Predicting Solar Flare Index Using Statistical And Machine Learning Methods

• Activity Number: 382 - Statistical and Machine Learning Efforts on Solar Flare Predictions II
• Type: Topic-Contributed
• Date/Time: Thursday, August 12, 2021 : 12:00 PM to 1:50 PM
• Sponsor: Section on Physical and Engineering Sciences
• Author(s): Zuofeng Shang* and Hewei Zhang
• Companies: New Jersey Institute of Technology and New Jersey Institute of Technology
• Keywords: Power Transformation; Feature Ranking; B-spline; SMOGN

Abstract

Solar flares are marked by giant bursts of X-rays and energy that travel at the speed of light, sometimes accompanied with corona mass ejection, which can damage facilities including satellites, communication systems and even ground-based technologies. Hence, the prediction of flares is vital to protect the facilities. Extensive studies have been conducted focusing on predicting the occurrence of flares in different classes for a certain active region (AR) in the next 24 hours with "yes" or "no" answers. In this talk, we introduce a different perspective of prediction involving the Flare Index (FI) that quantifies flares with occurrence probability and energy released. Specifically, we analyze ARs from May 2010 to Dec 2017 and their associated flares identified by the GOES, wherein 25 SHARP parameters are extracted to produce the FIs. With the SHARP parameters and their related FIs, we predict the FI for an AR in the next 1-day period by SMOGN algorithm, power transformation and B-spline regression, improving the accuracy of flare prediction for large FIs. In addition, we rank the 25 SHARP parameters based on their importance in flare prediction.