Using SDO Solar Flare Images Along with ML Techniques for Space Weather Prediction

Space weather prediction has become increasingly critical as technological systems—ranging from power grids to satellite communication networks—remain vulnerable to extreme solar activity. Solar flares and coronal mass ejections (CMEs) release high-energy particles and electromagnetic radiation, which can induce geomagnetic storms and disrupt critical infrastructure. Despite significant advances, accurately forecasting the timing, intensity, and impact of these events remains an open challenge due to the complex and non-linear nature of solar activity. Traditional physics-based models, while valuable, are limited by computational constraints and their inability to fully capture the high-dimensional variability of solar phenomena.

Recent progress in machine learning (ML) offers a promising pathway for advancing space weather forecasting by identifying hidden patterns in vast datasets generated by solar observatories. This study utilizes high-resolution, multi-wavelength extreme ultraviolet (EUV) imagery from NASA’s Solar Dynamics Observatory (SDO) and integrates deep learning techniques to improve solar flare prediction. Specifically, convolutional neural networks (CNNs) are employed to extract spatial features of solar flares, while recurrent neural networks (RNNs) model the temporal evolution of solar activity. These models are trained on historical datasets incorporating solar flare images, X-ray flux data, and geomagnetic indices (Dst and Kp) to classify flare intensity and predict potential geomagnetic impacts.

Preliminary results demonstrate that the ML models outperform traditional methods in both detection accuracy and real-time prediction capabilities. Additionally, by leveraging the multi-channel nature of SDO’s EUV imagery, the models can capture complex spatiotemporal dynamics of solar flares, providing a more nuanced understanding of their development. However, key challenges remain, including improving model interpretability, ensuring data completeness, and integrating diverse data sources into operational space weather forecasting frameworks. This study highlights both the potential of machine learning in heliophysics and the ongoing need for interdisciplinary approaches to develop robust and scalable space weather prediction systems.