A Regional Ionospheric Storm Forecasting Method Using a Deep Learning Algorithm: LSTM
Abstract An ionospheric storm forecasting method was proposed using a deep learning algorithm, LSTM (long short‐term memory). We used the perturbation index to denote the level of an ionospheric storm, deduced from foF2 data, and helped to remove most of the local time and seasonal variations in the...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2023-03-01
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| Series: | Space Weather |
| Online Access: | https://doi.org/10.1029/2022SW003061 |
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| _version_ | 1841536484665982976 |
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| author | Panpan Ban Lixin Guo Zhenwei Zhao Shuji Sun Tong Xu Zhengwen Xu Fengjuan Sun |
| author_facet | Panpan Ban Lixin Guo Zhenwei Zhao Shuji Sun Tong Xu Zhengwen Xu Fengjuan Sun |
| author_sort | Panpan Ban |
| collection | DOAJ |
| description | Abstract An ionospheric storm forecasting method was proposed using a deep learning algorithm, LSTM (long short‐term memory). We used the perturbation index to denote the level of an ionospheric storm, deduced from foF2 data, and helped to remove most of the local time and seasonal variations in the ionosphere. In constructing the model, a number of correlated factors were used as inputs, including the properties of coronal mass ejections, solar flare bursts, interplanetary conditions, and geomagnetic and ionospheric states, and the output was whether an ionospheric storm occurred locally in the next 24 hr. Data sets from 2007 to 2014 were used to train the model, and those from 2015 to 2016 were used for validation. The results showed that the model behaved well in most events. The mean precision rate, recall rate, accuracy, and F1 score of the model were 71.7%, 59.7%, 92.7%, and 65.0% in northern China and 78.9%, 56.3%, 96.3% and 65.0% in southern China, respectively. The LSTM forecasting model performed better than other models such as persistence, multiple‐layer perceptron and support vector machine models. Case studies also showed good performance during geomagnetic storms of different strengths. We believe that this model can be beneficial for functional ionospheric storm operation. |
| format | Article |
| id | doaj-art-5c81042b33f0480fbcd1323cb79a7d87 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2023-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-5c81042b33f0480fbcd1323cb79a7d872025-01-14T16:27:17ZengWileySpace Weather1542-73902023-03-01213n/an/a10.1029/2022SW003061A Regional Ionospheric Storm Forecasting Method Using a Deep Learning Algorithm: LSTMPanpan Ban0Lixin Guo1Zhenwei Zhao2Shuji Sun3Tong Xu4Zhengwen Xu5Fengjuan Sun6Xidian University Xi'an ChinaChina Research Institute of Radiowave Propagation Qingdao ChinaChina Research Institute of Radiowave Propagation Qingdao ChinaChina Research Institute of Radiowave Propagation Qingdao ChinaChina Research Institute of Radiowave Propagation Qingdao ChinaChina Research Institute of Radiowave Propagation Qingdao ChinaChina Research Institute of Radiowave Propagation Qingdao ChinaAbstract An ionospheric storm forecasting method was proposed using a deep learning algorithm, LSTM (long short‐term memory). We used the perturbation index to denote the level of an ionospheric storm, deduced from foF2 data, and helped to remove most of the local time and seasonal variations in the ionosphere. In constructing the model, a number of correlated factors were used as inputs, including the properties of coronal mass ejections, solar flare bursts, interplanetary conditions, and geomagnetic and ionospheric states, and the output was whether an ionospheric storm occurred locally in the next 24 hr. Data sets from 2007 to 2014 were used to train the model, and those from 2015 to 2016 were used for validation. The results showed that the model behaved well in most events. The mean precision rate, recall rate, accuracy, and F1 score of the model were 71.7%, 59.7%, 92.7%, and 65.0% in northern China and 78.9%, 56.3%, 96.3% and 65.0% in southern China, respectively. The LSTM forecasting model performed better than other models such as persistence, multiple‐layer perceptron and support vector machine models. Case studies also showed good performance during geomagnetic storms of different strengths. We believe that this model can be beneficial for functional ionospheric storm operation.https://doi.org/10.1029/2022SW003061 |
| spellingShingle | Panpan Ban Lixin Guo Zhenwei Zhao Shuji Sun Tong Xu Zhengwen Xu Fengjuan Sun A Regional Ionospheric Storm Forecasting Method Using a Deep Learning Algorithm: LSTM Space Weather |
| title | A Regional Ionospheric Storm Forecasting Method Using a Deep Learning Algorithm: LSTM |
| title_full | A Regional Ionospheric Storm Forecasting Method Using a Deep Learning Algorithm: LSTM |
| title_fullStr | A Regional Ionospheric Storm Forecasting Method Using a Deep Learning Algorithm: LSTM |
| title_full_unstemmed | A Regional Ionospheric Storm Forecasting Method Using a Deep Learning Algorithm: LSTM |
| title_short | A Regional Ionospheric Storm Forecasting Method Using a Deep Learning Algorithm: LSTM |
| title_sort | regional ionospheric storm forecasting method using a deep learning algorithm lstm |
| url | https://doi.org/10.1029/2022SW003061 |
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