Research Progress on Solar Flare Forecast Methods Based on Data-driven Models

Abstract

Abstract Eruption of solar flares is a complex nonlinear process, and the rays and high-energy particles generated by such an eruption are detrimental to the reliability of space-based or ground-based systems. So far, there are not reliable physical models to accurately account for the flare outburst mechanism, but a lot of data-driven models have been built to study a solar flare and forecast it. In the paper, the status of solar-flare forecasting is reviewed, with emphasis on the machine learning methods and data-processing techniques used in the models. At first, the essential forecast factors strongly relevant to solar flare outbursts, such as classification information of the sunspots and evolution pattern of the magnetic field, are reviewed and analyzed. Subsequently, methods of resampling for data preprocessing are introduced to solve the problems of class imbalance in the solar flare samples. Afterwards, typical model structures adopted for flare forecasting are reviewed from the aspects of the single and fusion models, and the forecast performances of the different models are analyzed. Finally, we herein summarize the current research on solar flare forecasting and outline its development trends.