An Interpretable Machine Learning Procedure Which Unravels Hidden Interplanetary Drivers of the Low Latitude Dayside Magnetopause
Abstract In this study, we propose an interpretable machine learning procedure to unravel the importance of multiple interplanetary parameters to the Earth's magnetopause standoff distance (MSD). We construct the interpretable procedure based on SHapley Additive exPlanations. A magnetopause cro...
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| Language: | English |
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Wiley
2023-03-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2022SW003391 |
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| author | Sheng Li Yang‐Yi Sun Chieh‐Hung Chen |
| author_facet | Sheng Li Yang‐Yi Sun Chieh‐Hung Chen |
| author_sort | Sheng Li |
| collection | DOAJ |
| description | Abstract In this study, we propose an interpretable machine learning procedure to unravel the importance of multiple interplanetary parameters to the Earth's magnetopause standoff distance (MSD). We construct the interpretable procedure based on SHapley Additive exPlanations. A magnetopause crossings database from the Time History of Events and Macroscale Interactions during Substorms satellites and the multiple interplanetary parameters from OMNI during the period of 2007–2016 are utilized. The solar wind dynamic pressure and the interplanetary magnetic field (IMF) BZ are widely suggested as the important two interplanetary parameters that drive the MSD. However, the examination of the interpretable procedure suggests that the magnitude of the IMF is the second significant parameter after the dynamic pressure. Although the magnetic pressure, which is the function of the IMF magnitude was considered in previous studies, the importance of the IMF magnitude was underestimated. The interpretable procedure also reveals that the IMF magnitude and the BZ have different effects on the MSD. Their joint effect is the formation of the MSD sag near BZ = 5 nT. This is for the first time the interpretable concept is being applied to construct a machine‐learning magnetopause model. |
| format | Article |
| id | doaj-art-9bd4e948b00f4d808b3b8a0efa54a3a9 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2023-03-01 |
| publisher | Wiley |
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| series | Space Weather |
| spelling | doaj-art-9bd4e948b00f4d808b3b8a0efa54a3a92025-01-14T16:27:17ZengWileySpace Weather1542-73902023-03-01213n/an/a10.1029/2022SW003391An Interpretable Machine Learning Procedure Which Unravels Hidden Interplanetary Drivers of the Low Latitude Dayside MagnetopauseSheng Li0Yang‐Yi Sun1Chieh‐Hung Chen2School of Geophysics and Geomatics China University of Geosciences (Wuhan) Wuhan ChinaSchool of Geophysics and Geomatics China University of Geosciences (Wuhan) Wuhan ChinaSchool of Geophysics and Geomatics China University of Geosciences (Wuhan) Wuhan ChinaAbstract In this study, we propose an interpretable machine learning procedure to unravel the importance of multiple interplanetary parameters to the Earth's magnetopause standoff distance (MSD). We construct the interpretable procedure based on SHapley Additive exPlanations. A magnetopause crossings database from the Time History of Events and Macroscale Interactions during Substorms satellites and the multiple interplanetary parameters from OMNI during the period of 2007–2016 are utilized. The solar wind dynamic pressure and the interplanetary magnetic field (IMF) BZ are widely suggested as the important two interplanetary parameters that drive the MSD. However, the examination of the interpretable procedure suggests that the magnitude of the IMF is the second significant parameter after the dynamic pressure. Although the magnetic pressure, which is the function of the IMF magnitude was considered in previous studies, the importance of the IMF magnitude was underestimated. The interpretable procedure also reveals that the IMF magnitude and the BZ have different effects on the MSD. Their joint effect is the formation of the MSD sag near BZ = 5 nT. This is for the first time the interpretable concept is being applied to construct a machine‐learning magnetopause model.https://doi.org/10.1029/2022SW003391magnetopauseinterpretable machine learningIMF magnitudeSHAP value |
| spellingShingle | Sheng Li Yang‐Yi Sun Chieh‐Hung Chen An Interpretable Machine Learning Procedure Which Unravels Hidden Interplanetary Drivers of the Low Latitude Dayside Magnetopause Space Weather magnetopause interpretable machine learning IMF magnitude SHAP value |
| title | An Interpretable Machine Learning Procedure Which Unravels Hidden Interplanetary Drivers of the Low Latitude Dayside Magnetopause |
| title_full | An Interpretable Machine Learning Procedure Which Unravels Hidden Interplanetary Drivers of the Low Latitude Dayside Magnetopause |
| title_fullStr | An Interpretable Machine Learning Procedure Which Unravels Hidden Interplanetary Drivers of the Low Latitude Dayside Magnetopause |
| title_full_unstemmed | An Interpretable Machine Learning Procedure Which Unravels Hidden Interplanetary Drivers of the Low Latitude Dayside Magnetopause |
| title_short | An Interpretable Machine Learning Procedure Which Unravels Hidden Interplanetary Drivers of the Low Latitude Dayside Magnetopause |
| title_sort | interpretable machine learning procedure which unravels hidden interplanetary drivers of the low latitude dayside magnetopause |
| topic | magnetopause interpretable machine learning IMF magnitude SHAP value |
| url | https://doi.org/10.1029/2022SW003391 |
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