A Machine Learning Approach to Predicting SEP Events Using Properties of Coronal Mass Ejections
Abstract Solar energetic particles (SEPs) can cause severe damage to astronauts and their equipment, and can disrupt communications on Earth. A lack of thorough understanding the eruption processes of solar activities and the subsequent acceleration and transport processes of energetic particles mak...
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| Format: | Article |
| Language: | English |
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Wiley
2022-07-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2021SW002797 |
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| _version_ | 1841536522113777664 |
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| author | Jesse Torres Lulu Zhao Philip K. Chan Ming Zhang |
| author_facet | Jesse Torres Lulu Zhao Philip K. Chan Ming Zhang |
| author_sort | Jesse Torres |
| collection | DOAJ |
| description | Abstract Solar energetic particles (SEPs) can cause severe damage to astronauts and their equipment, and can disrupt communications on Earth. A lack of thorough understanding the eruption processes of solar activities and the subsequent acceleration and transport processes of energetic particles makes it difficult to forecast the occurrence of an SEP event and its intensity using conventional modeling with physics‐based parameters. Therefore, in order to provide an advance warning for astronauts to seek shelter in a timely manner, we apply neural networks to forecast the occurrence of SEP events. We use the properties of coronal mass ejections (CMEs) archived in the Coordinated Data Analysis Workshops catalog based on SOHO Large Angle and Spectrometric Coronagraph Experiment observations. We also derive some features based on these properties associated with the CME, and analyze the contribution of each feature to the overall prediction. Our algorithm achieves an average True Skill Statistic of 0.906, an average F1 score of 0.246, an average probability of detection of 0.920, and an average false alarm rate of 0.882. An analysis of the features shows that sunspot number and a feature based on Type II radio bursts contribute the most, but when grouped together, CME speed‐related features are the most important features. |
| format | Article |
| id | doaj-art-dafd5c0a68f44ccd836ed319c3a83d9e |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2022-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-dafd5c0a68f44ccd836ed319c3a83d9e2025-01-14T16:26:58ZengWileySpace Weather1542-73902022-07-01207n/an/a10.1029/2021SW002797A Machine Learning Approach to Predicting SEP Events Using Properties of Coronal Mass EjectionsJesse Torres0Lulu Zhao1Philip K. Chan2Ming Zhang3Department of Computer Engineering and Sciences Florida Institute of Technology Melbourne FL USADepartment of Aerospace, Physics and Space Sciences Florida Institute of Technology Melbourne FL USADepartment of Computer Engineering and Sciences Florida Institute of Technology Melbourne FL USADepartment of Aerospace, Physics and Space Sciences Florida Institute of Technology Melbourne FL USAAbstract Solar energetic particles (SEPs) can cause severe damage to astronauts and their equipment, and can disrupt communications on Earth. A lack of thorough understanding the eruption processes of solar activities and the subsequent acceleration and transport processes of energetic particles makes it difficult to forecast the occurrence of an SEP event and its intensity using conventional modeling with physics‐based parameters. Therefore, in order to provide an advance warning for astronauts to seek shelter in a timely manner, we apply neural networks to forecast the occurrence of SEP events. We use the properties of coronal mass ejections (CMEs) archived in the Coordinated Data Analysis Workshops catalog based on SOHO Large Angle and Spectrometric Coronagraph Experiment observations. We also derive some features based on these properties associated with the CME, and analyze the contribution of each feature to the overall prediction. Our algorithm achieves an average True Skill Statistic of 0.906, an average F1 score of 0.246, an average probability of detection of 0.920, and an average false alarm rate of 0.882. An analysis of the features shows that sunspot number and a feature based on Type II radio bursts contribute the most, but when grouped together, CME speed‐related features are the most important features.https://doi.org/10.1029/2021SW002797coronal mass ejectionsforecasting solar energetic particle eventneural networks |
| spellingShingle | Jesse Torres Lulu Zhao Philip K. Chan Ming Zhang A Machine Learning Approach to Predicting SEP Events Using Properties of Coronal Mass Ejections Space Weather coronal mass ejections forecasting solar energetic particle event neural networks |
| title | A Machine Learning Approach to Predicting SEP Events Using Properties of Coronal Mass Ejections |
| title_full | A Machine Learning Approach to Predicting SEP Events Using Properties of Coronal Mass Ejections |
| title_fullStr | A Machine Learning Approach to Predicting SEP Events Using Properties of Coronal Mass Ejections |
| title_full_unstemmed | A Machine Learning Approach to Predicting SEP Events Using Properties of Coronal Mass Ejections |
| title_short | A Machine Learning Approach to Predicting SEP Events Using Properties of Coronal Mass Ejections |
| title_sort | machine learning approach to predicting sep events using properties of coronal mass ejections |
| topic | coronal mass ejections forecasting solar energetic particle event neural networks |
| url | https://doi.org/10.1029/2021SW002797 |
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