Water levels forecasting for the river basins of Kien Giang in Quang Binh province using Transformer-based approaches

Water levels forecasting for the river basins of Kien Giang in Quang Binh province using Transformer-based approaches | 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 Water levels forecasting for the river basins of Kien Giang in Quang Binh province using Transformer-based approaches Bao Bui Quoc, Hung Nguyen Khanh, Hieu Nguyen Dac, Dat Tran Anh, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3877743/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Precision and timely forecasting of water levels holds critical significance across diverse applications such as flood prediction, hydroelectric power management, and environmental surveillance. While conventional recurrent neural network (RNN)-based techniques have been extensively employed for this purpose, recent strides in long-term time-series forecasting have ushered in transformer-based models, showcasing remarkable advancements in predicting time-series data. This study delves into the utilization of transformer-based models for water level prediction, specifically concentrating on the Nhat Le River Basin. Through numerous experiments encompassing different scenarios and employing various model architectures, we offer detailed analyses of the model's predictive capabilities. Demonstrating consistent superiority over traditional RNN-based methods, the transformer-based models excel across various evaluation metrics, including root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). Notably, these models exhibit outstanding precision in forecasting flood peaks, consistently maintaining errors below 0.02 meters. The resilience and scalability of transformer-based models position them as highly promising for achieving accurate water-level predictions in practical, real-world applications. Water level prediction Time series forecasting Transformer-based models Deep learning Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 18 Mar, 2024 Reviewers invited by journal 29 Feb, 2024 Editor assigned by journal 16 Feb, 2024 First submitted to journal 15 Feb, 2024 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-3877743","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":275791324,"identity":"d7fd8427-006f-4dce-8df2-b2aad004a665","order_by":0,"name":"Bao Bui Quoc","email":"","orcid":"","institution":"Hanoi University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Bao","middleName":"Bui","lastName":"Quoc","suffix":""},{"id":275791325,"identity":"8fe1fc28-ced7-4452-a8b6-981b17048abe","order_by":1,"name":"Hung Nguyen Khanh","email":"","orcid":"","institution":"Hanoi University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Hung","middleName":"Nguyen","lastName":"Khanh","suffix":""},{"id":275791326,"identity":"efa196b1-9763-413a-82cf-11168fde8520","order_by":2,"name":"Hieu Nguyen Dac","email":"","orcid":"","institution":"Water Resources University: Thuy Loi University","correspondingAuthor":false,"prefix":"","firstName":"Hieu","middleName":"Nguyen","lastName":"Dac","suffix":""},{"id":275791327,"identity":"56e61f1b-7a4c-4c96-be94-a4fca3f6b6a9","order_by":3,"name":"Dat Tran Anh","email":"","orcid":"","institution":"Water Resources University: Thuy Loi University","correspondingAuthor":false,"prefix":"","firstName":"Dat","middleName":"Tran","lastName":"Anh","suffix":""},{"id":275791328,"identity":"029dca7a-fc74-4ad0-aebc-05a267dd9516","order_by":4,"name":"Quang Chieu TA","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/UlEQVRIiWNgGAWjYPCCAzwQuoJBBkRJEKuFsYHhjAEP0VoYwFoY24jQwncj+dnDLzV3ZPglEtgf/Jz3h8fgAPPB2zwMh/NwaZG8kWZuLHPsGY/kjATGxt5tBkAtbMnWQC3FuLQY3Egwk5ZsOMwDZDA28IK18JhJ8zCkJTbg1JL+DazFHqil8e8ckBb+bwS05JhJfgTZIpHA2MzbALaFDajFBqcWyTNvyqQZjh3mkTjzsHG2zDFjHsnDbMaWcwxwa+E7nr5N8kfNYXv+9uQDH9/UyMnxHW9+eONNhQROLaAYYYZEPSNUDTPYwbjUQ7Qw/sAjPwpGwSgYBaOAAQALG1V5liyBtQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0001-5079-7762","institution":"Water Resources University: Thuy Loi University","correspondingAuthor":true,"prefix":"","firstName":"Quang","middleName":"Chieu","lastName":"TA","suffix":""}],"badges":[],"createdAt":"2024-01-19 05:28:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3877743/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3877743/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51987158,"identity":"41f113bc-0509-433a-825e-413553f2ed53","added_by":"auto","created_at":"2024-03-05 02:48:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":501781,"visible":true,"origin":"","legend":"","description":"","filename":"SoftComputingSubmissionWaterlevelprediction.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3877743/v1_covered_668ad09e-20ac-4e86-b441-d6caab991b28.pdf"}],"financialInterests":"","formattedTitle":"Water levels forecasting for the river basins of Kien Giang in Quang Binh province using Transformer-based approaches","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"soft-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"soco","sideBox":"Learn more about [Soft Computing](https://www.springer.com/journal/500)","snPcode":"500","submissionUrl":"https://submission.nature.com/new-submission/500/3","title":"Soft Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Water level prediction, Time series forecasting, Transformer-based models, Deep learning","lastPublishedDoi":"10.21203/rs.3.rs-3877743/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3877743/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Precision and timely forecasting of water levels holds critical significance across diverse applications such as flood prediction, hydroelectric power management, and environmental surveillance. 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