Deep Learning for Ocean Parameters Prediction: A Systematic Literature Review

Deep Learning for Ocean Parameters Prediction: A Systematic Literature Review | 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 Systematic Review Deep Learning for Ocean Parameters Prediction: A Systematic Literature Review Wei Wang, Anqi Lu, Zheng Jiang, Gaowei zhang, YI Wang, Lingyu Xu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7460394/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 Accurately predicting ocean temperature, salinity, and velocity is fundamental to capture the dynamics of subsurface thermohaline structure and understanding its impacts on marine ecosystems and the Earth’s climate system. With the rise of Deep Learning (DL), researchers have explored its potential to capture complex spatiotemporal dependencies in ocean parameter prediction. However, a comprehensive understanding of the application of DL in this field remains in its early stage. To address this gap, we conduct a systematic review of 122 peer-reviewed articles published between 2013 and 2024, aiming to answer four key Research Questions (RQs). In RQ1, we examine how marine datasets are categorized and represented for ocean parameter prediction. RQ2 explores the spatial, temporal, and input-level patterns of these datasets as utilized across various DL models. In RQ3, we analyze the DL architectures adopted for forecasting temperature, salinity, and velocity, highlighting trends across model types. Finally, RQ4 investigates the optimization strategies and evaluation metrics employed to assess predictive performance. Based on our findings, we highlight underexplored challenges and outline promising directions for future research. Artificial Intelligence and Machine Learning deep learning ocean parameter prediction thermohaline structure survey Full Text Additional Declarations The authors declare no competing interests. The graphical abstract file and the supplementary material file are not available with this version. 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. 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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-7460394","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":505676158,"identity":"6006ea05-3b65-4c6e-b61e-3ae7ed516691","order_by":0,"name":"Wei Wang","email":"","orcid":"","institution":"BUPT","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Wang","suffix":""},{"id":505676159,"identity":"f6aa592b-f846-49af-be7d-b7f480447d5e","order_by":1,"name":"Anqi 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