeosciences ; Cluster analysis ; Coal mines ; Coal mining ; Collinearity ; Decision making ; Disaster relief ; Disasters ; Discriminant analysis ; Earth and Environmental Science ; Earth Sciences ; Emergency preparedness ; Environmental Science and Engineering ; Factor analysis ; Geochemistry ; Geology ; Hydrogeology ; Hydrology/Water Resources ; Mine drainage ; Mine waters ; Model accuracy ; Model testing ; Multivariate analysis ; Multivariate statistical analysis ; Original Article ; Prevention ; Statistical analysis ; Statistical methods ; Statistics ; Substitutes ; Terrestrial Pollution ; Warning systems ; Water analysis ; Water inrush ; Water sampling ; Water sources</subject><ispartof>Environmental earth sciences, 2021-02, Vol.80 (4), Article 144</ispartof><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021</rights><rights>The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-a342t-df0901850fd47a31c25935436c95411289008ac7efe5c0e0aa71e95dc0483d2f3</citedby><cites>FETCH-LOGICAL-a342t-df0901850fd47a31c25935436c95411289008ac7efe5c0e0aa71e95dc0483d2f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s12665-021-09450-8$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s12665-021-09450-8$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Bi, Yaoshan</creatorcontrib><creatorcontrib>Wu, Jiwen</creatorcontrib><creatorcontrib>Zhai, Xiaorong</creatorcontrib><creatorcontrib>Wang, Guangtao</creatorcontrib><creatorcontrib>Shen, Shuhao</creatorcontrib><creatorcontrib>Qing, Xianbin</creatorcontrib><title>Discriminant analysis of mine water inrush sources with multi-aquifer based on multivariate statistical analysis</title><title>Environmental earth sciences</title><addtitle>Environ Earth Sci</addtitle><description>Accurate and effective identification of the source mine water inrush water is vital for warning system implementation and post-disaster rescue decision-making and is crucial for mine water disaster prevention plans. To fully excavate the hydrogeological information carried by the water samples of different water sources, and fully consider the influence of the correlation between the ions and the existence of multi-collinearity on the discrimination results, so as to improve the accuracy of the inrush water source discrimination, this study conducted multivariate statistical analysis and discriminant analysis on 37 water samples of three types of water sources in Xutuan coal mine, and established the Fisher discriminant model for mine water inrush sources based on fuzzy cluster analysis and factor analysis. The discriminant accuracy of the model was tested using re-substitution and cross-validation, and compared with the discriminant result of traditional Fisher discriminant model. The results showed that the discriminant accuracies of the re-substitution and cross-validation were 91.9% and 89.2%, respectively, while the discrimination accuracy of cross-validation of the traditional Fisher discrimination model was 86.5%. The discrimination accuracy of this model was higher than that of the traditional Fisher discrimination model. Therefore, the Fisher discriminant model for mine water inrush sources based on fuzzy cluster analysis and factor analysis established in this study can improve the accuracy of a water source discrimination model, and can provide a useful reference for mine water disaster prevention.</description><subject>Accuracy</subject><subject>Aquifers</subject><subject>Biogeosciences</subject><subject>Cluster analysis</subject><subject>Coal mines</subject><subject>Coal mining</subject><subject>Collinearity</subject><subject>Decision making</subject><subject>Disaster relief</subject><subject>Disasters</subject><subject>Discriminant analysis</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Emergency preparedness</subject><subject>Environmental Science and Engineering</subject><subject>Factor analysis</subject><subject>Geochemistry</subject><subject>Geology</subject><subject>Hydrogeology</subject><subject>Hydrology/Water Resources</subject><subject>Mine drainage</subject><subject>Mine