Solar flare forecasting based on sequential sunspot data

Abstract

It is widely believed that the evolution of solar active regions leads to solar flares. However, information about the evolution of solar active regions is not employed in most existing solar flare forecasting models. In the current work, a short-term solar flare forecasting model is proposed, in which sequential sunspot data, including three days of information about evolution from active regions, are taken as one of the basic predictors. The sunspot area, the McIntosh classification, the magnetic classification and the radio flux are extracted and converted to a numerical format that is suitable for the current forecasting model. Based on these parameters, the sliding-window method is used to form the sequential data by adding three days of information about evolution. Then, multi-layer perceptron and learning vector quantization are employed to predict the flare level within 48h. Experimental results indicate that the performance of the proposed flare forecasting model works better than previous models.