Short-Term Tidal Forecasting Based on a TPE-Optimized Hybrid BiGRU–ResNet Framework

Short-Term Tidal Forecasting Based on a TPE-Optimized Hybrid BiGRU–ResNet Framework | 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 Article Short-Term Tidal Forecasting Based on a TPE-Optimized Hybrid BiGRU–ResNet Framework Abdulrazak H. Almaliki, Jihad A. Younis This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7322013/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 Tidal level prediction is significant in maritime navigation, coastal development planning, and the anticipation of coastal hazards. This research proposes a hybrid deep learning approach that incorporates a Bidirectional Gated Recurrent Unit (BiGRU) network and a Residual Neural Network (ResNet) for short-term tidal level estimation. The BiGRU component captures temporal dependencies from forward and backward directions, while the ResNet module performs deep feature learning based on residual learning to capture sequential and spatial-temporal complicated patterns in tidal signals. Hyperparameters of both BiGRU and ResNet components were tuned by the Tree-structured Parzen Estimator (TPE) through optimizing hyperparameters of the model by probabilistic search procedure. Tidal level data collected from Ras Tanura during the time period from 2012 to 2021 were used in developing and testing the proposed hybrid BiGRU–ResNet framework. Forecasting performance was tested at short horizons of 5, 10, 15, and 20 days ahead. The proposed model outperformed the BiLSTM, BiGRU, and CNN models across all forecasting horizons, achieving lower prediction errors at each test interval. The outcomes indicate that the proposed hybrid model produces satisfactory short-term tidal level forecasts for coastal development and maritime activity. Physical sciences/Mathematics and computing Earth and environmental sciences/Ocean sciences Forecasting Tide Deep Learning Time Series Analysis 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-7322013","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":561613157,"identity":"ac807ef3-8f26-436e-b900-a342b3de8faf","order_by":0,"name":"Abdulrazak H. Almaliki","email":"","orcid":"","institution":"Taif University","correspondingAuthor":false,"prefix":"","firstName":"Abdulrazak","middleName":"H.","lastName":"Almaliki","suffix":""},{"id":561613163,"identity":"b736d3b8-84fb-4a1c-93a3-7da082c61826","order_by":1,"name":"Jihad A. 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