A Hybrid Deep Learning‐Based Forecasting Model for the Peak Height of Ionospheric F2 Layer
Abstract To achieve accurate forecasting of the peak height of the ionospheric F2 layer (hmF2), we propose a hybrid deep learning model of improved seagull optimization algorithm (ISOA) optimized long short‐term memory (LSTM) model based on a complete ensemble empirical mode decomposition with adapt...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-10-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2023SW003581 |
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| author | Ya‐fei Shi Cheng Yang Jian Wang Yu Zheng Fan‐yi Meng Leonid F. Chernogor |
| author_facet | Ya‐fei Shi Cheng Yang Jian Wang Yu Zheng Fan‐yi Meng Leonid F. Chernogor |
| author_sort | Ya‐fei Shi |
| collection | DOAJ |
| description | Abstract To achieve accurate forecasting of the peak height of the ionospheric F2 layer (hmF2), we propose a hybrid deep learning model of improved seagull optimization algorithm (ISOA) optimized long short‐term memory (LSTM) model based on a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) theory. The hybrid model decomposes the hmF2 time data into multiple subsequences through CEEMDAN and reconstructs the subsequences by sample entropy and correlation coefficient into high and low‐frequency sequences, which effectively shortens the calculation time of the model. Then, we determine the optimal hyperparameters of the LSTM models through ISOA, achieving high‐precision forecasting of the hmF2. In single‐step forecasting, the forecasting values of the hybrid model in diurnal and seasonal changes are highly consistent with the observation, which can better capture the severe changes in the hmF2. The model's RMSE, MAE, MAPE, and CC evaluation metrics are 15.86, 11.03 km, 4.76%, and 0.93 in the test set. Compared to IRI, GRU, and LSTM models, taking RMSE as an example, the forecasting accuracy of the models increased by 65.24%, 29.89%, and 29.60%, respectively. In multi‐step forecasting, the proposed model is better at forecasting the changing trend of hmF2, and the forecasting accuracies are significantly better than the IRI model. The data from multiple stations also verified the applicability of the proposed model for hmF2 forecasting. The above results indicate that the hybrid model has high accuracy in hmF2 short‐term forecasting and good applicability in multiple multi‐step forecasting, which can further improve the accurate forecasting of space weather. |
| format | Article |
| id | doaj-art-d8dee87643454922b1dc57fa8080b4c6 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2023-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-d8dee87643454922b1dc57fa8080b4c62025-01-14T16:31:16ZengWileySpace Weather1542-73902023-10-012110n/an/a10.1029/2023SW003581A Hybrid Deep Learning‐Based Forecasting Model for the Peak Height of Ionospheric F2 LayerYa‐fei Shi0Cheng Yang1Jian Wang2Yu Zheng3Fan‐yi Meng4Leonid F. Chernogor5School of Microelectronics Tianjin University Tianjin ChinaSchool of Microelectronics Tianjin University Tianjin ChinaSchool of Microelectronics Tianjin University Tianjin ChinaCollege of Electronic Information Qingdao University Qingdao ChinaSchool of Microelectronics Tianjin University Tianjin ChinaCollege of Electronic Information Qingdao University Qingdao ChinaAbstract To achieve accurate forecasting of the peak height of the ionospheric F2 layer (hmF2), we propose a hybrid deep learning model of improved seagull optimization algorithm (ISOA) optimized long short‐term memory (LSTM) model based on a complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) theory. The hybrid model decomposes the hmF2 time data into multiple subsequences through CEEMDAN and reconstructs the subsequences by sample entropy and correlation coefficient into high and low‐frequency sequences, which effectively shortens the calculation time of the model. Then, we determine the optimal hyperparameters of the LSTM models through ISOA, achieving high‐precision forecasting of the hmF2. In single‐step forecasting, the forecasting values of the hybrid model in diurnal and seasonal changes are highly consistent with the observation, which can better capture the severe changes in the hmF2. The model's RMSE, MAE, MAPE, and CC evaluation metrics are 15.86, 11.03 km, 4.76%, and 0.93 in the test set. Compared to IRI, GRU, and LSTM models, taking RMSE as an example, the forecasting accuracy of the models increased by 65.24%, 29.89%, and 29.60%, respectively. In multi‐step forecasting, the proposed model is better at forecasting the changing trend of hmF2, and the forecasting accuracies are significantly better than the IRI model. The data from multiple stations also verified the applicability of the proposed model for hmF2 forecasting. The above results indicate that the hybrid model has high accuracy in hmF2 short‐term forecasting and good applicability in multiple multi‐step forecasting, which can further improve the accurate forecasting of space weather.https://doi.org/10.1029/2023SW003581ionospherepeak heightforecastingdeep learning |
| spellingShingle | Ya‐fei Shi Cheng Yang Jian Wang Yu Zheng Fan‐yi Meng Leonid F. Chernogor A Hybrid Deep Learning‐Based Forecasting Model for the Peak Height of Ionospheric F2 Layer Space Weather ionosphere peak height forecasting deep learning |
| title | A Hybrid Deep Learning‐Based Forecasting Model for the Peak Height of Ionospheric F2 Layer |
| title_full | A Hybrid Deep Learning‐Based Forecasting Model for the Peak Height of Ionospheric F2 Layer |
| title_fullStr | A Hybrid Deep Learning‐Based Forecasting Model for the Peak Height of Ionospheric F2 Layer |
| title_full_unstemmed | A Hybrid Deep Learning‐Based Forecasting Model for the Peak Height of Ionospheric F2 Layer |
| title_short | A Hybrid Deep Learning‐Based Forecasting Model for the Peak Height of Ionospheric F2 Layer |
| title_sort | hybrid deep learning based forecasting model for the peak height of ionospheric f2 layer |
| topic | ionosphere peak height forecasting deep learning |
| url | https://doi.org/10.1029/2023SW003581 |
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