Automated flare forecasting using a statistical learning technique

Details Automated flare forecasting using a statistical learning technique Automated flare forecasting using a statistical learning technique

Aug 2010

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010raa....10..785y&link_type=abstract

Research in Astronomy and Astrophysics, Volume 10, Issue 8, pp. 785-796 (2010).

Astronomy and Astrophysics

Astrophysics

3

Scientific paper

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.

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