Gas Outburst Prediction Model Using Improved Entropy Weight Grey Correlation Analysis and IPSO-LSSVM
This paper investigates the problem of gas outburst prediction in the working face of coal mine. Firstly, based on a comprehensive analysis of influence factors of gas outburst, an improved entropy weight algorithm is introduced into a grey correlation analysis algorithm; thus, the reasonable weight...
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| Veröffentlicht in: | Mathematical problems in engineering 2020, Vol.2020 (2020), p.1-10 |
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| creator | Liu, Haibo Wang, Fuzhong Dong, Yujie |
| description | This paper investigates the problem of gas outburst prediction in the working face of coal mine. Firstly, based on a comprehensive analysis of influence factors of gas outburst, an improved entropy weight algorithm is introduced into a grey correlation analysis algorithm; thus, the reasonable weights and correlation order of the influencing factors are obtained to improve the objectivity of the evaluation. The main controlling factors obtained are used as the input of the prediction model. Secondly, by utilizing the improved particle swarm optimization (IPSO), the penalty factor and kernel parameter of least square support vector machine (LSSVM) are optimized to enhance the global search ability and avoid the occurrence of the local optimal solutions, and a new prediction model of gas outburst based on IPSO-LSSVM is established. At last, the prediction model is applied in the tunneling heading face 14141 of Jiuli Hill mine in Jiaozuo City, China. The case study demonstrates that the prediction accuracy of the proposed model is 92%, which is improved compared with that of the SVM model and GA-LSSVM model. |
| doi_str_mv | 10.1155/2020/8863425 |
| format | Article |
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Firstly, based on a comprehensive analysis of influence factors of gas outburst, an improved entropy weight algorithm is introduced into a grey correlation analysis algorithm; thus, the reasonable weights and correlation order of the influencing factors are obtained to improve the objectivity of the evaluation. The main controlling factors obtained are used as the input of the prediction model. Secondly, by utilizing the improved particle swarm optimization (IPSO), the penalty factor and kernel parameter of least square support vector machine (LSSVM) are optimized to enhance the global search ability and avoid the occurrence of the local optimal solutions, and a new prediction model of gas outburst based on IPSO-LSSVM is established. At last, the prediction model is applied in the tunneling heading face 14141 of Jiuli Hill mine in Jiaozuo City, China. The case study demonstrates that the prediction accuracy of the proposed model is 92%, which is improved compared with that of the SVM model and GA-LSSVM model.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2020/8863425</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Accuracy ; Algorithms ; Artificial intelligence ; Coal mines ; Coal mining ; Correlation analysis ; Data mining ; Entropy ; Fuzzy logic ; Machine learning ; Mathematical problems ; Methods ; Model accuracy ; Neural networks ; Particle swarm optimization ; Prediction models ; Set theory ; Support vector machines ; Wavelet transforms ; Weight</subject><ispartof>Mathematical problems in engineering, 2020, Vol.2020 (2020), p.1-10</ispartof><rights>Copyright © 2020 Haibo Liu et al.</rights><rights>Copyright © 2020 Haibo Liu et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c360t-98cae2094863bc0462c1dab9c3db3a6012cb76a4c324cabee87986402cc2856d3</citedby><cites>FETCH-LOGICAL-c360t-98cae2094863bc0462c1dab9c3db3a6012cb76a4c324cabee87986402cc2856d3</cites><orcidid>0000-0002-1562-8816 ; 0000-0002-1561-3913 ; 0000-0002-5481-7249</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,4024,27923,27924,27925</link.rule.ids></links><search><contributor>Syed Ali, M</contributor><contributor>M Syed Ali</contributor><creatorcontrib>Liu, Haibo</creatorcontrib><creatorcontrib>Wang, Fuzhong</creatorcontrib><creatorcontrib>Dong, Yujie</creatorcontrib><title>Gas Outburst Prediction Model Using Improved Entropy Weight Grey Correlation Analysis and IPSO-LSSVM</title><title>Mathematical problems in engineering</title><description>This paper investigates the problem of gas outburst prediction in the working face of coal mine. Firstly, based on a comprehensive analysis of influence factors of gas outburst, an improved entropy weight algorithm is introduced into a grey correlation analysis algorithm; thus, the reasonable weights and correlation order of the influencing factors are obtained to improve the objectivity of the evaluation. The main controlling factors obtained are used as the input of the prediction model. Secondly, by utilizing the improved particle swarm optimization (IPSO), the penalty factor and kernel parameter of least square support vector machine (LSSVM) are optimized to enhance the global search ability and avoid the occurrence of the local optimal solutions, and a new prediction model of gas outburst based on IPSO-LSSVM is established. At last, the prediction model is applied in the tunneling heading face 14141 of Jiuli Hill mine in Jiaozuo City, China. The case study demonstrates that the prediction accuracy of the proposed model is 92%, which is improved compared with that of the SVM model and GA-LSSVM model.