Solar flare prediction by SVM integrated GA

Solar flare has various influences on the global environment, in particular on the magnetic storm and the likelihood of natural disasters. Specifically, it may have serious impacts on the Earth such as failure of satellite communication and navigation (GPS), satellite damage, increased radiation exposure to astronauts, geomagnetic storm and aurora, and power plant failures causing more serious disaster. For a precise forecast of larger scale solar flares causing serious disaster, it is important to improve the space weather forecast, which is basically a daily forecast of the solar flare. In the work so far, a machine-learning algorithm called Support Vector Machine (SVM) was used to forecast the space weather. We extend this technology by integrating Genetic Algorithm (GA) elaborately combined with Case Based Reasoning for more precise forecast or imbalanced data classification. Finally, basic evaluation of this architectural idea called CBGALO shows it is promising in improving solar flare prediction.