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2022 | OriginalPaper | Chapter

6. Deep Learning in Solar Forecasting Tasks

Authors : Long Xu, Yihua Yan, Xin Huang

Published in: Deep Learning in Solar Astronomy

Publisher: Springer Nature Singapore

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Abstract

Besides classification and generation, deep learning is also applicable to time series analysis. Unlike CNN which accepts singe image input, RNN is specifically designed for handling time series input, e.g., video sequence, natural language processing. As the best representative of RNN, LSTM has been widely exploited in various of time series analysis, achieving big success. In this chapter, it is applied to solar activity/event forecasting and solar radiation index prediction. As one of the most violent solar eruptions, solar flare is the main driving source of catastrophic space weather, so forecasting of solar flare is of great importance. The solar radio flux of 10.7 cm is a typical index for measuring global solar activity. It is a typical indicator of long-term space weather.

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previous chapter Deep Learning in Solar Image Generation Tasks
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Metadata
Title
Deep Learning in Solar Forecasting Tasks
Authors
Long Xu
Yihua Yan
Xin Huang
Copyright Year
2022
Publisher
Springer Nature Singapore
Book
Deep Learning in Solar Astronomy

Print ISBN: 978-981-19-2745-4

Electronic ISBN: 978-981-19-2746-1

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-981-19-2746-1_6

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