Automated flare forecasting using a statistical learning technique

Research output: Contribution to journalArticlepeer-review

55 Scopus citations

Abstract

We present a new method for automatically forecasting the occurrence of solar flares based on photospheric magnetic measurements. The method is a cascading combination of an ordinal logistic regression model and a support vector machine classifier. The predictive variables are three photospheric magnetic parameters, i.e., the total unsigned magnetic flux, length of the strong-gradient magnetic polarity inversion line, and total magnetic energy dissipation. The output is true or false for the occurrence of a certain level of flares within 24 hours. Experimental results, from a sample of 230 active regions between 1996 and 2005, show the accuracies of a 24- hour flare forecast to be 0.86, 0.72, 0.65 and 0.84 respectively for the four different levels. Comparison shows an improvement in the accuracy of X-class flare forecasting.

Original languageEnglish (US)
Pages (from-to)785-796
Number of pages12
JournalResearch in Astronomy and Astrophysics
Volume10
Issue number8
DOIs
StatePublished - Aug 1 2010

All Science Journal Classification (ASJC) codes

  • Astronomy and Astrophysics
  • Space and Planetary Science

Keywords

  • Flares-Sun
  • Magnetic fields
  • Sun

Fingerprint

Dive into the research topics of 'Automated flare forecasting using a statistical learning technique'. Together they form a unique fingerprint.

Cite this