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Artificial intelligence</subject><subject>Coal mines</subject><subject>Coal mining</subject><subject>Correlation analysis</subject><subject>Data mining</subject><subject>Entropy</subject><subject>Fuzzy logic</subject><subject>Machine learning</subject><subject>Mathematical problems</subject><subject>Methods</subject><subject>Model accuracy</subject><subject>Neural networks</subject><subject>Particle swarm optimization</subject><subject>Prediction models</subject><subject>Set theory</subject><subject>Support vector machines</subject><subject>Wavelet transforms</subject><subject>Weight</subject><issn>1024-123X</issn><issn>1563-5147</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>RHX</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqF0M9PwjAUB_DGaCKiN8-miUed9PfKkRBEEggkiHpburZAydiw3TT77x2OxKOn9w6f9_LeF4BbjJ4w5rxHEEE9KQVlhJ-BDuaCRhyz-LzpEWERJvTjElyFsEOIYI5lB5ixCnBelWnlQwkX3hqnS1fkcFYYm8FVcPkGTvYHX3xZA0d56YtDDd-t22xLOPa2hsPCe5up36FBrrI6uABVbuBksZxH0-XybXYNLtYqC_bmVLtg9Tx6Hb5E0_l4MhxMI00FKqO-1MoS1GfNB6lGTBCNjUr7mpqUKoEw0WksFNOUMK1Sa2Xcl4IhojWRXBjaBfft3ubcz8qGMtkVlW9uCglhghMiEWONemyV9kUI3q6Tg3d75esEo-SYY3LMMTnl2PCHlm9dbtS3-0_ftdo2xq7VnyYIx0LSH4Yqe3E</recordid><startdate>2020</startdate><enddate>2020</enddate><creator>Liu, Haibo</creator><creator>Wang, Fuzhong</creator><creator>Dong, Yujie</creator><general>Hindawi Publishing Corporation</general><general>Hindawi</general><general>Hindawi Limited</general><scope>ADJCN</scope><scope>AHFXO</scope><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KR7</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><orcidid>https://orcid.org/0000-0002-1562-8816</orcidid><orcidid>https://orcid.org/0000-0002-1561-3913</orcidid><orcidid>https://orcid.org/0000-0002-5481-7249</orcidid></search><sort><creationdate>2020</creationdate><title>Gas Outburst Prediction Model Using Improved Entropy Weight Grey Correlation Analysis and IPSO-LSSVM</title><author>Liu, Haibo ; Wang, Fuzhong ; Dong, Yujie</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c360t-98cae2094863bc0462c1dab9c3db3a6012cb76a4c324cabee87986402cc2856d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Artificial intelligence</topic><topic>Coal mines</topic><topic>Coal mining</topic><topic>Correlation analysis</topic><topic>Data mining</topic><topic>Entropy</topic><topic>Fuzzy logic</topic><topic>Machine learning</topic><topic>Mathematical problems</topic><topic>Methods</topic><topic>Model accuracy</topic><topic>Neural networks</topic><topic>Particle swarm optimization</topic><topic>Prediction models</topic><topic>Set theory</topic><topic>Support vector machines</topic><topic>Wavelet transforms</topic><topic>Weight</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Haibo</creatorcontrib><creatorcontrib>Wang, Fuzhong</creatorcontrib><creatorcontrib>Dong, Yujie</creatorcontrib><collection>الدوريات العلمية والإحصائية - e-Marefa Academic and Statistical Periodicals</collection><collection>معرفة - المحتوى العربي الأكاديمي المتكامل - e-Marefa Academic Complete</collection><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access Journals</collection><collection>CrossRef</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>Middle East & Africa Database</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>Mathematical problems in engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Haibo</au><au>Wang, Fuzhong</au><au>Dong, Yujie</au><au>Syed Ali, M</au><au>M Syed Ali</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Gas Outburst Prediction Model Using Improved Entropy Weight Grey Correlation Analysis and IPSO-LSSVM</atitle><jtitle>Mathematical problems in engineering</jtitle><date>2020</date><risdate>2020</risdate><volume>2020</volume><issue>2020</issue><spage>1</spage><epage>10</epage><pages>1-10</pages><issn>1024-123X</issn><eissn>1563-5147</eissn><abstract>This paper investigates the problem of gas outburst prediction in the working face of coal mine. Firstly, based on a comprehensive analysis of influence factors of gas outburst, an improved entropy weight algorithm is introduced into a grey correlation analysis algorithm; thus, the reasonable weights and correlation order of the influencing factors are obtained to improve the objectivity of the evaluation. The main controlling factors obtained are used as the input of the prediction model. Secondly, by utilizing the improved particle swarm optimization (IPSO), the penalty factor and kernel parameter of least square support vector machine (LSSVM) are optimized to enhance the global search ability and avoid the occurrence of the local optimal solutions, and a new prediction model of gas outburst based on IPSO-LSSVM is established. At last, the prediction model is applied in the tunneling heading face 14141 of Jiuli Hill mine in Jiaozuo City, China. 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| subjects | Accuracy Algorithms Artificial intelligence Coal mines Coal mining Correlation analysis Data mining Entropy Fuzzy logic Machine learning Mathematical problems Methods Model accuracy Neural networks Particle swarm optimization Prediction models Set theory Support vector machines Wavelet transforms Weight |
| title | Gas Outburst Prediction Model Using Improved Entropy Weight Grey Correlation Analysis and IPSO-LSSVM |
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