Toward safer flight training: The data-driven modeling of accident risk network using text mining based on deep learning

Toward safer flight training: The data-driven modeling of accident risk network using text mining based on deep learning | 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 Research Article Toward safer flight training: The data-driven modeling of accident risk network using text mining based on deep learning Zibo Zhuang, Yongkang Hou, Lei Yang, Jingwei Gong, Lei Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4872273/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 14 You are reading this latest preprint version Abstract The flight training, a critical component of the general aviation industry, exhibits a relatively high severity of risk due to its complexity and the uncertainty inherent in risk interactions. To mine the risk factors and dynamic evolution characteristics affecting flight safety, a data-driven network modeling methodology that integrates text mining with domain knowledge in accident analysis is proposed for the analysis of accident risks specific to flight training. Firstly, flight training accident reports are labeled using domain knowledge gained from accident causation theory to provide basic data for subsequent study. Secondly, the adversarial training algorithm is introduced to enhance the generalization capability of BERT model in processing imbalanced accident textual data. The fine-tuned BERT, Bi-directional Long Short-Term Memory (Bi-LSTM) Conditional Random Field (CRF) algorithm is fused to construct an ensemble algorithm for risk identification, which accomplishes the joint entity-relationship extraction of accident reports. Thirdly, based on the risk identification results, data-driven modeling of the Flight Training Risk Network (FTRN) is performed to quantify the accident evolution characteristics. Then, the aforementioned tasks are meticulously optimized and integrated, subsequently applied to a case study focusing on loss of control in flight (LOCI) accidents. The findings suggest that the identification algorithm effectively and efficiently extracts risk information and interrelationships. Additionally, the network analysis results reveal the key insights into flight training accidents, facilitating the development of holistic risk control strategies. This study provides offers a powerful and innovative analytical tool for safety management departments, enhancing safety and reliability in flight training operations. Aviation accident Risk identification Accident analysis Text mining Complex network Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 13 Sep, 2024 Reviews received at journal 13 Sep, 2024 Reviews received at journal 13 Sep, 2024 Reviews received at journal 27 Aug, 2024 Reviewers agreed at journal 26 Aug, 2024 Reviewers agreed at journal 25 Aug, 2024 Reviewers agreed at journal 25 Aug, 2024 Reviewers agreed at journal 23 Aug, 2024 Reviewers agreed at journal 23 Aug, 2024 Reviewers agreed at journal 22 Aug, 2024 Reviewers invited by journal 22 Aug, 2024 Editor assigned by journal 22 Aug, 2024 Submission checks completed at journal 13 Aug, 2024 First submitted to journal 07 Aug, 2024 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-4872273","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":350205265,"identity":"fe845f27-7475-4da6-9349-7c545e87d121","order_by":0,"name":"Zibo Zhuang","email":"","orcid":"","institution":"The College of Air Traffic Management, Civil Aviation University of China","correspondingAuthor":false,"prefix":"","firstName":"Zibo","middleName":"","lastName":"Zhuang","suffix":""},{"id":350205268,"identity":"f967a7e0-9161-46a6-820d-21b0afda5d50","order_by":1,"name":"Yongkang Hou","email":"","orcid":"","institution":"The College of Safety Science and Engineering, Civil Aviation University of China","correspondingAuthor":false,"prefix":"","firstName":"Yongkang","middleName":"","lastName":"Hou","suffix":""},{"id":350205269,"identity":"3da96d16-bdcc-4164-b4ff-bc43f154a251","order_by":2,"name":"Lei Yang","email":"","orcid":"","institution":"The Flight Academy, Civil Aviation University of China","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Yang","suffix":""},{"id":350205271,"identity":"88f9c9b2-43ae-4672-80df-d552b52cf7aa","order_by":3,"name":"Jingwei Gong","email":"","orcid":"","institution":"The College of Safety Science and Engineering, Civil Aviation University of China","correspondingAuthor":false,"prefix":"","firstName":"Jingwei","middleName":"","lastName":"Gong","suffix":""},{"id":350205273,"identity":"01c70336-91fa-4ffa-9b46-1e50a265b2ed","order_by":4,"name":"Lei Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYLCCBwwSPPbtDYmPwTxm5gbCWhIYJOQMeA48NmZgMABqYSRKC4OxgYTjM2mwFgYCWgyOnz38IqHCInG7BHNadUHFn2j+dqCWHxXbcGs5k5dmkXBGInHn7La02zPOGOTOOMzYwNhz5jZOLWYHcswMEtskEhvunEm7zdtmkNsA1MLM2IZHy/k3QC3/gFpu5H8rBmmZT1DLjRzjB4kNEsYGNxLSmEFaNhDSYn/jjRlDwjEJOcmeA8nSPGeMczcCtRzE5xfJ/hzjDx9q6nj42RsSP/NUyOXOO3/44IMfFbi1AAGbBIbQAXzqgYD5AwEFo2AUjIJRMNIBAHiEXucOsIIDAAAAAElFTkSuQmCC","orcid":"","institution":"The Flight Academy, Civil Aviation University of China","correspondingAuthor":true,"prefix":"","firstName":"Lei","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2024-08-07 06:17:35","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4872273/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4872273/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64208670,"identity":"da58a215-c5f8-4680-a285-6e3f25cae46e","added_by":"auto","created_at":"2024-09-10 