【摘 要】
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We present a new method for automatically forecasting the occurrence of solar flares based on photospheric magnetic measurements.The method is a cascading combi
【机 构】
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Computer Vision Laboratory,Space Weather Research Laboratory
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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 24hour 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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