GCBi-ScintNet: Predicting GNSS Positioning Errors under Ionospheric Scintillation with a GA-CNN-BiLSTM Hybrid Model

GCBi-ScintNet: Predicting GNSS Positioning Errors under Ionospheric Scintillation with a GA-CNN-BiLSTM Hybrid Model | 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 GCBi-ScintNet: Predicting GNSS Positioning Errors under Ionospheric Scintillation with a GA-CNN-BiLSTM Hybrid Model Chendong Li, Matthew Pike, Dongsheng Zhao, Linlin Shen, Alejandro Guerra Manzanares, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8943877/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 Ionospheric scintillation during periods of high solar activity significantly degrades the accuracy and reliability of Global Navigation Satellite System (GNSS) positioning. While prior research has largely focused on predicting long-term positional variations under relatively quiet conditions or scintillation indices separately, the direct forecasting of positioning errors under strong ionospheric disturbances remains scarcely explored. In this study, we develop a hybrid machine learning model integrating Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Genetic Algorithm (GA) to predict scintillation-induced 3D positioning errors. Using the Rate of Total Electron Content Index (ROTI) values—computed from 30-second GNSS data—as well as satellite numbers, Geometric Dilution of Precision (GDOP), and timestamps, the model was trained on a high-latitude station (LERI, 2023) and tested across multiple stations in 2023 and 2024. Results show strong agreement between predicted and actual errors, with Pearson Correlation Coefficients (PCC) reaching 0.816 at BOAV in 2024, respectively. The model reduced Root Mean Squre Error (RMSE) by a maximum of 50.7% at NYA1 in 2024. Performance varied by receiver type and latitude: stations sharing the same receiver type as the training station showed higher prediction accuracy, and low-latitude sites showed more days with very high correlation (PCC ≥ 0.9). These findings highlight the model’s ability to capture error trends across diverse ionospheric conditions, while also underscoring the impact of data source and geographic location on prediction accuracy. Bidirectional Long Short-Term Memory (BiLSTM) Convolutional Neural Network (CNN) Genetic Algorithm (GA) Global Navigation Satellite System (GNSS) ionospheric scintillation positioning error prediction 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-8943877","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":607502447,"identity":"f9b1b92b-1bf3-4edf-b8fc-144d7ef7597a","order_by":0,"name":"Chendong Li","email":"","orcid":"","institution":"Zhejiang Normal University","correspondingAuthor":false,"prefix":"","firstName":"Chendong","middleName":"","lastName":"Li","suffix":""},{"id":607502448,"identity":"87f5d0ce-e2ed-4ca9-980a-855699f5f81c","order_by":1,"name":"Matthew Pike","email":"","orcid":"","institution":"the University of Nottingham Ningbo China","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"","lastName":"Pike","suffix":""},{"id":607502449,"identity":"eeff842c-257c-4f83-96a9-c67a746a02cb","order_by":2,"name":"Dongsheng Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYDCCAwxsQNIGymMjXksaEDOTpuUwCVr4zh9/9uBHxXl5g2vnDzB8KDvMwD+7Ab8WyRsJ6YY9Z24bbridzMA449xhBok7B/BrMbjBcEyCt+12ggFQCzNv22EGA4kEAlrOH2yT/Nt2DqLlL1FaDiSzSfO2HYBoYSRGi+SNNDZpmTPJhjNvJxsc7DmXziNxg4AWUIhJvqmwk+e7nfjwwY8yazn+GQS0oIADQMxDgvpRMApGwSgYBbgAAD4IRJd3j2KgAAAAAElFTkSuQmCC","orcid":"","institution":"China University of Mining and Technology","correspondingAuthor":true,"prefix":"","firstName":"Dongsheng","middleName":"","lastName":"Zhao","suffix":""},{"id":607502450,"identity":"50440051-a984-4c10-b731-baf86315243e","order_by":3,"name":"Linlin Shen","email":"","orcid":"","institution":"Shenzhen University","correspondingAuthor":false,"prefix":"","firstName":"Linlin","middleName":"","lastName":"Shen","suffix":""},{"id":607502451,"identity":"392319bb-442b-40f5-a09e-973cf41641e7","order_by":4,"name":"Alejandro Guerra Manzanares","email":"","orcid":"","institution":"the University of Nottingham Ningbo China","correspondingAuthor":false,"prefix":"","firstName":"Alejandro","middleName":"Guerra","lastName":"Manzanares","suffix":""},{"id":607502452,"identity":"ab483fc0-3bdc-4ed4-a0c5-0de4c1627c2e","order_by":5,"name":"Nicholas A.S. 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While prior research has largely focused on predicting long-term positional variations under relatively quiet conditions or scintillation indices separately, the direct forecasting of positioning errors under strong ionospheric disturbances remains scarcely explored. In this study, we develop a hybrid machine learning model integrating Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Genetic Algorithm (GA) to predict scintillation-induced 3D positioning errors. Using the Rate of Total Electron Content Index (ROTI) values\u0026mdash;computed from 30-second GNSS data\u0026mdash;as well as satellite numbers, Geometric Dilution of Precision (GDOP), and timestamps, the model was trained on a high-latitude station (LERI, 2023) and tested across multiple stations in 2023 and 2024. Results show strong agreement between predicted and actual errors, with Pearson Correlation Coefficients (PCC) reaching 0.816 at BOAV in 2024, respectively. The model reduced Root Mean Squre Error (RMSE) by a maximum of 50.7% at NYA1 in 2024. Performance varied by receiver type and latitude: stations sharing the same receiver type as the training station showed higher prediction accuracy, and low-latitude sites showed more days with very high correlation (PCC\u0026thinsp;\u0026ge;\u0026thinsp;0.9). These findings highlight the model\u0026rsquo;s ability to capture error trends across diverse ionospheric conditions, while also underscoring the impact of data source and geographic location on prediction accuracy.\u003c/p\u003e","manuscriptTitle":"GCBi-ScintNet: Predicting GNSS Positioning Errors under Ionospheric Scintillation with a GA-CNN-BiLSTM Hybrid Model","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-19 06:01:21","doi":"10.21203/rs.3.rs-8943877/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"efb1b5a0-da73-42aa-8d38-cce650688f76","owner":[],"postedDate":"March 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T00:53:56+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-19 06:01:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8943877","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8943877","identity":"rs-8943877","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}