Multispectral CNN-RF Approach for Shoreline Extraction and Climate-Change Driven Coastal Erosion Risk Assessment

Multispectral CNN-RF Approach for Shoreline Extraction and Climate-Change Driven Coastal Erosion Risk Assessment | 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 Multispectral CNN-RF Approach for Shoreline Extraction and Climate-Change Driven Coastal Erosion Risk Assessment Hafsa Aeman, Imran Nadeem, Hong Shu, Hamera Aisha This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7842633/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 Sea-level rise (SLR) driven by climate change is accelerating coastal erosion, which necessitates comprehensive assessment of shoreline dynamics to identify areas for targeted mitigation and adaptive planning. However, extracting coastlines at the sub-pixel level from medium spatial data with the limited annotated large-scale information availability hinder comprehensive risk assessment under climate change projections. This study utilizes geospatial techniques and machine learning based multispectral indices approach combined with Convolutional Neural Network (CNN) and Random Forest (RF) to quantify the erosion and accretion along the 1,365 km coastline of Pakistan with historic and climate change scenarios. Shoreline trends were analyzed, and future projections were derived from CMIP6 General Circulation Models (GCMs) under Shared Socioeconomic Pathways (SSPs) for 2020 to 2050. Results reveal pronounced erosion along the Indus Delta, with retreat rates reaching cumulative − 150.4 ± 1.02 m with increase in SLR (0.015–0.15 m) from 2000 to 2020, accompanied by increases in sea surface temperature (297–301 K). Sandspit coast showed up to 23.24 km² of accretion at 89.45 ± 0.23 m, while Gwadar Port experienced accretion rates up to 50 m with annual temperature increases of 0.02°C-0.05°C. Under the high emission SSP5-8.5 scenario, persistent erosion of -90 ± 1.35 m is expected in the Indus Delta by 2050. Projections across Global Warming Levels (GWL) with 1.5-5°C suggest SLR may reach 0.23 m at 5°C warming. Our findings underscore the importance of Integrated Coastal Zone Management (ICZM) and reinforce the IPCC’s call to limit global warming to safeguard coastal ecosystems and achieve SDG 13 and 15 targets by 2030. Sea-level rise (SLR) climate change shoreline dynamics Random Forest (RF) Global Warming Levels (GWL) Integrated Coastal Zone Management (ICZM) Full Text Supplementary Files SupplementaryMaterial.docx 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-7842633","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":534354568,"identity":"b46476ec-6144-4e7e-9bf5-b349098cfdc9","order_by":0,"name":"Hafsa 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Assessment","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":"Sea-level rise (SLR), climate change, shoreline dynamics; Random Forest (RF), Global Warming Levels (GWL), Integrated Coastal Zone Management (ICZM)","lastPublishedDoi":"10.21203/rs.3.rs-7842633/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7842633/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSea-level rise (SLR) driven by climate change is accelerating coastal erosion, which necessitates comprehensive assessment of shoreline dynamics to identify areas for targeted mitigation and adaptive planning. However, extracting coastlines at the sub-pixel level from medium spatial data with the limited annotated large-scale information availability hinder comprehensive risk assessment under climate change projections. This study utilizes geospatial techniques and machine learning based multispectral indices approach combined with Convolutional Neural Network (CNN) and Random Forest (RF) to quantify the erosion and accretion along the 1,365 km coastline of Pakistan with historic and climate change scenarios. Shoreline trends were analyzed, and future projections were derived from CMIP6 General Circulation Models (GCMs) under Shared Socioeconomic Pathways (SSPs) for 2020 to 2050. Results reveal pronounced erosion along the Indus Delta, with retreat rates reaching cumulative \u0026minus;\u0026thinsp;150.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02 m with increase in SLR (0.015\u0026ndash;0.15 m) from 2000 to 2020, accompanied by increases in sea surface temperature (297\u0026ndash;301 K). Sandspit coast showed up to 23.24 km\u0026sup2; of accretion at 89.45\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23 m, while Gwadar Port experienced accretion rates up to 50 m with annual temperature increases of 0.02\u0026deg;C-0.05\u0026deg;C. Under the high emission SSP5-8.5 scenario, persistent erosion of -90\u0026thinsp;\u0026plusmn;\u0026thinsp;1.35 m is expected in the Indus Delta by 2050. Projections across Global Warming Levels (GWL) with 1.5-5\u0026deg;C suggest SLR may reach 0.23 m at 5\u0026deg;C warming. Our findings underscore the importance of Integrated Coastal Zone Management (ICZM) and reinforce the IPCC\u0026rsquo;s call to limit global warming to safeguard coastal ecosystems and achieve SDG 13 and 15 targets by 2030.\u003c/p\u003e","manuscriptTitle":"Multispectral CNN-RF Approach for Shoreline Extraction and Climate-Change Driven Coastal Erosion Risk Assessment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-05 15:32:53","doi":"10.21203/rs.3.rs-7842633/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"7383c42f-7426-4ae0-86d8-49a007a959f4","owner":[],"postedDate":"November 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-12T17:20:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-05 15:32:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7842633","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7842633","identity":"rs-7842633","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}