  We developed a solar flare prediction model using a deep neural network
(DNN), named Deep Flare Net (DeFN). The model can calculate the probability of
flares occurring in the following 24 h 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 3x10^5
observation images taken during 2010-2015 by 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 A (T>=10^7 K) and the
X-ray and 131 A intensity data 1 and 2 h before an image. For operational
evaluation, we divided the database into two for training and testing: the
dataset 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.
