Forecasting auroral substorms from observed data with a supervised learning algorithm

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 11th IEEE International Conference on eScience, eScience 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages284-287
Number of pages4
ISBN (Electronic)9781467393256
DOIs
Publication statusPublished - Oct 22 2015
Event11th IEEE International Conference on eScience, eScience 2015 - Munich, Germany
Duration: Aug 31 2015Sep 4 2015

Publication series

NameProceedings - 11th IEEE International Conference on eScience, eScience 2015

Other

Other11th IEEE International Conference on eScience, eScience 2015
CountryGermany
CityMunich
Period8/31/159/4/15

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All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Management Science and Operations Research

Cite this

Tanaka, T., Kitao, D., Sato, Y., Tanaka, Y., & Ikeda, D. (2015). Forecasting auroral substorms from observed data with a supervised learning algorithm. In Proceedings - 11th IEEE International Conference on eScience, eScience 2015 (pp. 284-287). [7304307] (Proceedings - 11th IEEE International Conference on eScience, eScience 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/eScience.2015.48