Solar Flare Prediction Using Low-Resolution Solar Global Line-of-Sight Magnetogram with Active Region Extraction and LSTM Model

Abstract

Solar flare prediction is essential for mitigating the adverse effects of space weather on satellites and ground-based infrastructure. This study proposes a novel method for solar flare prediction using low-resolution (512 × 512) line-of-sight magnetogram data provided by the Solar Dynamics Observatory (SDO). By utilizing low-resolution data, the proposed method aims to reduce computational costs while maintaining practical predictive accuracy. The method involves extracting active regions from the SDO's line-of-sight magnetogram, computing features such as magnetic complexity and polarity distribution, and applying a Long Short-Term Memory (LSTM) model for time-series analysis. The prediction focuses on M-class or larger flares. Using data from 2011 to 2014, the method achieved a True Skill Statistic (TSS) of 0.763, demonstrating its effectiveness. This result highlights the feasibility of using low-resolution data for efficient and accurate solar flare forecasting.