Forecasting auroral substorms from observed data with a supervised learning algorithm

Takanori Tanaka, Daisuke Kitao, Yuka Sato, Yoshimasa Tanaka, Daisuke Ikeda

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

1 被引用数 (Scopus)

抄録

Auroras are beautiful phenomena and attract many people. However, its physical model still remains a subject of dispute because it is caused by the interaction of diverse areas, such as solar wind, magnetosphere, and ionosphere, and it is difficult to simultaneously obtain data in such wide areas. This paper is devoted to forecasting the onset of brightening of auroras followed by poleward expansion, called auroral substorms. We adopt a data-driven approach, instead of physical models of auroras. This approach requires labeled data, which shows when auroras appeared. However, this is challenging because there exist a wide variety of observed data from diverse areas while they are not tied with onset time of auroras. We identified auroral substorms using all-sky images obtained at Tromso, Norway. Then, we chose solar wind and geomagnetic field data as the first attempt toward the goal, out of many types of data, and associated them with the onset times of the identified auroral substorms. We trained a classifier of the support vector machine, which is a typical supervised learning algorithm, using the constructed data, and the classifier achieves around 78% classification accuracy at 5-fold cross validation.

本文言語英語
ホスト出版物のタイトルProceedings - 11th IEEE International Conference on eScience, eScience 2015
出版社Institute of Electrical and Electronics Engineers Inc.
ページ284-287
ページ数4
ISBN(電子版)9781467393256
DOI
出版ステータス出版済み - 10 22 2015
イベント11th IEEE International Conference on eScience, eScience 2015 - Munich, ドイツ
継続期間: 8 31 20159 4 2015

出版物シリーズ

名前Proceedings - 11th IEEE International Conference on eScience, eScience 2015

その他

その他11th IEEE International Conference on eScience, eScience 2015
Countryドイツ
CityMunich
Period8/31/159/4/15

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Management Science and Operations Research

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