Solar Flare Short-term Forecast Model Based on Long and Short-term Memory Neural Network

Abstract

Solar flares are a kind of violent solar eruptive activity phenomenon and an important warning device of space weather disturbance. In space weather forecasting, flare forecasting is an important forecast content. This paper proposes a flare prediction model based on long and short-term memory neural network, which uses the time sequence of magnetic field changes in the solar active region in the past 24 h to construct samples, and analyzes the time series evolution of magnetic field characteristics through the long and short-term memory neural network to predict whether $\ge$M-level flares will occur in the next 48 h. This paper uses a data set for all active region samples from May 2010 to May 2017, and selects 10 magnetic field characteristic parameters of SDO/HMI SHARP. In the modeling process, six feature parameters with high weight, gain rate, and coverage rate were selected as input parameters through XGBoost method. Through test comparison, the false report rate and accuracy rate of the model are similar to the traditional machine learning model, and the accuracy rate and critical success index are better than the traditional machine learning model, which are 0.7483 and 0.7402, respectively. The overall effect of the model is better than that of the traditional machine learning model.