Research on Extreme Rainstorm Prediction in Chengdu Region Based on a Multi-Weight Scheme Machine Learning Approach

Research on Extreme Rainstorm Prediction in Chengdu Region Based on a Multi-Weight Scheme Machine Learning Approach | 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 Method Article Research on Extreme Rainstorm Prediction in Chengdu Region Based on a Multi-Weight Scheme Machine Learning Approach Jinlong NIU This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8835218/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 This study focuses on extreme rainstorm events in the Chengdu region during 2025 and constructs an extreme rainstorm prediction model based on a multi-weight scheme machine learning framework. By integrating eight distinct sample weighting strategies, the research systematically analyzes 11 rainstorm episodes in the Chengdu area-eight used for model training and three reserved for independent validation. The study combines ERA5 reanalysis data with station observation data to develop a feature engineering system encompassing multi-level meteorological variables and derived features. Results indicate that different sample weighting strategies significantly influence model performance, with Scheme 6 (Custom 1) demonstrating optimal results across multiple evaluation metrics. The model effectively captures the spatial distribution characteristics of extreme precipitation and shows good predictive capability for stations experiencing rainfall ≥ 50 mm. This research provides a novel technical approach and methodological support for refined extreme rainstorm forecasting in the Chengdu region. Meteoritics extreme rainstorm machine learning XGBoost sample weighting Full Text Additional Declarations The authors declare no competing interests. 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-8835218","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Method Article","associatedPublications":[],"authors":[{"id":588633241,"identity":"99575a9f-7082-47ba-adb5-57ce45b236ed","order_by":0,"name":"Jinlong NIU","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYBACefnzHx8kGPyTs29vIFKL4QwGY4MPBQeMDXgOEGvNDQYzwRkfDiRukEggUgfj7IY0Zh6DO4nbJR9vvMFQYxNNUAu7zIFjj3kMnhnvnJ1WbMFwLC23gaAtDYntxjwGzLINt3PMJBgbDhPWwnAgmU0aqIWx4eYZYrXcSGOTnGFwWHHDDR4itRj2nGE2+GCQZizZA/RLAjF+kWfvYXyQ8MdGjp/98MYbH2psiHAYEjAgOmqQtJCqYxSMglEwCkYGAAChgkM+J+k/uwAAAABJRU5ErkJggg==","orcid":"","institution":"Chengdu Meteorological Office of Sichuan Province","correspondingAuthor":true,"prefix":"","firstName":"Jinlong","middleName":"","lastName":"NIU","suffix":""}],"badges":[],"createdAt":"2026-02-10 01:39:27","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8835218/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8835218/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102746050,"identity":"457c8b7a-a5aa-478f-8882-395f5c24b48b","added_by":"auto","created_at":"2026-02-16 08:55:26","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":996516,"visible":true,"origin":"","legend":"","description":"","filename":"ResearchonExtremeRainstormPredictioninChengduRegionBasedonaMultiWeightSchemeMachineLearningApproach.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8835218/v1_covered_9e0a9d67-7c58-480c-b674-fe6cf1dd9d15.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eResearch on Extreme Rainstorm Prediction in Chengdu Region Based on a Multi-Weight Scheme Machine Learning Approach\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Chengdu Meteorological Office of Sichuan Province","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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":"extreme rainstorm, machine learning, XGBoost, sample weighting","lastPublishedDoi":"10.21203/rs.3.rs-8835218/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8835218/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study focuses on extreme rainstorm events in the Chengdu region during 2025 and constructs an extreme rainstorm prediction model based on a multi-weight scheme machine learning framework. 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