Ionospheric TEC Prediction Based on Ensemble Learning Models

IF 3.7 2区 地球科学
Space Weather Pub Date : 2024-03-20 DOI:10.1029/2023sw003790
Yang Zhou, Jing Liu, Shuhan Li, Qiaoling Li
{"title":"Ionospheric TEC Prediction Based on Ensemble Learning Models","authors":"Yang Zhou, Jing Liu, Shuhan Li, Qiaoling Li","doi":"10.1029/2023sw003790","DOIUrl":null,"url":null,"abstract":"In this paper, we propose the usage of an ensemble learning approach for predicting total electron content (TEC). The training data set spans from 2007 to 2016, while the testing data set is set to the year 2017. The model inputs in our study included Solar radio flux (F107), Solar Wind plasma speed, By, Bz, Dst, Ap, AE, day of year, universal time, 30-day and 90-day TEC averages. Specifically, eXtreme Gradient Boosting (XGBoost), Gradient Boosting Decision Tree, and Decision Tree were utilized for 1-hr TEC prediction at high- (80°W, 80°N), mid- (80°W, 40°N), and low- latitudes (80°W, 10°N). Results indicate that all three models performed well in predicting TEC, with a mean error of only approximately 0.6 TECU at high- and mid- latitudes and 1.13 TECU at low latitudes. At the same time, we compared the model with 1-day Beijing University of Aeronautics and Astronautics model during the period of magnetic storm from 25 August 2018 to 27 August 2018 and a quiet period from 13 December 2018 to 15 December 2018. In the magnetic storm period, Our model showed an average reduction of 1.83 TECU compared to BUAA model. During the quiet period, XGBoost exhibit an average error that is 1.14 TECU lower than that of BUAA model. Moreover, TEC prediction over the region between the 20°N–45°N and 70°E−120°E during geomagnetic storm has an error of 2.74 TECU, showing the stability and superiority of XGBoost. Overall, the ensemble learning approach exhibits its advantage in predicting TEC.","PeriodicalId":22181,"journal":{"name":"Space Weather","volume":null,"pages":null},"PeriodicalIF":3.7000,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Space Weather","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1029/2023sw003790","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

In this paper, we propose the usage of an ensemble learning approach for predicting total electron content (TEC). The training data set spans from 2007 to 2016, while the testing data set is set to the year 2017. The model inputs in our study included Solar radio flux (F107), Solar Wind plasma speed, By, Bz, Dst, Ap, AE, day of year, universal time, 30-day and 90-day TEC averages. Specifically, eXtreme Gradient Boosting (XGBoost), Gradient Boosting Decision Tree, and Decision Tree were utilized for 1-hr TEC prediction at high- (80°W, 80°N), mid- (80°W, 40°N), and low- latitudes (80°W, 10°N). Results indicate that all three models performed well in predicting TEC, with a mean error of only approximately 0.6 TECU at high- and mid- latitudes and 1.13 TECU at low latitudes. At the same time, we compared the model with 1-day Beijing University of Aeronautics and Astronautics model during the period of magnetic storm from 25 August 2018 to 27 August 2018 and a quiet period from 13 December 2018 to 15 December 2018. In the magnetic storm period, Our model showed an average reduction of 1.83 TECU compared to BUAA model. During the quiet period, XGBoost exhibit an average error that is 1.14 TECU lower than that of BUAA model. Moreover, TEC prediction over the region between the 20°N–45°N and 70°E−120°E during geomagnetic storm has an error of 2.74 TECU, showing the stability and superiority of XGBoost. Overall, the ensemble learning approach exhibits its advantage in predicting TEC.
基于集合学习模型的电离层 TEC 预测
在本文中,我们提出使用集合学习方法来预测电子总含量(TEC)。训练数据集的时间跨度为 2007 年至 2016 年,测试数据集的时间跨度为 2017 年。研究中的模型输入包括太阳射电通量(F107)、太阳风等离子体速度、By、Bz、Dst、Ap、AE、年月日、全球时间、30 天和 90 天 TEC 平均值。具体来说,在高纬度(西经 80°,北纬 80°)、中纬度(西经 80°,北纬 40°)和低纬度(西经 80°,北纬 10°)地区,利用极端梯度提升模型(XGBoost)、梯度提升决策树模型和决策树模型进行 1 小时 TEC 预测。结果表明,这三个模式在预测 TEC 方面都表现良好,在高纬度和中纬度的平均误差仅为 0.6 TECU 左右,在低纬度为 1.13 TECU 左右。同时,我们将 2018 年 8 月 25 日至 2018 年 8 月 27 日磁暴期间和 2018 年 12 月 13 日至 2018 年 12 月 15 日静止期间的模型与北京航空航天大学 1 天模型进行了比较。在磁暴期间,我们的模型比北京航空航天大学模型平均减少了 1.83 TECU。在宁静期,XGBoost 的平均误差比 BUAA 模型低 1.14 TECU。此外,在地磁暴期间,20°N-45°N 和 70°E-120°E 之间区域的 TEC 预测误差为 2.74 TECU,显示了 XGBoost 的稳定性和优越性。总体而言,集合学习方法在预测 TEC 方面表现出优势。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
29.70%
发文量
166
×
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