Integrating Morphometric Controls for Runoff Dynamics in Bayelsa State, Nigeria: Enhancing Flood Susceptibility Mapping with Machine Learning

Integrating Morphometric Controls for Runoff Dynamics in Bayelsa State, Nigeria: Enhancing Flood Susceptibility Mapping with Machine Learning | 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 Morphometric Controls for Runoff Dynamics in Bayelsa State, Nigeria: Enhancing Flood Susceptibility Mapping with Machine Learning Okes Imoni This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8993426/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 Flooding remains one of the most frequent and destructive hazards in Nigeria’s Niger Delta, where flat terrain and intense rainfall combine with rapid land-use change to heighten risk. This study integrates morphometric basin analysis with supervised machine learning to enhance flood-susceptibility mapping across four representative catchments in Bayelsa State Forcados, Nun, Ekole, and Seibri. Using a 30 m SRTM digital elevation model, fourteen morphometric parameters were derived to quantify drainage efficiency, infiltration potential, and relief characteristics that influence runoff generation. These parameters served as predictors for Random Forest, Support Vector Machine, and XGBoost classifiers trained with flood and non-flood samples extracted from Sentinel-1 SAR data (2018–2024). Among the tested models, XGBoost achieved the highest performance (accuracy = 93.1%, AUC = 0.95), reliably delineating high-risk sub-catchments. The resulting probability maps revealed micro-zones localized clusters of elevated flood potential within generally moderate basins such as Ekole, emphasizing the spatial heterogeneity of flood processes. Drainage density (Dd), relief ratio (Rh), and infiltration number (If) were the most influential variables controlling flood susceptibility. Forcados exhibited the greatest hazard (Dd = 3.57 km km⁻²; If = 41.80), whereas Seibri and Ekole showed lower overall susceptibility but contained critical micro-zones requiring attention. The approach demonstrates that combining morphometric controls with data-driven classification provides a transferable, high-resolution tool for flood-risk assessment in other tropical deltaic environments lacking dense hydrological networks. These findings support more precise local planning, early warning, and adaptation strategies in vulnerable low-lying regions. Climate Analysis and Modeling Flood Risk Management Machine Learning Catchments Geospatial Morphometry XGBoost SRTM Remote Sensing 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-8993426","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":598460087,"identity":"8dfad0da-8d7a-409e-b6ca-4f9a9393857d","order_by":0,"name":"Okes Imoni","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYBACxgYQaQDEzMwHgKSEDCla2BJAWnhIsZDHAEwSVMfc3nvsMU/BNjlzdp7Pr27UWPAwsB8+ugGvw3rOpRvzGNw2tmzm3WadcwzoMJ60tBt4tczIMZMGaknccJh3m3EOG1CLBI8Zfi3z38C08DwzzvlHjJYZPHAtzI9z24jR0pNjJjkH6BeDw2xmzLl9EjxshPxi2H7GTOLNn9tyBucPP/6c861Ojp/98DH8WhoYGJigccEmASbxKQcBeZDjfkDYzB8IqR4Fo2AUjIKRCQAsJULeKCn/SQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0007-3548-2889","institution":"Niger Delta University","correspondingAuthor":true,"prefix":"","firstName":"Okes","middleName":"","lastName":"Imoni","suffix":""}],"badges":[],"createdAt":"2026-02-28 08:43:28","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-8993426/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8993426/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103855261,"identity":"12cb0456-d6ee-4ab3-a531-73ccddc5c5d9","added_by":"auto","created_at":"2026-03-03 17:55:17","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1962738,"visible":true,"origin":"","legend":"","description":"","filename":"IntegratingMorphometricControlsforRunoffDynamicsinBayelsaStateupdate911.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8993426/v1_covered_2cfab07f-35f5-430e-b287-6171a3da4357.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eIntegrating Morphometric Controls for Runoff Dynamics in Bayelsa State, Nigeria: Enhancing Flood Susceptibility Mapping with Machine Learning\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Niger Delta University","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":"Flood Risk Management, Machine Learning, Catchments, Geospatial Morphometry, XGBoost, SRTM, Remote Sensing","lastPublishedDoi":"10.21203/rs.3.rs-8993426/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8993426/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFlooding remains one of the most frequent and destructive hazards in Nigeria\u0026rsquo;s Niger Delta, where flat terrain and intense rainfall combine with rapid land-use change to heighten risk. This study integrates morphometric basin analysis with supervised machine learning to enhance flood-susceptibility mapping across four representative catchments in Bayelsa State Forcados, Nun, Ekole, and Seibri. Using a 30 m SRTM digital elevation model, fourteen morphometric parameters were derived to quantify drainage efficiency, infiltration potential, and relief characteristics that influence runoff generation. These parameters served as predictors for Random Forest, Support Vector Machine, and XGBoost classifiers trained with flood and non-flood samples extracted from Sentinel-1 SAR data (2018\u0026ndash;2024). Among the tested models, XGBoost achieved the highest performance (accuracy\u0026thinsp;=\u0026thinsp;93.1%, AUC\u0026thinsp;=\u0026thinsp;0.95), reliably delineating high-risk sub-catchments. The resulting probability maps revealed micro-zones localized clusters of elevated flood potential within generally moderate basins such as Ekole, emphasizing the spatial heterogeneity of flood processes. Drainage density (Dd), relief ratio (Rh), and infiltration number (If) were the most influential variables controlling flood susceptibility. Forcados exhibited the greatest hazard (Dd\u0026thinsp;=\u0026thinsp;3.57 km km⁻\u0026sup2;; If\u0026thinsp;=\u0026thinsp;41.80), whereas Seibri and Ekole showed lower overall susceptibility but contained critical micro-zones requiring attention. The approach demonstrates that combining morphometric controls with data-driven classification provides a transferable, high-resolution tool for flood-risk assessment in other tropical deltaic environments lacking dense hydrological networks. 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