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    The Automated Prediction of Solar Flares from SDO Images Using Deep Learning

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    Publication date
    2021-04-15
    Author
    Abed, Ali K.
    Qahwaji, Rami S.R.
    Abed, A.
    Keyword
    Solar flare prediction
    Deep learning
    Convolution neural network
    Softmax classifier
    Peer-Reviewed
    Yes
    Open Access status
    Not Open Access
    
    Metadata
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    Abstract
    In the last few years, there has been growing interest in near-real-time solar data processing, especially for space weather applications. This is due to space weather impacts on both space-borne and ground-based systems, and industries, which subsequently impacts our lives. In the current study, the deep learning approach is used to establish an automated hybrid computer system for a short-term forecast; it is achieved by using the complexity level of the sunspot group on SDO/HMI Intensitygram images. Furthermore, this suggested system can generate the forecast for solar flare occurrences within the following 24 h. The input data for the proposed system are SDO/HMI full-disk Intensitygram images and SDO/HMI full-disk magnetogram images. System outputs are the “Flare or Non-Flare” of daily flare occurrences (C, M, and X classes). This system integrates an image processing system to automatically detect sunspot groups on SDO/HMI Intensitygram images using active-region data extracted from SDO/HMI magnetogram images (presented by Colak and Qahwaji, 2008) and deep learning to generate these forecasts. Our deep learning-based system is designed to analyze sunspot groups on the solar disk to predict whether this sunspot group is capable of releasing a significant flare or not. Our system introduced in this work is called ASAP_Deep. The deep learning model used in our system is based on the integration of the Convolutional Neural Network (CNN) and Softmax classifier to extract special features from the sunspot group images detected from SDO/HMI (Intensitygram and magnetogram) images. Furthermore, a CNN training scheme based on the integration of a back-propagation algorithm and a mini-batch AdaGrad optimization method is suggested for weight updates and to modify learning rates, respectively. The images of the sunspot regions are cropped automatically by the imaging system and processed using deep learning rules to provide near real-time predictions. The major results of this study are as follows. Firstly, the ASAP_Deep system builds on the ASAP system introduced in Colak and Qahwaji (2009) but improves the system with an updated deep learning-based prediction capability. Secondly, we successfully apply CNN to the sunspot group image without any pre-processing or feature extraction. Thirdly, our system results are considerably better, especially for the false alarm ratio (FAR); this reduces the losses resulting from the protection measures applied by companies. Also, the proposed system achieves a relatively high scores for True Skill Statistics (TSS) and Heidke Skill Score (HSS).
    URI
    http://hdl.handle.net/10454/18410
    Version
    No full-text in the repository
    Citation
    Abed AK, Qahwaji RSR and Abed A (2021) The Automated Prediction of Solar Flares from SDO Images Using Deep Learning. Advances in Space Research. 67(8): 2544-2557.
    Link to publisher’s version
    https://doi.org/10.1016/j.asr.2021.01.042
    Type
    Article
    Collections
    Engineering and Digital Technology Publications

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