Integrating Flood Depth, Duration, and Structural Damage in Vulnerability Surface Modelling Using Empirical Regression

Integrating Flood Depth, Duration, and Structural Damage in Vulnerability Surface Modelling Using Empirical Regression | 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 Integrating Flood Depth, Duration, and Structural Damage in Vulnerability Surface Modelling Using Empirical Regression Dedy Alfian, Ashfa Achmad, Ella Meilianda, Muhammad Syukri This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6493405/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Flood vulnerability assessment is critical for enhancing disaster risk reduction in regions exposed to compound flooding. This study presents a novel approach by developing flood vulnerability surfaces through the integration of flood depth, flood duration, and damage ratio across various housing typologies using a polynomial regression model. Unlike conventional models that primarily rely on flood depth, this study incorporates flood duration to better capture the non-linear and synergistic effects of prolonged inundation on structural damage. Three housing typologies, i.e., Non-Permanent, Semi-Permanent, and Permanent were analyzed, each with sub-types reflecting variations in construction material and resilience. Using empirical field survey data from a flood-prone district, three-dimensional (3D) surface models were constructed. Polynomial regression demonstrated superior performance, with coefficients of determination (R²) ranging from 0.92 to 0.97 across typologies. Notably, the Non-Permanent Type D structure reached a damage ratio of 0.9 at a flood depth of 2.5 m and a duration of 10 days, indicating extreme vulnerability. Semi-permanent structures such as Type B and C experienced damage ratios of 0.8–0.9 under flood depths of 3–4 m and durations exceeding 12 days. In contrast, Permanent structures showed more resistance, with damage ratios remaining below 0.6 under similar conditions, but rising rapidly above 0.85 when exposed to depths greater than 4 m and durations over 12 days. These 3D vulnerability surfaces provide clear visualizations of how flood parameters influence structural damage, improving the interpretability of risk assessments. The findings highlight the importance of typology-specific modelling to address local structural differences. This study offers a robust and flexible method for accurately modelling flood vulnerability, supporting the design of targeted mitigation measures and resilient housing strategies tailored to varying structural capacities under compound flood hazards. GIS House Typology Empirical method Statistical Analysis Flood Vulnerability Surface Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 12 May, 2025 Reviewers invited by journal 08 May, 2025 Editor invited by journal 03 May, 2025 Editor assigned by journal 23 Apr, 2025 First submitted to journal 22 Apr, 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-6493405","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":453921110,"identity":"7ab63da6-cd60-4f3b-ab27-46806ba673e8","order_by":0,"name":"Dedy Alfian","email":"data:image/png;base64,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","orcid":"","institution":"USK: Universitas Syiah Kuala","correspondingAuthor":true,"prefix":"","firstName":"Dedy","middleName":"","lastName":"Alfian","suffix":""},{"id":453921111,"identity":"35e8ea32-d261-4d50-b502-2b56488fedb4","order_by":1,"name":"Ashfa Achmad","email":"","orcid":"","institution":"Syiah Kuala University: Universitas Syiah Kuala","correspondingAuthor":false,"prefix":"","firstName":"Ashfa","middleName":"","lastName":"Achmad","suffix":""},{"id":453921112,"identity":"5e9f2d6f-0371-4900-bb33-923f68423645","order_by":2,"name":"Ella Meilianda","email":"","orcid":"","institution":"Universitas Syiah Kuala","correspondingAuthor":false,"prefix":"","firstName":"Ella","middleName":"","lastName":"Meilianda","suffix":""},{"id":453921113,"identity":"005c526a-06a4-4efc-a012-9a4502ea72a1","order_by":3,"name":"Muhammad Syukri","email":"","orcid":"","institution":"Universitas Syiah Kuala","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Syukri","suffix":""}],"badges":[],"createdAt":"2025-04-21 07:19:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6493405/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6493405/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82655477,"identity":"dd3a37c0-00fe-4e6f-beb3-c27cb3ddb525","added_by":"auto","created_at":"2025-05-13 18:39:30","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1686980,"visible":true,"origin":"","legend":"","description":"","filename":"paperFloodVurnerabilitysurfaceDedyAlfianLN.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6493405/v1_covered_2e289cc1-941b-4cf6-924e-a5b3f3f248b6.pdf"}],"financialInterests":"","formattedTitle":"Integrating Flood Depth, Duration, and Structural Damage in Vulnerability Surface Modelling Using Empirical Regression","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":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"natural-hazards","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nhaz","sideBox":"Learn more about [Natural Hazards](https://www.springer.com/journal/11069)","snPcode":"11069","submissionUrl":"https://submission.nature.com/new-submission/11069/3","title":"Natural Hazards","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"GIS, House Typology, Empirical method, Statistical Analysis, Flood Vulnerability Surface","lastPublishedDoi":"10.21203/rs.3.rs-6493405/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6493405/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFlood vulnerability assessment is critical for enhancing disaster risk reduction in regions exposed to compound flooding. This study presents a novel approach by developing flood vulnerability surfaces through the integration of flood depth, flood duration, and damage ratio across various housing typologies using a polynomial regression model. Unlike conventional models that primarily rely on flood depth, this study incorporates flood duration to better capture the non-linear and synergistic effects of prolonged inundation on structural damage. Three housing typologies, i.e., Non-Permanent, Semi-Permanent, and Permanent were analyzed, each with sub-types reflecting variations in construction material and resilience. Using empirical field survey data from a flood-prone district, three-dimensional (3D) surface models were constructed. Polynomial regression demonstrated superior performance, with coefficients of determination (R\u0026sup2;) ranging from 0.92 to 0.97 across typologies. 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