{"paper_id":"297622be-bb2a-45a0-9048-4dd8a0d4097a","body_text":"A Hybrid Forecasting and Fuzzy Comprehensive Evaluation Approach for Graded Early Warning of Experimental Task Frequency | 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 A Hybrid Forecasting and Fuzzy Comprehensive Evaluation Approach for Graded Early Warning of Experimental Task Frequency Ningna Sun, Danni Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9090387/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Accurate forecasting and graded warning of experimental task frequencies are essential for resource optimization and operational safety in large-scale scientific facilities. This paper proposes a novel graded early warning method that integrates deep sequential forecasting with fuzzy comprehensive evaluation. A hybrid Transformer-LSTM neural network is developed to capture both long-range dependencies and local temporal patterns in multivariate task frequency sequences. To address the sensitivity of deep models to hyperparameter configurations, a bioinspired Black-winged Kite Algorithm (BKA) is employed to jointly optimize the learning rate, hidden layer units, and regularization coefficients. Subsequently, a fuzzy comprehensive evaluation model, incorporating entropy-based weights and membership functions derived from prediction residuals, maps the forecasting uncertainties onto a four-level early warning scale (Safe, Mild, Moderate, Severe). Digital simulation verification shows that the proposed method has high prediction accuracy, and the integration of data-driven prediction and fuzzy logic provides a powerful framework for active anomaly management in complex dynamic systems. Physical sciences/Engineering Physical sciences/Mathematics and computing Time series forecasting Transformer-LSTM Black-winged Kite Algorithm Fuzzy comprehensive evaluation Graded early warning Full Text Additional Declarations No competing interests reported. Supplementary Files papercode.pdf Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 12 May, 2026 Reviews received at journal 11 May, 2026 Reviews received at journal 08 May, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviewers agreed at journal 30 Apr, 2026 Reviewers invited by journal 29 Apr, 2026 Editor invited by journal 16 Mar, 2026 Editor assigned by journal 11 Mar, 2026 Submission checks completed at journal 11 Mar, 2026 First submitted to journal 11 Mar, 2026 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. 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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-9090387\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":632772645,\"identity\":\"2abd9bde-def3-405c-b236-8b883c4f0173\",\"order_by\":0,\"name\":\"Ningna Sun\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Jiangsu University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ningna\",\"middleName\":\"\",\"lastName\":\"Sun\",\"suffix\":\"\"},{\"id\":632772646,\"identity\":\"9cc0fc94-2aab-4d11-ba7c-73dbbf8cf45b\",\"order_by\":1,\"name\":\"Danni Zhu\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYDACCTBi4GFsYGB8kFBRQ5oWZoMHZ44RrwUE2CQftjAT1iE/u8fwxs8dd2SY+8+YVSQ2sDHwt3cn4NXCOOeMsWXvmWc8jDNyzG4k7pBhkDhzdgNeLcwSOWYSvG2HgVp4gFrOsDEYSOTi18IG1CL5F6QF6LCCxDZmwlp4gFqkwbY05JgxEKVFQiKt2FoW7LC0YomEM8d4CPpFfkbyxptv2w7bG/Yf3vjxR0WNHH97L34tcGDYAHUpccrB1hGvdBSMglEwCkYaAABzfUSKtXqLgQAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Jiangsu University\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Danni\",\"middleName\":\"\",\"lastName\":\"Zhu\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-03-11 05:55:05\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-9090387/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-9090387/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":108806231,\"identity\":\"9ce4f7c8-fe40-4668-adb6-49404bfa4c34\",\"added_by\":\"auto\",\"created_at\":\"2026-05-08 15:28:05\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":931739,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"ScientificReports0311.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9090387/v1_covered_b16b5365-931d-4111-8692-eeaba5027f2f.pdf\"},{\"id\":108680655,\"identity\":\"b30edcdc-4499-489c-90f0-5df24dd1f7e9\",\"added_by\":\"auto\",\"created_at\":\"2026-05-07 09:16:17\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":164338,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"papercode.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9090387/v1/e1343ad4736c406602b6f108.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"A Hybrid Forecasting and Fuzzy Comprehensive Evaluation Approach for Graded Early Warning of Experimental Task Frequency\",\"fulltext\":[],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":false,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":true,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":true,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"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\":\"Time series forecasting, Transformer-LSTM, Black-winged Kite Algorithm, Fuzzy comprehensive evaluation, Graded early warning\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-9090387/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-9090387/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"Accurate forecasting and graded warning of experimental task frequencies are essential for resource optimization and operational safety in large-scale scientific facilities. 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