Urban Village Waterlogging in Shenzhen: Unveiling Mechanisms through Multi-Scale Buffer Spatial Analysis and Machine Learning during the “9·7” Rainstorm

Urban Village Waterlogging in Shenzhen: Unveiling Mechanisms through Multi-Scale Buffer Spatial Analysis and Machine Learning during the “9·7” Rainstorm | 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 Urban Village Waterlogging in Shenzhen: Unveiling Mechanisms through Multi-Scale Buffer Spatial Analysis and Machine Learning during the “9·7” Rainstorm Yinglong Lv, Heng Liu, Yu Yan, Xinghan Gong, Caicai Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8462341/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 Urban waterlogging has become increasingly severe under the combined pressures of extreme rainfall and rapid urbanization. Urban villages, as high-density and low-income settlements, are particularly vulnerable. Taking the “9·7” extreme rainstorm in Shenzhen as a case, this study investigates 623 urban-village study units across the Longgang River and Shenzhen River basins to identify the key drivers of waterlogging risk. Specifically, we examine the dominant role of the topographic low-lying effect and the amplifying role of street-canyon characteristics in urban-village vulnerability.We integrate multi-source data and construct a multi-ring buffer framework to quantify contrasts between urban villages and their surrounding environments. An XGBoost model, together with SHAP and partial dependence plots (PDP), is used to interpret factor contributions and interaction effects. The results show that: (1) the topographic low-lying effect dominates waterlogging risk and exhibits a clear threshold-switch pattern, with absolute elevation as the primary driver; (2) within a 0–400 m range, urban villages display pronounced morphological contrasts with their surroundings, forming a risk “vulnerability ring”; (3) street-canyon indicators are more important than conventional density metrics, highlighting the critical role of micro-scale morphology in regulating runoff pathways; and (4) maintaining the sky view factor (SVF) within 0.3–0.55 can effectively mitigate waterlogging risk. Overall, this study elucidates the synergistic driving mechanisms and key thresholds of waterlogging in urban villages, providing a scientific basis for targeted resilience enhancement. Earth and environmental sciences/Environmental sciences Social science/Environmental studies Scientific community and society/Geography Social science/Geography Earth and environmental sciences/Hydrology Earth and environmental sciences/Natural hazards Urban-village waterlogging Topographic low-lying effect Street canyon Multi-scale buffer analysis Machine learning waterlogging mechanism Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Mar, 2026 Reviews received at journal 18 Mar, 2026 Reviewers agreed at journal 18 Mar, 2026 Reviewers agreed at journal 16 Mar, 2026 Reviewers agreed at journal 15 Mar, 2026 Reviews received at journal 15 Mar, 2026 Reviewers agreed at journal 09 Mar, 2026 Reviewers agreed at journal 08 Mar, 2026 Reviewers agreed at journal 13 Feb, 2026 Reviewers invited by journal 22 Jan, 2026 Editor invited by journal 30 Dec, 2025 Editor assigned by journal 29 Dec, 2025 Submission checks completed at journal 29 Dec, 2025 First submitted to journal 27 Dec, 2025 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. 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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-8462341","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":580769103,"identity":"f3fc9ae8-916e-455f-8cac-0e4b34058253","order_by":0,"name":"Yinglong Lv","email":"","orcid":"","institution":"School of Architecture and Urban Planning","correspondingAuthor":false,"prefix":"","firstName":"Yinglong","middleName":"","lastName":"Lv","suffix":""},{"id":580769104,"identity":"97a62f53-b6a7-4ae8-a8b0-10ec69363717","order_by":1,"name":"Heng Liu","email":"","orcid":"","institution":"School of Architecture and Urban Planning","correspondingAuthor":false,"prefix":"","firstName":"Heng","middleName":"","lastName":"Liu","suffix":""},{"id":580769105,"identity":"c7a9a502-543f-4aae-ab85-22313d54249e","order_by":2,"name":"Yu Yan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvUlEQVRIiWNgGAWjYDACZjBpA+HwkKAljRQtEHCYBC0Gx7nTpHnbztvLz0hgfPC2jUHenKCWw7zbgFpuJzbOSGA2nNvGYLizgQgtkjPbbicwSySwAfUyJBgcIE7LOXs2iQT230RrkfjYdoCxB2gLM1FaJA/zbrb4cC45cQbPw2bJOeckDDcQ0sJ3/uzGGwlldvby7ckHP7wps5EnaIvCAQYWCQiTsQFISBBQDwTyDQzMHwgrGwWjYBSMghENAPqYO/nzT04sAAAAAElFTkSuQmCC","orcid":"","institution":"School of Architecture and Urban Planning","correspondingAuthor":true,"prefix":"","firstName":"Yu","middleName":"","lastName":"Yan","suffix":""},{"id":580769106,"identity":"3258eb10-e460-4d4e-a03a-9eb282cb1289","order_by":3,"name":"Xinghan Gong","email":"","orcid":"","institution":"School of Architecture and Urban Planning","correspondingAuthor":false,"prefix":"","firstName":"Xinghan","middleName":"","lastName":"Gong","suffix":""},{"id":580769107,"identity":"07ef4b95-1d52-4775-b5aa-db86bab6ebc3","order_by":4,"name":"Caicai Xu","email":"","orcid":"","institution":"School of Architecture and Urban Planning","correspondingAuthor":false,"prefix":"","firstName":"Caicai","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2025-12-27 16:53:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8462341/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8462341/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101297246,"identity":"559f20d0-3da5-46d1-ad3e-5f6b403699e5","added_by":"auto","created_at":"2026-01-28 09:26:10","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2671554,"visible":true,"origin":"","legend":"","description":"","filename":"UrbanVillageWaterlogginginShenzhenUnveilingMechanismsthroughMultiScaleBufferSpatialAnalysisandMachineLearningduringthe97Rainstorm.