LightGBM-Based Stochastic Modeling for River Dust-Raising Alert

LightGBM-Based Stochastic Modeling for River Dust-Raising Alert | 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 LightGBM-Based Stochastic Modeling for River Dust-Raising Alert Chih Chao Ho, Chih Hsiung Chang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3773734/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 To enhance the accuracy of Taiwan's existing river dust-raising alert system, which exclusively depends on wind speed predictions, this study combines hydrological, meteorological, air quality information with LightGBM to establish a stochastic model for forecasting PM10 exceedance probabilities. The flexible probability information can effectively reduce the risk of poor decision-making caused by concentration deterministic forecast errors. LightGBM, a boosting-based ensemble learning algorithm, employs a depth-constrained leaf-wise growth strategy, speeding up training, reducing memory consumption, and shortening training time. The results of model training and validation demonstrate good performance in terms of accuracy, recall, and specificity metrics. This signifies that the model effectively predicts the occurrence of actual dust-raising events. In comparison to the current dust-raising alert mechanism, the model can significantly reduce unnecessary dust alerts and lightening the workforce's burden. Moreover, this model effectively forecasts dust events under low to moderate wind speed conditions, providing decision-makers with crucial support data for proactive dust control deployment. River Dust PM10 Stochastic Model LightGBM Full Text 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-3773734","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":266964859,"identity":"18411cfe-efbc-4d25-b21e-dec37725646a","order_by":0,"name":"Chih Chao Ho","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYFACxsYHCT9swIwDRGphPmzwsCcNpKWBWC1saZIP2A6DmcRpke8/Y2yQwHPebm37YaAtNTbRBLUwNpwxfJBgcTt525lEoJZjabkNhLQwM/aAbLmdbHYAqIWx4TBhLWzMPGYSCWznks3OPyRSCw8bWxpQywE7sxvE2iLBAwzkxJ7kBLMbQFsSiPGLfP/Bxoc/ftjZm51Pf/jgQ40NYS0wkAhWmUCschCwJ0XxKBgFo2AUjDAAAJQfRsP/O3iXAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0004-0793-6298","institution":"Feng Chia University","correspondingAuthor":true,"prefix":"","firstName":"Chih","middleName":"Chao","lastName":"Ho","suffix":""},{"id":266964860,"identity":"6b09dfed-5366-4b66-b17e-41167b872160","order_by":1,"name":"Chih Hsiung Chang","email":"","orcid":"","institution":"Feng Chia University","correspondingAuthor":false,"prefix":"","firstName":"Chih","middleName":"Hsiung","lastName":"Chang","suffix":""}],"badges":[],"createdAt":"2023-12-18 21:39:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3773734/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3773734/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51813665,"identity":"cbed82d4-e830-4cc1-aafe-d7bdc85c6339","added_by":"auto","created_at":"2024-02-29 13:53:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":545994,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3773734/v1_covered_42682382-00d9-45d4-8b2b-22e3593cf901.pdf"}],"financialInterests":"","formattedTitle":"LightGBM-Based Stochastic Modeling for River Dust-Raising Alert","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":"River Dust, PM10, Stochastic Model, LightGBM","lastPublishedDoi":"10.21203/rs.3.rs-3773734/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3773734/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"To enhance the accuracy of Taiwan's existing river dust-raising alert system, which exclusively depends on wind speed predictions, this study combines hydrological, meteorological, air quality information with LightGBM to establish a stochastic model for forecasting PM10 exceedance probabilities. 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