waters</subject><subject>Model accuracy</subject><subject>Model testing</subject><subject>Multivariate analysis</subject><subject>Multivariate statistical analysis</subject><subject>Original Article</subject><subject>Prevention</subject><subject>Statistical analysis</subject><subject>Statistical methods</subject><subject>Statistics</subject><subject>Substitutes</subject><subject>Terrestrial Pollution</subject><subject>Warning systems</subject><subject>Water analysis</subject><subject>Water inrush</subject><subject>Water sampling</subject><subject>Water sources</subject><issn>1866-6280</issn><issn>1866-6299</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kE9LAzEQxYMoWGq_gKeA59VJstlNjlL_QsGLnkPMZm1Ku9tmspZ-e6Mr9eZcZhjeezx-hFwyuGYA9Q0yXlWyAM4K0KWEQp2QCVNVVVRc69PjreCczBBXkEcwoaGakO1dQBfDJnS2S9R2dn3AgLRvaX55urfJRxq6OOCSYj9E55HuQ1rSzbBOobC7IbRZ8W7RN7TvxvenjSEbKSabAqbg7PoYfUHOWrtGP_vdU_L2cP86fyoWL4_P89tFYUXJU9G0oIEpCW1T1lYwx6UWshSV07JkjCsNoKyrfeulAw_W1sxr2TgolWh4K6bkaszdxn43eExmlevnEmh4qWomRQUyq_iocrFHjL412wzDxoNhYL7hmhGuyXDND1yjskmMJszi7sPHv-h_XF_qOH5K</recordid><startdate>20210201</startdate><enddate>20210201</enddate><creator>Bi, Yaoshan</creator><creator>Wu, Jiwen</creator><creator>Zhai, Xiaorong</creator><creator>Wang, Guangtao</creator><creator>Shen, Shuhao</creator><creator>Qing, Xianbin</creator><general>Springer Berlin Heidelberg</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7ST</scope><scope>7TG</scope><scope>7UA</scope><scope>7XB</scope><scope>88I</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>F1W</scope><scope>GNUQQ</scope><scope>H96</scope><scope>HCIFZ</scope><scope>KL.</scope><scope>L.G</scope><scope>M2P</scope><scope>PATMY</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>SOI</scope></search><sort><creationdate>20210201</creationdate><title>Discriminant analysis of mine water inrush sources with multi-aquifer based on multivariate statistical analysis</title><author>Bi, Yaoshan ; Wu, Jiwen ; Zhai, Xiaorong ; Wang, Guangtao ; Shen, Shuhao ; Qing, Xianbin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a342t-df0901850fd47a31c25935436c95411289008ac7efe5c0e0aa71e95dc0483d2f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Accuracy</topic><topic>Aquifers</topic><topic>Biogeosciences</topic><topic>Cluster analysis</topic><topic>Coal mines</topic><topic>Coal mining</topic><topic>Collinearity</topic><topic>Decision making</topic><topic>Disaster relief</topic><topic>Disasters</topic><topic>Discriminant analysis</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Emergency preparedness</topic><topic>Environmental Science and Engineering</topic><topic>Factor analysis</topic><topic>Geochemistry</topic><topic>Geology</topic><topic>Hydrogeology</topic><topic>Hydrology/Water Resources</topic><topic>Mine drainage</topic><topic>Mine waters</topic><topic>Model accuracy</topic><topic>Model testing</topic><topic>Multivariate analysis</topic><topic>Multivariate statistical analysis</topic><topic>Original Article</topic><topic>Prevention</topic><topic>Statistical analysis</topic><topic>Statistical methods</topic><topic>Statistics</topic><topic>Substitutes</topic><topic>Terrestrial Pollution</topic><topic>Warning systems</topic><topic>Water analysis</topic><topic>Water inrush</topic><topic>Water sampling</topic><topic>Water sources</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Bi, Yaoshan</creatorcontrib><creatorcontrib>Wu, Jiwen</creatorcontrib><creatorcontrib>Zhai, Xiaorong</creatorcontrib><creatorcontrib>Wang, Guangtao</creatorcontrib><creatorcontrib>Shen, Shuhao</creatorcontrib><creatorcontrib>Qing, Xianbin</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Environment Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Water Resources Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric & Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>ProQuest Central Student</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>SciTech Premium Collection</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Science Database</collection><collection>Environmental Science Database</collection><collection>Earth, Atmospheric & Aquatic Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>Environment Abstracts</collection><jtitle>Environmental earth sciences</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Bi, Yaoshan</au><au>Wu, Jiwen</au><au>Zhai, Xiaorong</au><au>Wang, Guangtao</au><au>Shen, Shuhao</au><au>Qing, Xianbin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Discriminant analysis of mine water inrush sources with multi-aquifer based on multivariate statistical analysis</atitle><jtitle>Environmental earth sciences</jtitle><stitle>Environ Earth Sci</stitle><date>2021-02-01</date><risdate>2021</risdate><volume>80</volume><issue>4</issue><artnum>144</artnum><issn>1866-6280</issn><eissn>1866-6299</eissn><abstract>Accurate and effective identification of the source mine water inrush water is vital for warning system implementation and post-disaster rescue decision-making and is crucial for mine water disaster prevention plans. To fully excavate the hydrogeological information carried by the water samples of different water sources, and fully consider the influence of the correlation between the ions and the existence of multi-collinearity on the discrimination results, so as to improve the accuracy of the inrush water source discrimination, this study conducted multivariate statistical analysis and discriminant analysis on 37 water samples of three types of water sources in Xutuan coal mine, and established the Fisher discriminant model for mine water inrush sources based on fuzzy cluster analysis and factor analysis. The discriminant accuracy of the model was tested using re-substitution and cross-validation, and compared with the discriminant result of traditional Fisher discriminant model. The results showed that the discriminant accuracies of the re-substitution and cross-validation were 91.9% and 89.2%, respectively, while the discrimination accuracy of cross-validation of the traditional Fisher discrimination model was 86.5%. The discrimination accuracy of this model was higher than that of the traditional Fisher discrimination model. Therefore, the Fisher discriminant model for mine water inrush sources based on fuzzy cluster analysis and factor analysis established in this study can improve the accuracy of a water source discrimination model, and can provide a useful reference for mine water disaster prevention.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><doi>10.1007/s12665-021-09450-8</doi></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1866-6280 |
ispartof | Environmental earth sciences, 2021-02, Vol.80 (4), Article 144 |
issn | 1866-6280 1866-6299 |
language | eng |
recordid | cdi_proquest_journals_2487153605 |
source | SpringerNature Journals |
subjects | Accuracy Aquifers Biogeosciences Cluster analysis Coal mines Coal mining Collinearity Decision making Disaster relief Disasters Discriminant analysis Earth and Environmental Science Earth Sciences Emergency preparedness Environmental Science and Engineering Factor analysis Geochemistry Geology Hydrogeology Hydrology/Water Resources Mine drainage Mine waters Model accuracy Model testing Multivariate analysis Multivariate statistical analysis Original Article Prevention Statistical analysis Statistical methods Statistics Substitutes Terrestrial Pollution Warning systems Water analysis Water inrush Water sampling Water sources |
title | Discriminant analysis of mine water inrush sources with multi-aquifer based on multivariate statistical analysis |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-02T01%3A13%3A49IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Discriminant%20analysis%20of%20mine%20water%20inrush%20sources%20with%20multi-aquifer%20based%20on%20multivariate%20statistical%20analysis&rft.jtitle=Environmental%20earth%20sciences&rft.au=Bi,%20Yaoshan&rft.date=2021-02-01&rft.volume=80&rft.issue=4&rft.artnum=144&rft.issn=1866-6280&rft.eissn=1866-6299&rft_id=info:doi/10.1007/s12665-021-09450-8&rft_dat=%3Cproquest_cross%3E2487153605%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2487153605&rft_id=info:pmid/&rfr_iscdi=true |