A Deep-Learning Approach for Operation of an Automated Realtime Flare Forecast
By Yuko Hada Muranushi, Takayuki Muranushi, Ayumi Asai, Daisuke Okanohara, Raymond H. Putra, Genta Watanabe, Shigeru Nemoto, Kazunari Shibata. 2016
13 citations recorded by the source corpus.
Automated forecasts serve important role in space weather science, by providing statistical insights to flare-trigger mechanisms, and by enabling tailor-made forecasts and high-frequency forecasts. Only by realtime forecast we can experimentally measure the performance of flare-forecasting methods while confidently avoiding overlearning. We have been operating unmanned flare forecast service since August, 2015 that provides 24-hour-ahead forecast of solar flares, every 12 minutes. We report the method and prediction results of the system.
Subfields: Artificial Intelligence, Astronomy and Astrophysics
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In Artificial Intelligence
- Ala Al-Fuqaha
- Mohsen Guizani
- Sameh Sorour
- M. Mohammadi
- Ekram Hossain
- Sanjay Chawla
- Raghavendra Chalapathy
- Syed Ali Hassan
Citations in the corpus
- Deep Learning for IoT Big Data and Streaming Analytics: A Survey (2017, methodology): “In [86], an online forecasting system based on LSTM is proposed to predict the solar flare power 24 hours ahead.”