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arXiv · 2112.00977

Operational solar flare prediction model using Deep Flare Net

Naoto Nishizuka·Yuki Kubo·Komei Sugiura·Mitsue Den·Mamoru Ishii
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Abstract

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 =C-class and =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.

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Naoto Nishizuka, Yuki Kubo, Komei Sugiura, Mitsue Den, Mamoru Ishii. 2021-12-02. Operational solar flare prediction model using Deep Flare Net. https://doi.org/10.1186/s40623-021-01381-9

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