EoFTCNets: Efficient Solar Flare Nowcasting using 3D Temporal Convolutional Networks (3DTCN)

EoFTCNets: Efficient Solar Flare Nowcasting using 3D Temporal Convolutional Networks (3DTCN) | 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 EoFTCNets: Efficient Solar Flare Nowcasting using 3D Temporal Convolutional Networks (3DTCN) Besma Guesmi, Jinen Daghrir, David Moloney, José Luis Espinosa Aranda, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5946730/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 In the evolving landscape of 21st-century space science, forecasting space weather events such as solar flares and Coronal Mass Ejections (CMEs) are crucial yet challenging. Solar flares are intense bursts of radiation caused by the release of magnetic energy in active regions and are often accompanied byCMEs. These events can significantly impact Earth’s space environment, causing disruptions in radio communication, satellite operations, and power grids. Monitoring the temporal evolution of active regions and providing early warnings of solar flares is essential to mitigate these risks. Deep learning techniques have demonstrated significant success in detecting and predicting time-dependent events. By leveraging spatial data through convolution operations with temporal correlations, we introduce 3D Temporal Convolutional Networks(3DTCNs) to efficiently analyze active region patches over time, leveraging spatial and temporal correlations. Additionally, we introduce separate predictor modules based on flare classification to enhance the performance of our EoFTC-Nets (Eye-on-Flare Temporal Convolutional Networks) nowcasting system. Our results demonstrate that the proposed architecture matches or outperforms state-of-the-art approaches in the literature, achieving an accuracy exceeding 96% for a 24-hour forecasting window. Furthermore, the model is computationally efficient, consuming approximately 1.2 watts on Intel Movidius Myriad X, making it well-suited for onboard deployment and real-time space weather monitoring. solar physics space weather solar flare active region nowcasting forecasting deep learning time series 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. 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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-5946730","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":411051031,"identity":"f337af78-c201-47a7-bd44-11fc2610fe4d","order_by":0,"name":"Besma Guesmi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYBACA2TOgQ8MBxgYmEnRcnAGUAsPSVqYeUBaCDnMnL396YYfDLWJ/fynEw/btt2Rs2dnfviBocYmGpcWy54zZjd7GI4nzpyRu+FwbtszYx5mNmMJhmNpuQ24HHYjh+0GD8OxxA03eEFaDif2AJ0nwdhwGLeW+8+f3fwD1LL//NkNhy0hWph/4NVyg8HsNg9DTeIGBqDDGCFa2PDaYtmTY3ZbxuCA8YwbuRsO9pwD+uUwm5lFAh6/mLMff3bzTUWdbH//2c0ffpTdkWPvP/z4xocaG5xaoM47jCaQgFc5GNQRVjIKRsEoGAUjFwAAKs1hyPYFLJoAAAAASUVORK5CYII=","orcid":"","institution":"Ubotica Technologies","correspondingAuthor":true,"prefix":"","firstName":"Besma","middleName":"","lastName":"Guesmi","suffix":""},{"id":411051032,"identity":"3aeb4033-691b-49b1-83c5-7598b451d56e","order_by":1,"name":"Jinen Daghrir","email":"","orcid":"","institution":"Ubotica Technologies","correspondingAuthor":false,"prefix":"","firstName":"Jinen","middleName":"","lastName":"Daghrir","suffix":""},{"id":411051033,"identity":"07758036-5834-4a51-8074-91f070b7a76c","order_by":2,"name":"David Moloney","email":"","orcid":"","institution":"Ubotica Technologies","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"","lastName":"Moloney","suffix":""},{"id":411051034,"identity":"93d2c46b-f383-4e57-b9ab-622249ab3ac9","order_by":3,"name":"José Luis Espinosa Aranda","email":"","orcid":"","institution":"Ubotica Technologies","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"Luis Espinosa","lastName":"Aranda","suffix":""},{"id":411051035,"identity":"a618f5d6-eb47-4820-b1c1-3d458cbc738f","order_by":4,"name":"Elena Hervas Martin","email":"","orcid":"","institution":"Ubotica Technologies","correspondingAuthor":false,"prefix":"","firstName":"Elena","middleName":"Hervas","lastName":"Martin","suffix":""}],"badges":[],"createdAt":"2025-02-02 16:53:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5946730/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5946730/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82694670,"identity":"8d3aa829-db6d-4005-bba4-05163faa2065","added_by":"auto","created_at":"2025-05-14 08:32:11","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":405799,"visible":true,"origin":"","legend":"","description":"","filename":"EoFTCNetsEfficientSolarFlareNowcasting3DTemporalConvolutionalNetworks3DTCNforSolarPhysics.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5946730/v1_covered_70d63e81-d769-4322-a53f-d8155167a0cc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eEoFTCNets: Efficient Solar Flare Nowcasting using 3D Temporal Convolutional Networks (3DTCN)\u003c/p\u003e","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":"solar physics, space weather, solar flare, active region, nowcasting, forecasting, deep learning, time series","lastPublishedDoi":"10.21203/rs.3.rs-5946730/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5946730/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the evolving landscape of 21st-century space science, forecasting space weather events such as solar flares and Coronal Mass Ejections (CMEs) are crucial yet challenging. Solar flares are intense bursts of radiation caused by the release of magnetic energy in active regions and are often accompanied byCMEs. These events can significantly impact Earth’s space environment, causing disruptions in radio communication, satellite operations, and power grids. Monitoring the temporal evolution of active regions and providing early warnings of solar flares is essential to mitigate these risks. Deep learning techniques have demonstrated significant success in detecting and predicting time-dependent events. By leveraging spatial data through convolution operations with temporal correlations, we introduce 3D Temporal Convolutional Networks(3DTCNs) to efficiently analyze active region patches over time, leveraging spatial and temporal correlations. 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