Prediction of Ionograms With/Without Spread‐F at Hainan by a Combined Spatio‐Temporal Neural Network
Abstract An intelligent high‐definition and short‐term prediction of ionograms with/without Spread‐F for the observation at Hainan (19.5°N, 109.1°E, magnetic 11°N) is presented in this paper, which comprises a spatio‐temporal ConvGRU network and a super‐resolution EDSR network. Our prediction is bas...
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| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2023SW003727 |
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| _version_ | 1841536511212781568 |
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| author | Pengdong Gao Jinhui Cai Zheng Wang Chu Qiu Guojun Wang Quan Qi Bo Wang Jiankui Shi Xiao Wang Kai Ding |
| author_facet | Pengdong Gao Jinhui Cai Zheng Wang Chu Qiu Guojun Wang Quan Qi Bo Wang Jiankui Shi Xiao Wang Kai Ding |
| author_sort | Pengdong Gao |
| collection | DOAJ |
| description | Abstract An intelligent high‐definition and short‐term prediction of ionograms with/without Spread‐F for the observation at Hainan (19.5°N, 109.1°E, magnetic 11°N) is presented in this paper, which comprises a spatio‐temporal ConvGRU network and a super‐resolution EDSR network. Our prediction is based on spatio‐temporal features in the ionogram graph only. There are 469,227 ionograms classified into 5 categories, that is, frequency/range/mix/strong range/no Spread F, over a solar cycle (14 years) labeled manually by the research group, and we process these ionograms into two data sets for training the two networks mentioned above. A series of comprehensive experiments have been designed and conducted to determine the optimal super‐parameters. Our method inputs 8 consecutive authentic ionograms (lasting 2 hr) and generates the next 2 figures (next 30 min). Remarkably, all predicted figures achieve a high accuracy rate of over 94% in predicting the occurrence of Spread‐F. |
| format | Article |
| id | doaj-art-b38295a2a54743c59a51c35447d20596 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-b38295a2a54743c59a51c35447d205962025-01-14T16:26:56ZengWileySpace Weather1542-73902024-01-01221n/an/a10.1029/2023SW003727Prediction of Ionograms With/Without Spread‐F at Hainan by a Combined Spatio‐Temporal Neural NetworkPengdong Gao0Jinhui Cai1Zheng Wang2Chu Qiu3Guojun Wang4Quan Qi5Bo Wang6Jiankui Shi7Xiao Wang8Kai Ding9Key Laboratory of Media Audio & Video (Communication University of China) Ministry of Education Beijing ChinaKey Laboratory of Media Audio & Video (Communication University of China) Ministry of Education Beijing ChinaState Key Laboratory of Space Weather National Space Science Center Chinese Academy of Sciences Beijing ChinaKey Laboratory of Media Audio & Video (Communication University of China) Ministry of Education Beijing ChinaState Key Laboratory of Space Weather National Space Science Center Chinese Academy of Sciences Beijing ChinaKey Laboratory of Media Audio & Video (Communication University of China) Ministry of Education Beijing ChinaKey Laboratory of Media Audio & Video (Communication University of China) Ministry of Education Beijing ChinaState Key Laboratory of Space Weather National Space Science Center Chinese Academy of Sciences Beijing ChinaState Key Laboratory of Space Weather National Space Science Center Chinese Academy of Sciences Beijing ChinaState Key Laboratory of Space Weather National Space Science Center Chinese Academy of Sciences Beijing ChinaAbstract An intelligent high‐definition and short‐term prediction of ionograms with/without Spread‐F for the observation at Hainan (19.5°N, 109.1°E, magnetic 11°N) is presented in this paper, which comprises a spatio‐temporal ConvGRU network and a super‐resolution EDSR network. Our prediction is based on spatio‐temporal features in the ionogram graph only. There are 469,227 ionograms classified into 5 categories, that is, frequency/range/mix/strong range/no Spread F, over a solar cycle (14 years) labeled manually by the research group, and we process these ionograms into two data sets for training the two networks mentioned above. A series of comprehensive experiments have been designed and conducted to determine the optimal super‐parameters. Our method inputs 8 consecutive authentic ionograms (lasting 2 hr) and generates the next 2 figures (next 30 min). Remarkably, all predicted figures achieve a high accuracy rate of over 94% in predicting the occurrence of Spread‐F.https://doi.org/10.1029/2023SW003727Spread‐Fpredictionionogram sequenceionogram generationspatio‐temporal neural network |
| spellingShingle | Pengdong Gao Jinhui Cai Zheng Wang Chu Qiu Guojun Wang Quan Qi Bo Wang Jiankui Shi Xiao Wang Kai Ding Prediction of Ionograms With/Without Spread‐F at Hainan by a Combined Spatio‐Temporal Neural Network Space Weather Spread‐F prediction ionogram sequence ionogram generation spatio‐temporal neural network |
| title | Prediction of Ionograms With/Without Spread‐F at Hainan by a Combined Spatio‐Temporal Neural Network |
| title_full | Prediction of Ionograms With/Without Spread‐F at Hainan by a Combined Spatio‐Temporal Neural Network |
| title_fullStr | Prediction of Ionograms With/Without Spread‐F at Hainan by a Combined Spatio‐Temporal Neural Network |
| title_full_unstemmed | Prediction of Ionograms With/Without Spread‐F at Hainan by a Combined Spatio‐Temporal Neural Network |
| title_short | Prediction of Ionograms With/Without Spread‐F at Hainan by a Combined Spatio‐Temporal Neural Network |
| title_sort | prediction of ionograms with without spread f at hainan by a combined spatio temporal neural network |
| topic | Spread‐F prediction ionogram sequence ionogram generation spatio‐temporal neural network |
| url | https://doi.org/10.1029/2023SW003727 |
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