Machine Learning-Based Estimation of foF2 and MUF(3000)F2 Using GNSS Ionospheric TEC Observations
This study developed machine learning models using different algorithms, including support vector machine (SVM), random forest (RF), and backpropagation neural network (BPNN), to estimate the critical frequency of the F2 layer (foF2) and the maximum usable frequency of the F2 layer for a 3000 km cir...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/17/10/1764 |
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| author | Yuhang Zhang Ming Ou Liang Chen Yi Hao Qinglin Zhu Xiang Dong Weimin Zhen |
| author_facet | Yuhang Zhang Ming Ou Liang Chen Yi Hao Qinglin Zhu Xiang Dong Weimin Zhen |
| author_sort | Yuhang Zhang |
| collection | DOAJ |
| description | This study developed machine learning models using different algorithms, including support vector machine (SVM), random forest (RF), and backpropagation neural network (BPNN), to estimate the critical frequency of the F2 layer (foF2) and the maximum usable frequency of the F2 layer for a 3000 km circuit (MUF(3000)F2) based on the total electron content (TEC) observed by global navigation satellite system (GNSS) receivers. The ionospheric dataset used comprised TEC, foF2, and MUF(3000)F2 measurements from 11 stations in China during a solar activity period (2008–2020). The results indicate that all three machine learning models performed better than the IRI-2020 model, with varying levels of accuracy. For foF2 (MUF(3000)F2) estimation, the root mean square error (RMSE) values at Kunming and Xi’an stations were reduced by approximately 38% (26%) and 18% (11%), respectively, compared to IRI-2020. During geomagnetic disturbances, all three models were able to reproduce the variations in both foF2 and MUF(3000)F2 parameters. Nevertheless, the RF model showed significantly better performance in foF2 estimation compared to the SVM and BPNN models. |
| format | Article |
| id | doaj-art-fe97d118fbf5407bb06110c3fed8cda4 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-fe97d118fbf5407bb06110c3fed8cda42025-08-20T03:47:58ZengMDPI AGRemote Sensing2072-42922025-05-011710176410.3390/rs17101764Machine Learning-Based Estimation of foF2 and MUF(3000)F2 Using GNSS Ionospheric TEC ObservationsYuhang Zhang0Ming Ou1Liang Chen2Yi Hao3Qinglin Zhu4Xiang Dong5Weimin Zhen6China Research Institute of Radiowave Propagation, Qingdao 266100, ChinaChina Research Institute of Radiowave Propagation, Qingdao 266100, ChinaChina Research Institute of Radiowave Propagation, Qingdao 266100, ChinaChina Research Institute of Radiowave Propagation, Qingdao 266100, ChinaChina Research Institute of Radiowave Propagation, Qingdao 266100, ChinaChina Research Institute of Radiowave Propagation, Qingdao 266100, ChinaChina Research Institute of Radiowave Propagation, Qingdao 266100, ChinaThis study developed machine learning models using different algorithms, including support vector machine (SVM), random forest (RF), and backpropagation neural network (BPNN), to estimate the critical frequency of the F2 layer (foF2) and the maximum usable frequency of the F2 layer for a 3000 km circuit (MUF(3000)F2) based on the total electron content (TEC) observed by global navigation satellite system (GNSS) receivers. The ionospheric dataset used comprised TEC, foF2, and MUF(3000)F2 measurements from 11 stations in China during a solar activity period (2008–2020). The results indicate that all three machine learning models performed better than the IRI-2020 model, with varying levels of accuracy. For foF2 (MUF(3000)F2) estimation, the root mean square error (RMSE) values at Kunming and Xi’an stations were reduced by approximately 38% (26%) and 18% (11%), respectively, compared to IRI-2020. During geomagnetic disturbances, all three models were able to reproduce the variations in both foF2 and MUF(3000)F2 parameters. Nevertheless, the RF model showed significantly better performance in foF2 estimation compared to the SVM and BPNN models.https://www.mdpi.com/2072-4292/17/10/1764machine learningSVMRFBPNNTECfoF2 |
| spellingShingle | Yuhang Zhang Ming Ou Liang Chen Yi Hao Qinglin Zhu Xiang Dong Weimin Zhen Machine Learning-Based Estimation of foF2 and MUF(3000)F2 Using GNSS Ionospheric TEC Observations Remote Sensing machine learning SVM RF BPNN TEC foF2 |
| title | Machine Learning-Based Estimation of foF2 and MUF(3000)F2 Using GNSS Ionospheric TEC Observations |
| title_full | Machine Learning-Based Estimation of foF2 and MUF(3000)F2 Using GNSS Ionospheric TEC Observations |
| title_fullStr | Machine Learning-Based Estimation of foF2 and MUF(3000)F2 Using GNSS Ionospheric TEC Observations |
| title_full_unstemmed | Machine Learning-Based Estimation of foF2 and MUF(3000)F2 Using GNSS Ionospheric TEC Observations |
| title_short | Machine Learning-Based Estimation of foF2 and MUF(3000)F2 Using GNSS Ionospheric TEC Observations |
| title_sort | machine learning based estimation of fof2 and muf 3000 f2 using gnss ionospheric tec observations |
| topic | machine learning SVM RF BPNN TEC foF2 |
| url | https://www.mdpi.com/2072-4292/17/10/1764 |
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