A Machine Learning-Based Method for Modeling TEC Regional Temporal-Spatial Map
In order to achieve the high-accuracy prediction of the total electron content (TEC) of the regional ionosphere for supporting the application of satellite navigation, positioning, measurement, and controlling, we proposed a modeling method based on machine learning (ML) and use this method to estab...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/21/5579 |
| _version_ | 1797466642529124352 |
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| author | Yiran Liu Jian Wang Cheng Yang Yu Zheng Haipeng Fu |
| author_facet | Yiran Liu Jian Wang Cheng Yang Yu Zheng Haipeng Fu |
| author_sort | Yiran Liu |
| collection | DOAJ |
| description | In order to achieve the high-accuracy prediction of the total electron content (TEC) of the regional ionosphere for supporting the application of satellite navigation, positioning, measurement, and controlling, we proposed a modeling method based on machine learning (ML) and use this method to establish an empirical prediction model of TEC for parts of Europe. The model has three main characteristics: (1) The principal component analysis (PCA) is used to separate TEC’s temporal and spatial variation characteristics and to establish its corresponding map, (2) the solar activity parameters of the 12-month mean flux of the solar radio waves at 10.7 cm (<i>F</i>10.7<sub>12</sub>) and the 12-month mean sunspot number (<i>R</i><sub>12</sub>) are introduced into the temporal map as independent variables to reflect the temporal variation characteristics of TEC, and (3) The modified Kriging spatial interpolation method is used to achieve the spatial reconstruction of TEC. Finally, the regression learning method is used to determine the coefficients and harmonic numbers of the model by using the root mean square error (RMSE) and its relative value (RRMSE) as the evaluation standard. Specially, the modeling process is easy to understand, and the determined model parameters are interpretable. The statistical results show that the monthly mean values of TEC predicted by the proposed model in this paper are highly consistent with the observed values curve of TEC, and the RRMSE of the predicted results is 12.76%. Furthermore, comparing the proposed model with the IRI model, it can be found that the prediction accuracy of TEC by the proposed model is much higher than that of the IRI model either with CCIR or URSI coefficients, and the improvement is 38.63% and 35.79%, respectively. |
| first_indexed | 2024-03-09T18:41:37Z |
| format | Article |
| id | doaj.art-f98a3c5b006343fdbe7264db66591ffe |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T18:41:37Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-f98a3c5b006343fdbe7264db66591ffe2023-11-24T06:41:21ZengMDPI AGRemote Sensing2072-42922022-11-011421557910.3390/rs14215579A Machine Learning-Based Method for Modeling TEC Regional Temporal-Spatial MapYiran Liu0Jian Wang1Cheng Yang2Yu Zheng3Haipeng Fu4School of Microelectronics, Tianjin University, Tianjin 300072, ChinaSchool of Microelectronics, Tianjin University, Tianjin 300072, ChinaSchool of Microelectronics, Tianjin University, Tianjin 300072, ChinaCollege Electronic and Information, Qingdao University, Qingdao 266071, ChinaSchool of Microelectronics, Tianjin University, Tianjin 300072, ChinaIn order to achieve the high-accuracy prediction of the total electron content (TEC) of the regional ionosphere for supporting the application of satellite navigation, positioning, measurement, and controlling, we proposed a modeling method based on machine learning (ML) and use this method to establish an empirical prediction model of TEC for parts of Europe. The model has three main characteristics: (1) The principal component analysis (PCA) is used to separate TEC’s temporal and spatial variation characteristics and to establish its corresponding map, (2) the solar activity parameters of the 12-month mean flux of the solar radio waves at 10.7 cm (<i>F</i>10.7<sub>12</sub>) and the 12-month mean sunspot number (<i>R</i><sub>12</sub>) are introduced into the temporal map as independent variables to reflect the temporal variation characteristics of TEC, and (3) The modified Kriging spatial interpolation method is used to achieve the spatial reconstruction of TEC. Finally, the regression learning method is used to determine the coefficients and harmonic numbers of the model by using the root mean square error (RMSE) and its relative value (RRMSE) as the evaluation standard. Specially, the modeling process is easy to understand, and the determined model parameters are interpretable. The statistical results show that the monthly mean values of TEC predicted by the proposed model in this paper are highly consistent with the observed values curve of TEC, and the RRMSE of the predicted results is 12.76%. Furthermore, comparing the proposed model with the IRI model, it can be found that the prediction accuracy of TEC by the proposed model is much higher than that of the IRI model either with CCIR or URSI coefficients, and the improvement is 38.63% and 35.79%, respectively.https://www.mdpi.com/2072-4292/14/21/5579ionospheremachine learningprincipal component analysisTEC |
| spellingShingle | Yiran Liu Jian Wang Cheng Yang Yu Zheng Haipeng Fu A Machine Learning-Based Method for Modeling TEC Regional Temporal-Spatial Map Remote Sensing ionosphere machine learning principal component analysis TEC |
| title | A Machine Learning-Based Method for Modeling TEC Regional Temporal-Spatial Map |
| title_full | A Machine Learning-Based Method for Modeling TEC Regional Temporal-Spatial Map |
| title_fullStr | A Machine Learning-Based Method for Modeling TEC Regional Temporal-Spatial Map |
| title_full_unstemmed | A Machine Learning-Based Method for Modeling TEC Regional Temporal-Spatial Map |
| title_short | A Machine Learning-Based Method for Modeling TEC Regional Temporal-Spatial Map |
| title_sort | machine learning based method for modeling tec regional temporal spatial map |
| topic | ionosphere machine learning principal component analysis TEC |
| url | https://www.mdpi.com/2072-4292/14/21/5579 |
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