基于长短期记忆神经网络的太阳耀斑短期预报

提出了一个基于长短期记忆神经网络的耀斑预报模型，利用过去24 h太阳活动区的磁场变化时序构建样本，通过长短期记忆神经网络对磁场特征时序演化进行分析，预报未来48 h内是否发生≥M级别耀斑事件。使用的数据集为2010年5月到2017年5月所有活动区样本，选取了SDO/HMI SHARP的10个磁场特征参量。在建模过程中通过XGBoost方法选取权重、增益率和覆盖率均较高的6个特征参量作为输入参数。通过测试对比，模型的虚报率和准确率与传统机器学习模型相近，报准率和临界成功指数分别为0.7483和0.7402，优于传统机器学习模型。模型总体效果优于传统机器学习模型。 

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

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 area 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 ≥M-level flares will occur in the next 48 h. This paper uses a data set for all active area 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.