A Precise Global Ionospheric Total Electron Content Forecasting Model Based on Multi-Neural Network Ensemble

A Precise Global Ionospheric Total Electron Content Forecasting Model Based on Multi-Neural Network Ensemble | 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 A Precise Global Ionospheric Total Electron Content Forecasting Model Based on Multi-Neural Network Ensemble Zhang Huan, Han Yuanyi, Peng Mengzhu, Zhang Bao, Xu Chaoqian, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6610380/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract The ionospheric total electron content (TEC) is a crucial parameter for studying the dynamic changes in the ionosphere. Accurate forecasting of TEC is significant for research related to space weather phenomena such as auroras and magnetic storms, as well as for long-distance communication and high-precision positioning using global navigation satellite systems (GNSS). Due to the nonlinear and highly irregular distribution of global TEC, existing forecasting models exhibit low efficiency. This study proposes a high-precision forecasting model for global TEC based on the squeeze-and-excitation (SE) attention mechanism and a combination of convolutional neural networks (CNN) and bidirectional long short-term memory (BILSTM) networks. In the data preprocessing stage, the SHAP value algorithm is employed to extract the six most significant feature parameters contributing to TEC forecasting. The model then leverages CNN and BILSTM algorithms to thoroughly explore both long and short-term dependencies in TEC data, while the SE attention mechanism is utilized to redistribute weights to the critically influential features, enabling precise forecasting of global TEC. Forecasting experiments were conducted on global TEC, and magnetic storms were categorized based on geomagnetic indices to investigate the model's accuracy across different storm levels. The experimental results indicate that the new model proposed in this study achieves an average accuracy of 2.59 TECU for ionospheric TEC forecasting, significantly outperforming similar models. When compared to the currently best-performing model, this new approach demonstrates a 24.3% improvement in accuracy, along with a marked reduction in training time. These findings suggest that the new CNNBILSTM_SE model developed in this research enhances forecasting precision, shortens model training duration, and improves the overall efficiency of forecasting models. This advancement holds significant research implications and practical value for applications in space weather prediction and high-precision GNSS positioning. Ionospheric Total Electron Content Electron Density Ensemble of Multiple Machine Learning Algorithms Geomagnetic Index Geomagnetic Activity Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 22 Sep, 2025 Reviews received at journal 18 Sep, 2025 Reviewers agreed at journal 01 Sep, 2025 Reviews received at journal 05 Aug, 2025 Reviewers agreed at journal 05 Jul, 2025 Reviewers agreed at journal 05 Jul, 2025 Reviewers agreed at journal 19 Jun, 2025 Reviews received at journal 08 Jun, 2025 Reviewers agreed at journal 02 Jun, 2025 Reviewers invited by journal 02 Jun, 2025 Editor assigned by journal 20 May, 2025 Submission checks completed at journal 08 May, 2025 First submitted to journal 07 May, 2025 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-6610380","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":465538919,"identity":"647722ab-b027-45c1-b7c7-03fc6dffde75","order_by":0,"name":"Zhang Huan","email":"","orcid":"","institution":"Chongqing Jiaotong University","correspondingAuthor":false,"prefix":"","firstName":"Zhang","middleName":"","lastName":"Huan","suffix":""},{"id":465538921,"identity":"6f3da1d2-35e4-438d-8178-bfef75db1686","order_by":1,"name":"Han Yuanyi","email":"","orcid":"","institution":"Chongqing Jiaotong 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