Research on coal and gas outburst prediction and sensitivity analysis of influencing factors based on PCA-ICA-BP

Research on coal and gas outburst prediction and sensitivity analysis of influencing factors based on PCA-ICA-BP | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Research on coal and gas outburst prediction and sensitivity analysis of influencing factors based on PCA-ICA-BP Jinzhang Jia, Yinghuan Xing This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4005960/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract There is a complex nonlinear mapping relationship between the phenomenon of coal and gas outburst and various influencing factors. In order to achieve accurate prediction of the danger of coal and gas outburst, traditional biomimetic algorithms have the disadvantage of easily falling into local optima and the problem of multiple correlations between the influencing factors of coal and gas outburst, This article constructs and uses Matlab to write an Imperial Competition Optimization BP Neural Network (PCA-ICA-BP) algorithm based on principal component analysis to predict the risk of coal and gas outburst. The results showed that after principal component analysis, five principal components were obtained, with a cumulative contribution rate of 89.397%; The average relative errors of PCA-ICA-BP, PCA-BP, ICA-BP, BP, PSO-BP, and GA-BP prediction models are 1.37%, 2.98%, 3.27%, 15%, 7.58%, and 8.65%, respectively. The convergence speed of PCA-ICA-BP model is 4.38 times that of BP model, and the convergence accuracy has been improved by 90.8%. The Sobol index method was used to explore the sensitivity of relevant influencing factors to coal and gas outburst. The sensitivity indicators of coal and gas outburst were sorted based on the main effect and total effect indices. The influencing factors related to gas geology have high sensitivity, and the mine is a gas dominated outburst mine. Sensitivity analysis can provide reference for the deployment of coal and gas outburst sensors in coal mines. Physical sciences/Engineering/Civil engineering Physical sciences/Engineering/Energy infrastructure coal and gas outburst Principal component analysis Imperial competition algorithm BP neural network Sobol Sensitivity Analysis Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4005960","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":285881917,"identity":"480f9bba-b19e-406a-9415-fc849574a972","order_by":0,"name":"Jinzhang Jia","email":"","orcid":"","institution":"Liaoning Technical University","correspondingAuthor":false,"prefix":"","firstName":"Jinzhang","middleName":"","lastName":"Jia","suffix":""},{"id":285881918,"identity":"5a8455fd-36ac-4d96-8e26-6764fc12687c","order_by":1,"name":"Yinghuan Xing","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYBACNobjBx//qLDh4Zc/fIA4LXyMZ5KNGc6kyUjOYEsgTosc8wEzYca2wzYGN3gMiHQY24E05gK2wzySs3s+3njDYCen20BIC8/BY49n8KTz8Muc3Ww5hyHZ2OwAIS0SB9INeCSseSQbcrdJ8zAcSNxGUIv8AzMJHgNmHoMDOc+I1MJwwEyaJ8GZx+BGDhuxWs4kG844kMYj2XPM2HKOARF+kW84fvDBx3829vzszQ9vvKmwkyOoBQVIEBs1yFpI1TEKRsEoGAUjAgAAAupCDi4KnqoAAAAASUVORK5CYII=","orcid":"","institution":"Liaoning Technical University","correspondingAuthor":true,"prefix":"","firstName":"Yinghuan","middleName":"","lastName":"Xing","suffix":""}],"badges":[],"createdAt":"2024-03-02 08:46:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4005960/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4005960/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57819106,"identity":"2c387f49-b2f3-4f99-b0d7-8b7948924ee7","added_by":"auto","created_at":"2024-06-06 05:25:54","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":560344,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4005960/v1_covered_83876d1b-5e24-49e8-8647-2cd03bd65ada.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on coal and gas outburst prediction and sensitivity analysis of influencing factors based on PCA-ICA-BP","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"coal and gas outburst, Principal component analysis, Imperial competition algorithm, BP neural network, Sobol Sensitivity Analysis","lastPublishedDoi":"10.21203/rs.3.rs-4005960/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4005960/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThere is a complex nonlinear mapping relationship between the phenomenon of coal and gas outburst and various influencing factors. In order to achieve accurate prediction of the danger of coal and gas outburst, traditional biomimetic algorithms have the disadvantage of easily falling into local optima and the problem of multiple correlations between the influencing factors of coal and gas outburst, This article constructs and uses Matlab to write an Imperial Competition Optimization BP Neural Network (PCA-ICA-BP) algorithm based on principal component analysis to predict the risk of coal and gas outburst. The results showed that after principal component analysis, five principal components were obtained, with a cumulative contribution rate of 89.397%; The average relative errors of PCA-ICA-BP, PCA-BP, ICA-BP, BP, PSO-BP, and GA-BP prediction models are 1.37%, 2.98%, 3.27%, 15%, 7.58%, and 8.65%, respectively. The convergence speed of PCA-ICA-BP model is 4.38 times that of BP model, and the convergence accuracy has been improved by 90.8%. The Sobol index method was used to explore the sensitivity of relevant influencing factors to coal and gas outburst. The sensitivity indicators of coal and gas outburst were sorted based on the main effect and total effect indices. The influencing factors related to gas geology have high sensitivity, and the mine is a gas dominated outburst mine. Sensitivity analysis can provide reference for the deployment of coal and gas outburst sensors in coal mines.\u003c/p\u003e","manuscriptTitle":"Research on coal and gas outburst prediction and sensitivity analysis of influencing factors based on PCA-ICA-BP","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-01 05:44:12","doi":"10.21203/rs.3.rs-4005960/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2adfc05e-3dd9-4c3d-a5c9-64ea8d32356e","owner":[],"postedDate":"April 1st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":30091101,"name":"Physical sciences/Engineering/Civil engineering"},{"id":30091102,"name":"Physical sciences/Engineering/Energy infrastructure"}],"tags":[],"updatedAt":"2024-06-06T05:09:46+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-01 05:44:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4005960","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4005960","identity":"rs-4005960","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}