A Model-Assisted Combined Machine Learning Method for Ionospheric TEC Prediction
In order to improve the prediction accuracy of ionospheric total electron content (TEC), a combined intelligent prediction model (MMAdapGA-BP-NN) based on a multi-mutation, multi-cross adaptive genetic algorithm (MMAdapGA) and a back propagation neural network (BP-NN) was proposed. The model combine...
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| Format: | Article |
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MDPI AG
2023-06-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/12/2953 |
| _version_ | 1797592859427209216 |
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| author | Jiaxuan Weng Yiran Liu Jian Wang |
| author_facet | Jiaxuan Weng Yiran Liu Jian Wang |
| author_sort | Jiaxuan Weng |
| collection | DOAJ |
| description | In order to improve the prediction accuracy of ionospheric total electron content (TEC), a combined intelligent prediction model (MMAdapGA-BP-NN) based on a multi-mutation, multi-cross adaptive genetic algorithm (MMAdapGA) and a back propagation neural network (BP-NN) was proposed. The model combines the international reference ionosphere (IRI), statistical machine learning (SML), BP-NN, and MMAdapGA. Compared with the IRI, SML-based, and other neural network models, MMAdapGA-BP-NN has higher accuracy and a more stable prediction effect. Taking the Athens station in Greece as an example, the root mean square errors (RMSEs) of MMAdapGA-BP-NN in 2015 and 2020 are 2.84TECU and 0.85TECU, respectively, 52.27% and 72.13% lower than the IRI model. Compared with the single neural network model, the MMAdapGA-BP-NN model reduced RMSE by 28.82% and 24.11% in 2015 and 2020, respectively. Furthermore, compared with the neural network optimized by a single mutation genetic algorithm, MMAdapGA-BP-NN has fewer iterations ranging from 10 to 30. The results show that the prediction effect and stability of the proposed model have obvious advantages. As a result, the model could be extended to an alternative prediction scheme for more ionospheric parameters. |
| first_indexed | 2024-03-11T01:59:06Z |
| format | Article |
| id | doaj.art-9c2af9f50916491fbf814316a2c6a70f |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T01:59:06Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-9c2af9f50916491fbf814316a2c6a70f2023-11-18T12:24:15ZengMDPI AGRemote Sensing2072-42922023-06-011512295310.3390/rs15122953A Model-Assisted Combined Machine Learning Method for Ionospheric TEC PredictionJiaxuan Weng0Yiran Liu1Jian Wang2School of Microelectronics, Tianjin University, Tianjin 300072, ChinaSchool of Microelectronics, Tianjin University, Tianjin 300072, ChinaSchool of Microelectronics, Tianjin University, Tianjin 300072, ChinaIn order to improve the prediction accuracy of ionospheric total electron content (TEC), a combined intelligent prediction model (MMAdapGA-BP-NN) based on a multi-mutation, multi-cross adaptive genetic algorithm (MMAdapGA) and a back propagation neural network (BP-NN) was proposed. The model combines the international reference ionosphere (IRI), statistical machine learning (SML), BP-NN, and MMAdapGA. Compared with the IRI, SML-based, and other neural network models, MMAdapGA-BP-NN has higher accuracy and a more stable prediction effect. Taking the Athens station in Greece as an example, the root mean square errors (RMSEs) of MMAdapGA-BP-NN in 2015 and 2020 are 2.84TECU and 0.85TECU, respectively, 52.27% and 72.13% lower than the IRI model. Compared with the single neural network model, the MMAdapGA-BP-NN model reduced RMSE by 28.82% and 24.11% in 2015 and 2020, respectively. Furthermore, compared with the neural network optimized by a single mutation genetic algorithm, MMAdapGA-BP-NN has fewer iterations ranging from 10 to 30. The results show that the prediction effect and stability of the proposed model have obvious advantages. As a result, the model could be extended to an alternative prediction scheme for more ionospheric parameters.https://www.mdpi.com/2072-4292/15/12/2953TECpredictionmachine learningneural networkgenetic algorithm |
| spellingShingle | Jiaxuan Weng Yiran Liu Jian Wang A Model-Assisted Combined Machine Learning Method for Ionospheric TEC Prediction Remote Sensing TEC prediction machine learning neural network genetic algorithm |
| title | A Model-Assisted Combined Machine Learning Method for Ionospheric TEC Prediction |
| title_full | A Model-Assisted Combined Machine Learning Method for Ionospheric TEC Prediction |
| title_fullStr | A Model-Assisted Combined Machine Learning Method for Ionospheric TEC Prediction |
| title_full_unstemmed | A Model-Assisted Combined Machine Learning Method for Ionospheric TEC Prediction |
| title_short | A Model-Assisted Combined Machine Learning Method for Ionospheric TEC Prediction |
| title_sort | model assisted combined machine learning method for ionospheric tec prediction |
| topic | TEC prediction machine learning neural network genetic algorithm |
| url | https://www.mdpi.com/2072-4292/15/12/2953 |
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