Monthly Discharge Forecasting in the Mahanadi Basin Using Fourier-Transformed LSTM, GRU, and Transformer

Monthly Discharge Forecasting in the Mahanadi Basin Using Fourier-Transformed LSTM, GRU, and Transformer | 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 Monthly Discharge Forecasting in the Mahanadi Basin Using Fourier-Transformed LSTM, GRU, and Transformer Shradhanjalee Pradhan, Janhabi Meher This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8799451/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 Reliable monthly discharge forecasting is crucial for water-resources planning, reservoir operation, and flood management in the Mahanadi River basin. This study evaluates six deep learning models, LSTM, GRU, Transformer, and their Fourier-transformed counterparts (FT-LSTM, FT-GRU, FT-TRANS) across three major gauging stations: Kurubhata, Boranda, and Tikarapara. The Fourier Transform is applied to smooth the time-series data, reducing short-term fluctuations while highlighting dominant seasonal and periodic trends before feeding them into the models. This preprocessing step enables the models to focus on the primary discharge dynamics, thereby improving prediction accuracy. Results show that Fourier-based models outperform their standard versions at all stations. Specifically, FT-GRU achieves the highest accuracy at Kurubhata, while FT-LSTM consistently delivers superior and stable performance at Boranda and Tikarapara. The smoothing process enhances the ability of models to capture monsoon-driven seasonal peaks and strengthens their generalisation under varying flow conditions. Overall, the integration of Fourier-based smoothing with deep learning architectures significantly improves the quality of monthly discharge forecasts, reduces errors during both low- and high-flow periods, and provides a practical tool for hydrologists, water managers, and planners in water resource management and flood risk reduction. Periodic trends Discharge forecasting Mahanadi River basin Deep learning LSTM GRU Transformer Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 25 Feb, 2026 Reviewers invited by journal 25 Feb, 2026 Editor assigned by journal 16 Feb, 2026 First submitted to journal 15 Feb, 2026 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. 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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-8799451","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":596940533,"identity":"9db68d0e-8126-41d2-a12d-d36be93256a3","order_by":0,"name":"Shradhanjalee Pradhan","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0009-7872-837X","institution":"Veer Surendra Sai University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Shradhanjalee","middleName":"","lastName":"Pradhan","suffix":""},{"id":596940537,"identity":"9d8c1920-0b10-46c0-9c8c-cabdd63274d3","order_by":1,"name":"Janhabi Meher","email":"","orcid":"","institution":"Veer Surendra Sai University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Janhabi","middleName":"","lastName":"Meher","suffix":""}],"badges":[],"createdAt":"2026-02-05 16:46:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8799451/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8799451/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104400603,"identity":"1c01c8be-5559-44c8-9b77-b1fb89c07540","added_by":"auto","created_at":"2026-03-11 12:10:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2107053,"visible":true,"origin":"","legend":"","description":"","filename":"MANUSCRIPTCopy.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8799451/v1_covered_3430e695-6897-4d18-a67d-d9ac5272d3b7.pdf"}],"financialInterests":"","formattedTitle":"Monthly Discharge Forecasting in the Mahanadi Basin Using Fourier-Transformed LSTM, GRU, and Transformer","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":"water-resources-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"warm","sideBox":"Learn more about [Water Resources Management](https://www.springer.com/journal/11269)","snPcode":"11269","submissionUrl":"https://submission.nature.com/new-submission/11269/3","title":"Water Resources Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Periodic trends, Discharge forecasting, Mahanadi River basin, Deep learning, LSTM, GRU, Transformer","lastPublishedDoi":"10.21203/rs.3.rs-8799451/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8799451/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eReliable monthly discharge forecasting is crucial for water-resources planning, reservoir operation, and flood management in the Mahanadi River basin. This study evaluates six deep learning models, LSTM, GRU, Transformer, and their Fourier-transformed counterparts (FT-LSTM, FT-GRU, FT-TRANS) across three major gauging stations: Kurubhata, Boranda, and Tikarapara. The Fourier Transform is applied to smooth the time-series data, reducing short-term fluctuations while highlighting dominant seasonal and periodic trends before feeding them into the models. This preprocessing step enables the models to focus on the primary discharge dynamics, thereby improving prediction accuracy. Results show that Fourier-based models outperform their standard versions at all stations. Specifically, FT-GRU achieves the highest accuracy at Kurubhata, while FT-LSTM consistently delivers superior and stable performance at Boranda and Tikarapara. The smoothing process enhances the ability of models to capture monsoon-driven seasonal peaks and strengthens their generalisation under varying flow conditions. 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