Investigation of the relationship between geomagnetic activity and solar wind parameters based on a novel neural network (potential learning)
Abstract Predicting geomagnetic conditions based on in-situ solar wind observations allows us to evade disasters caused by large electromagnetic disturbances originating from the Sun to save lives and protect economic activity. In this study, we aimed to examine the relationship between the K p inde...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2022-09-01
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| Series: | Earth, Planets and Space |
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| Online Access: | https://doi.org/10.1186/s40623-022-01697-0 |
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| author | Ryozo Kitajima Motoharu Nowada Ryotaro Kamimura |
| author_facet | Ryozo Kitajima Motoharu Nowada Ryotaro Kamimura |
| author_sort | Ryozo Kitajima |
| collection | DOAJ |
| description | Abstract Predicting geomagnetic conditions based on in-situ solar wind observations allows us to evade disasters caused by large electromagnetic disturbances originating from the Sun to save lives and protect economic activity. In this study, we aimed to examine the relationship between the K p index, representing global magnetospheric activity level, and solar wind conditions using an interpretable neural network known as potential learning (PL). Data analyses based on neural networks are often difficult to interpret; however, PL learns by focusing on the “potentiality of input neurons” and can identify which inputs are significantly utilized by the network. Using the full advantage of PL, we extracted the influential solar wind parameters that disturb the magnetosphere under southward Interplanetary magnetic field (IMF) conditions. The input parameters of PL were the three components of the IMF (Bx, By, Bz), solar wind flow speed (Vx), and proton number density (Np) in geocentric solar magnetospheric (GSM) coordinates obtained from the OMNI solar wind database between 1998 and 2019. Furthermore, we classified these input parameters into two groups (targets), depending on the K p level: K p = 6–9 (positive target) and K p = 0 to 1 + (negative target). Negative target samples were randomly selected to ensure that numbers of positive and negative targets were equal. The PL results revealed that solar wind flow speed is an influential parameter for increasing K p under southward IMF conditions, which was in good agreement with previous reports on the statistical relationship between the K p index and solar wind velocity, and the K p formulation based on the IMF and solar wind plasma parameters. Based on this new neural network, we aim to construct a more correct and parameter-dependent space weather forecasting model. Graphical Abstract |
| first_indexed | 2024-04-12T16:53:08Z |
| format | Article |
| id | doaj.art-ccc7a565754340388c0147a50b5cc911 |
| institution | Directory Open Access Journal |
| issn | 1880-5981 |
| language | English |
| last_indexed | 2024-04-12T16:53:08Z |
| publishDate | 2022-09-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Earth, Planets and Space |
| spelling | doaj.art-ccc7a565754340388c0147a50b5cc9112022-12-22T03:24:20ZengSpringerOpenEarth, Planets and Space1880-59812022-09-017411910.1186/s40623-022-01697-0Investigation of the relationship between geomagnetic activity and solar wind parameters based on a novel neural network (potential learning)Ryozo Kitajima0Motoharu Nowada1Ryotaro Kamimura2Department of Engineering, Tokyo Polytechnic UniversityShandong Provincial Key Laboratory of Optical Astronomy and Solar-Terrestrial Environment, Institute of Space Sciences, Shandong UniversityIT Education Center, Tokai UniversityAbstract Predicting geomagnetic conditions based on in-situ solar wind observations allows us to evade disasters caused by large electromagnetic disturbances originating from the Sun to save lives and protect economic activity. In this study, we aimed to examine the relationship between the K p index, representing global magnetospheric activity level, and solar wind conditions using an interpretable neural network known as potential learning (PL). Data analyses based on neural networks are often difficult to interpret; however, PL learns by focusing on the “potentiality of input neurons” and can identify which inputs are significantly utilized by the network. Using the full advantage of PL, we extracted the influential solar wind parameters that disturb the magnetosphere under southward Interplanetary magnetic field (IMF) conditions. The input parameters of PL were the three components of the IMF (Bx, By, Bz), solar wind flow speed (Vx), and proton number density (Np) in geocentric solar magnetospheric (GSM) coordinates obtained from the OMNI solar wind database between 1998 and 2019. Furthermore, we classified these input parameters into two groups (targets), depending on the K p level: K p = 6–9 (positive target) and K p = 0 to 1 + (negative target). Negative target samples were randomly selected to ensure that numbers of positive and negative targets were equal. The PL results revealed that solar wind flow speed is an influential parameter for increasing K p under southward IMF conditions, which was in good agreement with previous reports on the statistical relationship between the K p index and solar wind velocity, and the K p formulation based on the IMF and solar wind plasma parameters. Based on this new neural network, we aim to construct a more correct and parameter-dependent space weather forecasting model. Graphical Abstracthttps://doi.org/10.1186/s40623-022-01697-0Space weather modelingSolar wind conditionsGeomagnetic activityNeural networkData classification |
| spellingShingle | Ryozo Kitajima Motoharu Nowada Ryotaro Kamimura Investigation of the relationship between geomagnetic activity and solar wind parameters based on a novel neural network (potential learning) Earth, Planets and Space Space weather modeling Solar wind conditions Geomagnetic activity Neural network Data classification |
| title | Investigation of the relationship between geomagnetic activity and solar wind parameters based on a novel neural network (potential learning) |
| title_full | Investigation of the relationship between geomagnetic activity and solar wind parameters based on a novel neural network (potential learning) |
| title_fullStr | Investigation of the relationship between geomagnetic activity and solar wind parameters based on a novel neural network (potential learning) |
| title_full_unstemmed | Investigation of the relationship between geomagnetic activity and solar wind parameters based on a novel neural network (potential learning) |
| title_short | Investigation of the relationship between geomagnetic activity and solar wind parameters based on a novel neural network (potential learning) |
| title_sort | investigation of the relationship between geomagnetic activity and solar wind parameters based on a novel neural network potential learning |
| topic | Space weather modeling Solar wind conditions Geomagnetic activity Neural network Data classification |
| url | https://doi.org/10.1186/s40623-022-01697-0 |
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