We developed a solar flare prediction model using a deep neural network (DNN) named Deep Flare Net (DeFN). This model can calculate the probability of flares occurring in the following 24 hr in each active region, which is used to determine the most likely maximum classes of flares via a binary classification (e.g., ≥M class versus <M class or ≥C class versus <C class). From 3 ×105 observation images taken during 2010-2015 by the Solar Dynamic Observatory, we automatically detected sunspots and calculated 79 features for each region, to which flare occurrence labels of X-, M-, and C-class were attached. We adopted the features used in Nishizuka et al. (2017) and added some features for operational prediction: coronal hot brightening at 131 Å (T ≥ 107 K) and the X-ray and 131 Å intensity data 1 and 2 hr before an image. For operational evaluation, we divided the database into two for training and testing: the data set in 2010-2014 for training, and the one in 2015 for testing. The DeFN model consists of deep multilayer neural networks formed by adapting skip connections and batch normalizations. To statistically predict flares, the DeFN model was trained to optimize the skill score, i.e., the true skill statistic (TSS). As a result, we succeeded in predicting flares with TSS = 0.80 for ≥M-class flares and TSS = 0.63 for ≥C-class flares. Note that in usual DNN models, the prediction process is a black box. However, in the DeFN model, the features are manually selected, and it is possible to analyze which features are effective for prediction after evaluation.
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