{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T23:58:29Z","timestamp":1773705509059,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2024,7,23]],"date-time":"2024-07-23T00:00:00Z","timestamp":1721692800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information Systems","award":["CEMEE2022G0201"],"award-info":[{"award-number":["CEMEE2022G0201"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Evaluating and mitigating the adverse effects of the ionosphere on communication, navigation, and other services, as well as fully utilizing the ionosphere, have become increasingly prominent topics in the academic community. To quantify the dynamical changes and improve the prediction accuracy of the ionospheric Total Electron Content (TEC), we propose a prediction model based on grid-optimized Support Vector Regression (SVR). This modeling processes include three steps: (1) dividing the dataset for training, validation, and testing; (2) determining the hyperparameters C and g by the grid search method through cross-validation using training and validation data; and (3) testing the trained model using the test data. Taking the Gakona station as an example, we compared the proposed model with the International Reference Ionosphere (IRI) model and a TEC prediction model based on Statistical Machine Learning (SML). The performance of the models was evaluated using the metrics of mean absolute error (MAE) and root mean square error (RMSE). The specific results are as follows: the MAE of the CCIR, URSI, SML, and SVR models compared to the observations are 1.06 TECU, 1.41 TECU, 0.7 TECU, and 0.54 TECU, respectively; the RMSE are 1.36 TECU, 1.62 TECU, 0.92 TECU, and 0.68 TECU, respectively. These results indicate that the SVR model has the most minor prediction error and the highest accuracy for predicting TEC. This method also provides a new approach for predicting other ionospheric parameters.<\/jats:p>","DOI":"10.3390\/rs16152701","type":"journal-article","created":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T08:47:17Z","timestamp":1721810837000},"page":"2701","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Prediction Model of Ionospheric Total Electron Content Based on Grid-Optimized Support Vector Regression"],"prefix":"10.3390","volume":"16","author":[{"given":"Qiao","family":"Yu","sequence":"first","affiliation":[{"name":"School of Microelectronics, Tianjin University, Tianjin 300072, China"}]},{"given":"Xiaobin","family":"Men","sequence":"additional","affiliation":[{"name":"School of Microelectronics, Tianjin University, Tianjin 300072, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4361-8946","authenticated-orcid":false,"given":"Jian","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Microelectronics, Tianjin University, Tianjin 300072, China"},{"name":"Qingdao Institute for Ocean Technology, Tianjin University, Qingdao 266200, China"},{"name":"Shandong Engineering Technology Research Center of Ocean Information Awareness and Transmission, Qingdao 266200, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1175\/1520-0477(2000)081<0677:SARNGN>2.3.CO;2","article-title":"SuomiNet: A Real\u2013Time National GPS Network for Atmospheric Research and Education","volume":"81","author":"Ware","year":"2000","journal-title":"Bull. 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