An intelligent solar flare prediction model based on X-ray flow curves using Long Short-Term Memory

Solar flares are violent solar outbursts which have a great influence on the space environment surrounding Earth, potentially causing disruption of the ionosphere and interference with the geomagnetic field, thus causing magnetic storms. Consequently, it is very important to accurately predict the time period of solar flares. This paper proposes a flare prediction model, based on physical images of active solar regions. We employ X-ray flow curves recorded directly by the Geostationary Operational Environmental Satellite, used as input data for the model, allowing us to largely avoid the influence of accidental errors, effectively improving the model prediction efficiency. A model based on the X-ray flow curve can predict whether there will be a flare event within 24 hours. The reverse can also be verified by the peak of the X-ray flow curve to see if a flare has occurred within the past 24 hours. The True Positive Rate and False Positive Rate of the prediction model, based on physical images of active regions are 0.6070 and 0.2410 respectively, and the accuracy and True Skill Statistics are 0.7590 and 0.5556. Our model can effectively improve prediction efficiency compared with models based on the physical parameters of active regions or magnetic field records, providing a simple method for solar flare prediction.