We developed an operational solar flare prediction model using deep neural networks, named Deep Flare Net (DeFN). DeFN can issue probabilistic forecasts of solar flares in two categories, such as ≥ M-class and < M-class events or ≥ C-class and < C-class events, occurring in the next 24 h after observations and the maximum class of flares occurring in the next 24 h. DeFN is set to run every 6 h and has been operated since January 2019. The input database of solar observation images taken by the Solar Dynamic Observatory (SDO) is downloaded from the data archive operated by the Joint Science Operations Center (JSOC) of Stanford University. Active regions are automatically detected from magnetograms, and 79 features are extracted from each region nearly in real time using multiwavelength observation data. Flare labels are attached to the feature database, and then, the database is standardized and input into DeFN for prediction. DeFN was pretrained using the datasets obtained from 2010 to 2015. The model was evaluated with the skill score of the true skill statistics (TSS) and achieved predictions with TSS = 0.80 for ≥ M-class flares and TSS = 0.63 for ≥ C-class flares. For comparison, we evaluated the operationally forecast results from January 2019 to June 2020. We found that operational DeFN forecasts achieved TSS = 0.70 (0.84) for ≥ C-class flares with the probability threshold of 50 (40)%, although there were very few M-class flares during this period and we should continue monitoring the results for a longer time. Here, we adopted a chronological split to divide the database into two for training and testing. The chronological split appears suitable for evaluating operational models. Furthermore, we proposed the use of time-series cross-validation. The procedure achieved TSS = 0.70 for ≥ M-class flares and 0.59 for ≥ C-class flares using the datasets obtained from 2010 to 2017. Finally, we discuss the standard evaluation methods for operational forecasting models, such as the preparation of observation, training, and testing datasets, and the selection of verification metrics.

我们使用深层神经网络（称为深层火炬网（DeFN））开发了一个可操作的太阳耀斑预测模型。DeFN可以发出太阳耀斑的概率预测，分为两类，例如≥M类和<M类事件或≥C类和<C类事件，发生在观测之后的24小时内，并且是最大耀斑在接下来的24小时内发生。DeFN设置为每6小时运行一次，自2019年1月开始运行。由太阳动态观测站（SDO）拍摄的太阳观测图像输入数据库可从由斯坦福大学联合科学运营中心（JSOC）运营的数据档案中下载大学。活动区域是从磁图自动检测到的，并使用多波长观测数据几乎实时地从每个区域中提取了79个特征。将耀斑标签粘贴到特征数据库，然后将数据库标准化并输入到DeFN中进行预测。使用从2010年到2015年获得的数据集对DeFN进行了预训练。使用真实技能统计数据（TSS）的技能得分对模型进行评估，并对≥M级耀斑的TSS = 0.80，对于≥C级耀斑的TSS = 0.63进行预测耀斑。为了进行比较，我们评估了2019年1月至2020年6月的运营预测结果。我们发现，≥C级耀斑的运行DeFN预测达到TSS = 0.70（0.84），概率阈值为50（40）％，尽管存在非常高的概率。在此期间，很少有M级耀斑，我们应该继续监测结果更长的时间。在这里，我们按时间顺序将数据库分为两部分进行训练和测试。按时间顺序划分似乎适合评估运营模型。此外，我们建议使用时间序列交叉验证。该程序使用2010年至2017年获得的数据集，对于M级以上耀斑，TSS = 0.70，对于C级以上耀斑，TSS = 0.59。最后，我们讨论了业务预测模型的标准评估方法，例如观察值的准备，训练的，测试数据集以及验证指标的选择。