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8462341/v1_covered_1fc65f75-b954-4894-825f-ea82998f0941.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Urban Village Waterlogging in Shenzhen: Unveiling Mechanisms through Multi-Scale Buffer Spatial Analysis and Machine Learning during the “9·7” Rainstorm","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":"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":"Urban-village waterlogging, Topographic low-lying effect, Street canyon, Multi-scale buffer analysis, Machine learning, waterlogging mechanism","lastPublishedDoi":"10.21203/rs.3.rs-8462341/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8462341/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUrban waterlogging has become increasingly severe under the combined pressures of extreme rainfall and rapid urbanization. Urban villages, as high-density and low-income settlements, are particularly vulnerable. Taking the \u0026ldquo;9\u0026middot;7\u0026rdquo; extreme rainstorm in Shenzhen as a case, this study investigates 623 urban-village study units across the Longgang River and Shenzhen River basins to identify the key drivers of waterlogging risk. Specifically, we examine the dominant role of the topographic low-lying effect and the amplifying role of street-canyon characteristics in urban-village vulnerability.We integrate multi-source data and construct a multi-ring buffer framework to quantify contrasts between urban villages and their surrounding environments. An XGBoost model, together with SHAP and partial dependence plots (PDP), is used to interpret factor contributions and interaction effects. The results show that: (1) the topographic low-lying effect dominates waterlogging risk and exhibits a clear threshold-switch pattern, with absolute elevation as the primary driver; (2) within a 0\u0026ndash;400 m range, urban villages display pronounced morphological contrasts with their surroundings, forming a risk \u0026ldquo;vulnerability ring\u0026rdquo;; (3) street-canyon indicators are more important than conventional density metrics, highlighting the critical role of micro-scale morphology in regulating runoff pathways; and (4) maintaining the sky view factor (SVF) within 0.3\u0026ndash;0.55 can effectively mitigate waterlogging risk. Overall, this study elucidates the synergistic driving mechanisms and key thresholds of waterlogging in urban villages, providing a scientific basis for targeted resilience enhancement.\u003c/p\u003e","manuscriptTitle":"Urban Village Waterlogging in Shenzhen: Unveiling Mechanisms through Multi-Scale Buffer Spatial Analysis and Machine Learning during the “9·7” Rainstorm","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-27 17:11:21","doi":"10.21203/rs.3.rs-8462341/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-23T10:08:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-18T11:42:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"269607544781009965978962329516227952783","date":"2026-03-18T07:37:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"264097799042833099623312916946648856025","date":"2026-03-16T04:58:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"252734725166956707831381788120018455153","date":"2026-03-16T03:16:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-16T02:34:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"265580821512975981697122682788756457996","date":"2026-03-09T17:48:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"135869027987153074610273770285322157266","date":"2026-03-08T08:40:43+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"134564446227554503473806254870294417528","date":"2026-02-13T17:36:40+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-22T09:31:27+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-12-30T12:35:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-29T13:06:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-29T13:06:37+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-12-27T16:36:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","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}}],"origin":"","ownerIdentity":"dfba0b2e-4809-43a7-9b2f-2878d3e4161c","owner":[],"postedDate":"January 27th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":61844251,"name":"Earth and environmental sciences/Environmental sciences"},{"id":61844252,"name":"Social science/Environmental studies"},{"id":61844253,"name":"Scientific community and society/Geography"},{"id":61844254,"name":"Social science/Geography"},{"id":61844255,"name":"Earth and environmental sciences/Hydrology"},{"id":61844256,"name":"Earth and environmental sciences/Natural hazards"}],"tags":[],"updatedAt":"2026-05-14T05:25:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-27 17:11:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8462341","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8462341","identity":"rs-8462341","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}