利用基于神经网络的模型研究电离层电流对外部驱动的响应

IF 3.8 2区 地球科学 Q2 ASTRONOMY & ASTROPHYSICS
Xin Cao, Xiangning Chu, Jacob Bortnik, James M. Weygand, Jinxing Li, Homayon Aryan, Donglai Ma
{"title":"利用基于神经网络的模型研究电离层电流对外部驱动的响应","authors":"Xin Cao, Xiangning Chu, Jacob Bortnik, James M. Weygand, Jinxing Li, Homayon Aryan, Donglai Ma","doi":"10.1029/2023sw003506","DOIUrl":null,"url":null,"abstract":"Abstract A predictive model for the variation of ionospheric currents is of great scientific and practical importance to our modern industrial society. To study the response of ionospheric currents to external drivers including geomagnetic indices and solar radiation, we developed a feedforward neural network model trained on the Equivalent Ionospheric Current (EIC) data from 1st January 2007 to 31st December 2019. Due to the highly imbalanced nature of the ionospheric currents data, which means that the data of extreme events are much less than those of quiet times, we utilized different loss functions to improve the model performance. Our model demonstrates the potential to predict the active events of ionospheric currents reasonably well (e.g., EICs during substorms) within a timescale of a few minutes. Although the data used for training are measurements over the North American and Greenland sectors, our model is not only able to predict EICs within this region, but is also able to provide a promising out‐of‐sample prediction on a global scale.","PeriodicalId":49487,"journal":{"name":"Space Weather-The International Journal of Research and Applications","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The Response of Ionospheric Currents to External Drivers Investigated Using a Neural Network‐Based Model\",\"authors\":\"Xin Cao, Xiangning Chu, Jacob Bortnik, James M. Weygand, Jinxing Li, Homayon Aryan, Donglai Ma\",\"doi\":\"10.1029/2023sw003506\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract A predictive model for the variation of ionospheric currents is of great scientific and practical importance to our modern industrial society. To study the response of ionospheric currents to external drivers including geomagnetic indices and solar radiation, we developed a feedforward neural network model trained on the Equivalent Ionospheric Current (EIC) data from 1st January 2007 to 31st December 2019. Due to the highly imbalanced nature of the ionospheric currents data, which means that the data of extreme events are much less than those of quiet times, we utilized different loss functions to improve the model performance. Our model demonstrates the potential to predict the active events of ionospheric currents reasonably well (e.g., EICs during substorms) within a timescale of a few minutes. Although the data used for training are measurements over the North American and Greenland sectors, our model is not only able to predict EICs within this region, but is also able to provide a promising out‐of‐sample prediction on a global scale.\",\"PeriodicalId\":49487,\"journal\":{\"name\":\"Space Weather-The International Journal of Research and Applications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2023-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Space Weather-The International Journal of Research and Applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1029/2023sw003506\",\"RegionNum\":2,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ASTRONOMY & ASTROPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Space Weather-The International Journal of Research and Applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1029/2023sw003506","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ASTRONOMY & ASTROPHYSICS","Score":null,"Total":0}
引用次数: 0

摘要

摘要建立电离层电流变化的预测模型对现代工业社会具有重要的科学意义和现实意义。为了研究电离层电流对地磁指数和太阳辐射等外部驱动因素的响应,基于2007年1月1日至2019年12月31日的等效电离层电流(EIC)数据,建立了一个前馈神经网络模型。由于电离层电流数据具有高度的不平衡性,即极端事件的数据远少于平静时间的数据,我们使用不同的损失函数来提高模型的性能。我们的模型展示了在几分钟的时间尺度内相当好地预测电离层电流活动事件(例如,亚暴期间的EICs)的潜力。虽然用于训练的数据是北美和格陵兰地区的测量数据,但我们的模型不仅能够预测该地区的eic,而且还能够在全球范围内提供有希望的样本外预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Response of Ionospheric Currents to External Drivers Investigated Using a Neural Network‐Based Model
Abstract A predictive model for the variation of ionospheric currents is of great scientific and practical importance to our modern industrial society. To study the response of ionospheric currents to external drivers including geomagnetic indices and solar radiation, we developed a feedforward neural network model trained on the Equivalent Ionospheric Current (EIC) data from 1st January 2007 to 31st December 2019. Due to the highly imbalanced nature of the ionospheric currents data, which means that the data of extreme events are much less than those of quiet times, we utilized different loss functions to improve the model performance. Our model demonstrates the potential to predict the active events of ionospheric currents reasonably well (e.g., EICs during substorms) within a timescale of a few minutes. Although the data used for training are measurements over the North American and Greenland sectors, our model is not only able to predict EICs within this region, but is also able to provide a promising out‐of‐sample prediction on a global scale.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
5.90
自引率
29.70%
发文量
166
审稿时长
>12 weeks
期刊介绍: Space Weather: The International Journal of Research and Applications (SWE) is devoted to understanding and forecasting space weather. The scope of understanding and forecasting includes: origins, propagation and interactions of solar-produced processes within geospace; interactions in Earth’s space-atmosphere interface region produced by disturbances from above and below; influences of cosmic rays on humans, hardware, and signals; and comparisons of these types of interactions and influences with the atmospheres of neighboring planets and Earth’s moon. Manuscripts should emphasize impacts on technical systems including telecommunications, transportation, electric power, satellite navigation, avionics/spacecraft design and operations, human spaceflight, and other systems. Manuscripts that describe models or space environment climatology should clearly state how the results can be applied.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信