06:32:43","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1178251,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4872273/v1_covered_97f57b5c-3508-4b19-a6cc-55a5ae1c0f0c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Toward safer flight training: The data-driven modeling of accident risk network using text mining based on deep learning","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":"international-journal-of-computational-intelligence-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [International Journal of Computational Intelligence Systems](https://link.springer.com/journal/44196)","snPcode":"44196","submissionUrl":"https://submission.springernature.com/new-submission/44196/3","title":"International Journal of Computational Intelligence Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Aviation accident, Risk identification, Accident analysis, Text mining, Complex network","lastPublishedDoi":"10.21203/rs.3.rs-4872273/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4872273/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe flight training, a critical component of the general aviation industry, exhibits a relatively high severity of risk due to its complexity and the uncertainty inherent in risk interactions. To mine the risk factors and dynamic evolution characteristics affecting flight safety, a data-driven network modeling methodology that integrates text mining with domain knowledge in accident analysis is proposed for the analysis of accident risks specific to flight training. Firstly, flight training accident reports are labeled using domain knowledge gained from accident causation theory to provide basic data for subsequent study. Secondly, the adversarial training algorithm is introduced to enhance the generalization capability of BERT model in processing imbalanced accident textual data. The fine-tuned BERT, Bi-directional Long Short-Term Memory (Bi-LSTM) Conditional Random Field (CRF) algorithm is fused to construct an ensemble algorithm for risk identification, which accomplishes the joint entity-relationship extraction of accident reports. Thirdly, based on the risk identification results, data-driven modeling of the Flight Training Risk Network (FTRN) is performed to quantify the accident evolution characteristics. Then, the aforementioned tasks are meticulously optimized and integrated, subsequently applied to a case study focusing on loss of control in flight (LOCI) accidents. The findings suggest that the identification algorithm effectively and efficiently extracts risk information and interrelationships. Additionally, the network analysis results reveal the key insights into flight training accidents, facilitating the development of holistic risk control strategies. This study provides offers a powerful and innovative analytical tool for safety management departments, enhancing safety and reliability in flight training operations.\u003c/p\u003e","manuscriptTitle":"Toward safer flight training: The data-driven modeling of accident risk network using text mining based on deep learning","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-10 06:24:34","doi":"10.21203/rs.3.rs-4872273/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-13T23:47:29+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-13T18:37:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-13T15:50:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-28T00:49:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"197923389277726307188860100902189971484","date":"2024-08-26T08:51:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"254635352418222576479920014513925648732","date":"2024-08-25T11:09:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"15966643181067375128824164262531014626","date":"2024-08-25T06:06:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"107020733181475788460756109092717579988","date":"2024-08-23T23:06:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"173924946813404893854852335515458767771","date":"2024-08-23T13:22:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157007297656974709211604980221823535306","date":"2024-08-23T02:19:28+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-23T02:01:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-22T23:19:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-13T07:29:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of Computational Intelligence Systems","date":"2024-08-07T06:16:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-computational-intelligence-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [International Journal of Computational Intelligence Systems](https://link.springer.com/journal/44196)","snPcode":"44196","submissionUrl":"https://submission.springernature.com/new-submission/44196/3","title":"International Journal of Computational Intelligence Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9acee46d-e193-46af-a4e1-44839eeac8cb","owner":[],"postedDate":"September 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-11-13T16:38:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-10 06:24:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4872273","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4872273","identity":"rs-4872273","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}