Flare Forecasting

Solar flare forecasting aims to predict the occurrence and intensity of these powerful solar events, crucial for mitigating their impact on Earth's technological infrastructure. Current research heavily utilizes machine learning, particularly deep learning models like convolutional neural networks (CNNs) and time series methods, often incorporating both full-disk solar images and active region-specific data to improve prediction accuracy. These models are being enhanced through techniques such as feature selection, ensemble methods, and the incorporation of historical data to improve forecasting skill and reliability, as measured by metrics like the True Skill Statistic and Heidke Skill Score. Improved forecasting capabilities contribute significantly to space weather preparedness and the protection of critical infrastructure.