Influencing Factors and Predictive Modeling of the Urban Heat Island in Guangzhou, China

Influencing Factors and Predictive Modeling of the Urban Heat Island in Guangzhou, China | 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 Influencing Factors and Predictive Modeling of the Urban Heat Island in Guangzhou, China Yiming Huang, Ping Du, Hui Li, Jinqu Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5463284/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Jul, 2025 Read the published version in Environmental Earth Sciences → Version 1 posted 7 You are reading this latest preprint version Abstract Constructing a predictive model for urban heat islands (UHIs) is essential for accurately assessing urban thermal environments and guiding sustainable development strategies. Previous studies typically modeled urban heat island based on the entire city, ignoring the differences of influencing factors between local areas. Therefore, this article explores the impact of influencing factors on local heat island based on Local Climate Zones (LCZ) zoning. We first prepared 21 features, and then created an urban heat island prediction model using machine learning technology it in each LCZ region. The prediction results were compared to those from Random Forest (RF), Gradient Boosting Trees (GBT), and Artificial Neural Networks (ANN). Experimental results indicate that the XGBoost model offers higher accuracy in predicting UHI, with accuracy exceeding 80%. The SHAP (SHapley Additive ExPlanations) analysis found significant elements impacting UHI formation in each zone, including impervious surface density (ISD), building density (BD), green space density (GSD), and the richness of vegetation. This study not only improves the accuracy of UHI predictions, but also provides the groundwork for future research into the dynamic planning of urban heat islands. Urban heat island Predictive modeling XGBoost Local climate zones Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 09 Jul, 2025 Read the published version in Environmental Earth Sciences → Version 1 posted Editorial decision: Revision requested 18 Jan, 2025 Reviews received at journal 13 Jan, 2025 Reviewers agreed at journal 22 Dec, 2024 Reviewers invited by journal 22 Dec, 2024 Editor assigned by journal 17 Nov, 2024 Submission checks completed at journal 17 Nov, 2024 First submitted to journal 15 Nov, 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. 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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-5463284","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":379190947,"identity":"e9e6e411-76c3-47d0-a9d8-4a0036565243","order_by":0,"name":"Yiming Huang","email":"","orcid":"","institution":"South China Normal University","correspondingAuthor":false,"prefix":"","firstName":"Yiming","middleName":"","lastName":"Huang","suffix":""},{"id":379190949,"identity":"5403835e-4714-454e-b8dc-7b2d6a6ec263","order_by":1,"name":"Ping Du","email":"","orcid":"","institution":"Guangdong Construction Vocational Technology Institute","correspondingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Du","suffix":""},{"id":379190950,"identity":"fe5f01e9-7220-418e-89a9-c6f25cbe6090","order_by":2,"name":"Hui Li","email":"","orcid":"","institution":"South China Normal University","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Li","suffix":""},{"id":379190951,"identity":"44133cd0-5ed5-4454-8394-a45e10b31274","order_by":3,"name":"Jinqu Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtElEQVRIiWNgGAWjYFCCA4wPoCwDorUww5QSrYWBTYI0LfKNh7dV/qi5k9jA3rxNgqHmDmEtjA3Hym7zHHuW2MBzrEyC4dgzwlqYGc6Y3WZsOJzYIJFjJgFkENbCBtRS+BOkRf4NkVp4gFoYeMG28BCpBej8YmmeY4eN23jSii0SjhGhRX7G4Y0ff9Qclu1nP7zxxocaIrQwSByARAcbiEggQgMDA38D8ZE+CkbBKBgFIxQAABc1OaReAztrAAAAAElFTkSuQmCC","orcid":"","institution":"Guangdong Construction Vocational Technology Institute","correspondingAuthor":true,"prefix":"","firstName":"Jinqu","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-11-16 00:53:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5463284/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5463284/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s12665-025-12411-0","type":"published","date":"2025-07-09T15:57:10+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":86699333,"identity":"eb3d1f26-ba66-49ed-bc93-6a3e7df1c07f","added_by":"auto","created_at":"2025-07-14 16:07:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1145219,"visible":true,"origin":"","legend":"","description":"","filename":"paperByHuang.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5463284/v1_covered_3764b6c2-2c5f-40cc-a1ba-1e8b469a5f23.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Influencing Factors and Predictive Modeling of the Urban Heat Island in Guangzhou, China","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":"environmental-earth-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"enge","sideBox":"Learn more about [Environmental Earth Sciences](https://www.springer.com/journal/12665)","snPcode":"12665","submissionUrl":"https://submission.nature.com/new-submission/12665/3","title":"Environmental Earth Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Urban heat island, Predictive modeling, XGBoost, Local climate zones","lastPublishedDoi":"10.21203/rs.3.rs-5463284/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5463284/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eConstructing a predictive model for urban heat islands (UHIs) is essential for accurately assessing urban thermal environments and guiding sustainable development strategies. 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