A Prediction Model of Ionospheric Total Electron Content Based on Grid-Optimized Support Vector Regression

被引:0
|
作者
Yu, Qiao [1 ]
Men, Xiaobin [1 ]
Wang, Jian [1 ,2 ,3 ]
机构
[1] Tianjin Univ, Sch Microelect, Tianjin 300072, Peoples R China
[2] Tianjin Univ, Qingdao Inst Ocean Technol, Qingdao 266200, Peoples R China
[3] Shandong Engn Technol Res Ctr Ocean Informat Aware, Qingdao 266200, Peoples R China
关键词
ionosphere; total electron content; prediction; support vector regression; international reference ionosphere; statistical machine learning; MACHINE; REGION; FLUX;
D O I
10.3390/rs16152701
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
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页数:14
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