Physics-Constrained Deep Learning for Mangrove Distribution and Wind Modeling

Physics-Constrained Deep Learning for Mangrove Distribution and Wind Modeling | 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 Physics-Constrained Deep Learning for Mangrove Distribution and Wind Modeling Yulison Herry Chrisnanto, Julian Evan Chrisnanto This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8233891/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 This study introduces a physics-informed neural network (PINN) framework that integrates remote sensing with physical principles for multi-scale coastal ecosystem monitoring. A hybrid PINN-CNN-LSTM processes Sentinel-1/2 imagery and ERA5 data while enforcing conservation laws to predict mangrove distribution and coastal wind dynamics. Applied to East Luwu, South Sulawesi, Indonesia, the model attains 93.6% accuracy for mangrove classification (RMSE = 0.14731) and strong wind prediction performance (R2 = 0.91, RMSE = 0.048 m/s). Physical validation confirms drag coefficients (Cd = 0.8–1.4) and 97.9% energy closure, consistent with canopy flow theory. Cross-site validation across three mangrove systems shows transferability with <15% RMSE increase. The framework supports regional-to-global monitoring, offering mechanistic insights into carbon sequestration with an estimated 198,535 tons C/month capacity under physically constrained uncertainty. These results demonstrate the framework’s potential for credible blue carbon assessment and ESG-aligned coastal management. coastal hydrodynamics mangrove ecosystems physics-informed neural networks momentum transfer sediment transport carbon cycling biogeomorphology conservation laws Full Text Additional Declarations No competing interests reported. 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-8233891","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":562655477,"identity":"2e69aa96-38dd-4729-bd25-6b96f8846efb","order_by":0,"name":"Yulison Herry Chrisnanto","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYDACCQbGBwk2INYBBoYEEM3MYEBIC7NBQhqJWtgkGNJQxfBrMbjd/KziQQJD4nbGw88ePGCwk2dgZ96AX8udY2Y3EoBadjYcMzdIYEg2bGBmK8Cv5UYO243EHwyJGw4cMJNIYGAGIh4CDgNqKQDZsuHA8W9ALfXEaWGAaDkDsuUwYS2SN9KMJRISJIyBWsokEgyOG7YR8gvfjeSHH38k2MhuuHF8m+SPimp5fv7D+ENM4QCYkgAiEAvoJDa86oFAvgHG4m/ArWoUjIJRMApGNgAANfpFKuYSu14AAAAASUVORK5CYII=","orcid":"","institution":"Jenderal of Achmad Yani University","correspondingAuthor":true,"prefix":"","firstName":"Yulison","middleName":"Herry","lastName":"Chrisnanto","suffix":""},{"id":562655479,"identity":"c8daf581-f107-42c3-9602-db8f71d36875","order_by":1,"name":"Julian Evan Chrisnanto","email":"","orcid":"","institution":"Tokyo University of Agriculture and Technology","correspondingAuthor":false,"prefix":"","firstName":"Julian","middleName":"Evan","lastName":"Chrisnanto","suffix":""}],"badges":[],"createdAt":"2025-11-29 02:38:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8233891/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8233891/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105247805,"identity":"12dbda78-c65b-4dfd-a053-a5806dc25580","added_by":"auto","created_at":"2026-03-24 02:10:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4817893,"visible":true,"origin":"","legend":"","description":"","filename":"PINNMangroveElsevier2Wind.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8233891/v1_covered_969304ef-0930-4d41-a4f1-09a76837340c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Physics-Constrained Deep Learning for Mangrove Distribution and Wind Modeling","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":"coastal hydrodynamics, mangrove ecosystems, physics-informed neural networks, momentum transfer, sediment transport, carbon cycling, biogeomorphology, conservation laws","lastPublishedDoi":"10.21203/rs.3.rs-8233891/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8233891/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"This study introduces a physics-informed neural network (PINN) framework that integrates remote sensing with physical principles for multi-scale coastal ecosystem monitoring. 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