Image Feature Extraction for Solar Flare Prediction
Solar flare is the most violent solar activity which is the main driving source of space weather, so accurate prediction of flare occurrence in coming days would manner disaster treatment and protection. Due to detailed reasons of solar flares eruption are not clear in current field, so the prediction clues rely mainly on observing solar images. Many predictors have been used for solar flare prediction, mainly based on expert system or physical knowledge. In this paper, a system based on image information without prior physical knowledge for solar flare prediction is presented. The Magnetic field and texture distribution of active region, the largest sunspot groups fractal dimension, positive and negative areas and girth, extracted from SOHO/MDI longitudinal magnetograms are used in the model to describe the complexity of the photospheric magnetic field. Machine learning algorithms: C4.5 decision tree, CART tree and Bayesian network are employed to predict the flare level within 48 hours. It is concluded that the model trained by C4.5 decision tree could predict flare occurrence effectively.
image data mining image processing machine learning
Xiaopeng Zhang Jinfu Liu Qiang Wang
Harbin Institute of Technology Harbin 150001, China
国际会议
2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)
上海
英文
924-928
2011-10-15（万方平台首次上网日期，不代表论文的发表时间）