Prediction of Gas Emission based on GM (0, N) Model

Prediction of Gas Emission based on GM (0, N) Model | 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 Prediction of Gas Emission based on GM (0, N) Model Liyang Bai, Hui Geng, Guangming Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6739010/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract In order to improve the prediction accuracy of gas emission in mining working face and ensure coal mine safety production. A prediction model of gas emission based on grey system theory is proposed.11 indexes such as gas content, coal seam depth, coal seam thickness, coal seam dip angle and inclined length of working face are selected as the influencing factors of gas emission.The weight of each factor is determined by grey correlation analysis. Combined with the field measured data, three grey prediction models for predicting gas emission are determined.After a posterior difference test, the accuracy of GM (0,12) model is excellent.By comparing the predicted data of the model with the actual data, it shows that the GM (0,N) model has good forecasting results.At the same time, in order to prove the advantages of GM (0,N) model, the prediction results are compared with those of multiple linear regression model.The prediction results of GM (0,N) model and multiple linear regression model are compared.The prediction results show that the relative error of GM (0,12) model is 0.799%,the relative error of multiple linear regression model is 3.643%.It shows that GM (0,12) model can better predict gas emission. Physical sciences/Energy science and technology/Fossil fuels/Coal Physical sciences/Energy science and technology/Fossil fuels/Natural gas grey theory GM(0 N) model grey correlation degree gas emission quantity prediction multiple linear regression Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 02 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 18 Jun, 2025 Reviews received at journal 17 Jun, 2025 Reviewers agreed at journal 17 Jun, 2025 Reviews received at journal 17 Jun, 2025 Reviewers agreed at journal 06 Jun, 2025 Reviewers agreed at journal 04 Jun, 2025 Reviewers invited by journal 02 Jun, 2025 Editor assigned by journal 30 May, 2025 Editor invited by journal 30 May, 2025 Submission checks completed at journal 27 May, 2025 First submitted to journal 27 May, 2025 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. 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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-6739010","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":466337178,"identity":"190545a9-a96d-4a45-acfc-69a3733d8ccd","order_by":0,"name":"Liyang Bai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYBAC++PNBx98MJDg4WdvIFbPmWPJhjMKbOQkew4Qq+VGjpowx4c0Y4MbCUTqYOw5w8bMYHA4ccPNxxtvMNTYRBPUwszee+xxAVDLzNtpxRYMx9JyGwhpYeM5l248A6il73aOmQRjw2HCWngkcsykeYBaGm6eIVKLBERLmrHADR4itRjwgALZABTIQL8kEOMXA3ZQVP4BReXhjTc+1NgQ1oKiXSKBFOUQLaTqGAWjYBSMgpEBAAZCQ9XT391qAAAAAElFTkSuQmCC","orcid":"","institution":"Qingdao University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Liyang","middleName":"","lastName":"Bai","suffix":""},{"id":466337179,"identity":"edae2940-e967-409d-884a-0f0395cf10a8","order_by":1,"name":"Hui Geng","email":"","orcid":"","institution":"Xinjiang University","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Geng","suffix":""},{"id":466337180,"identity":"c4f805ca-18c9-4323-ad27-3e58141bb54e","order_by":2,"name":"Guangming Yu","email":"","orcid":"","institution":"Qingdao University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Guangming","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2025-05-24 12:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6739010/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6739010/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-09163-z","type":"published","date":"2025-07-02T15:57:50+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86179079,"identity":"a2ef0760-f317-4edb-849d-e7473497c2ad","added_by":"auto","created_at":"2025-07-07 16:15:30","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":477265,"visible":true,"origin":"","legend":"","description":"","filename":"PredictionofGasEmissionbasedonGM0NModel.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6739010/v1_covered_5ec9acb6-672f-43d5-82e5-13c2f335a5d4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prediction of Gas Emission based on GM (0, N) Model","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"grey theory, GM(0,N) model, grey correlation degree, gas emission quantity, prediction, multiple linear regression","lastPublishedDoi":"10.21203/rs.3.rs-6739010/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6739010/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn order to improve the prediction accuracy of gas emission in mining working face and ensure coal mine safety production. A prediction model of gas emission based on grey system theory is proposed.11 indexes such as gas content, coal seam depth, coal seam thickness, coal seam dip angle and inclined length of working face are selected as the influencing factors of gas emission.The weight of each factor is determined by grey correlation analysis. Combined with the field measured data, three grey prediction models for predicting gas emission are determined.After a posterior difference test, the accuracy of GM (0,12) model is excellent.By comparing the predicted data of the model with the actual data, it shows that the GM (0,N) model has good forecasting results.At the same time, in order to prove the advantages of GM (0,N) model, the prediction results are compared with those of multiple linear regression model.The prediction results of GM (0,N) model and multiple linear regression model are compared.The prediction results show that the relative error of GM (0,12) model is 0.799%,the relative error of multiple linear regression model is 3.643%.It shows that GM (0,12) model can better predict gas emission.\u003c/p\u003e","manuscriptTitle":"Prediction of Gas Emission based on GM (0, N) Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-05 15:39:03","doi":"10.21203/rs.3.rs-6739010/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-18T10:05:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-18T01:20:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"304123303584356497904282620320308141695","date":"2025-06-18T00:41:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-17T14:32:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"276410959746850051946928228195762809898","date":"2025-06-06T07:06:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"77454845011764907282011984107267759485","date":"2025-06-04T09:19:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-03T00:29:53+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-30T08:25:44+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-05-30T08:14:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-27T07:29:29+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-05-27T07:28:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"70cd11c4-bfd5-4a8a-b2c4-10ea7b7e48a6","owner":[],"postedDate":"June 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":49508078,"name":"Physical sciences/Energy science and technology/Fossil fuels/Coal"},{"id":49508079,"name":"Physical sciences/Energy science and technology/Fossil fuels/Natural gas"}],"tags":[],"updatedAt":"2025-07-07T16:03:48+00:00","versionOfRecord":{"articleIdentity":"rs-6739010","link":"https://doi.org/10.1038/s41598-025-09163-z","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-02 15:57:50","publishedOnDateReadable":"July 2nd, 2025"},"versionCreatedAt":"2025-06-05 15:39:03","video":"","vorDoi":"10.1038/s41598-025-09163-z","vorDoiUrl":"https://doi.org/10.1038/s41598-025-09163-z","workflowStages":[]},"version":"v1","identity":"rs-6739010","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6739010","identity":"rs-6739010","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}