Application of the Deep Convolutional Neural Network to the Forecast of Solar Flare Occurrence Using Full-disk Solar Magnetograms

Abstract

In this study, we present the application of the Convolutional Neural Network (CNN) to the forecast of solar flare occurrence. For this, we consider three CNN models (two pretrained models, AlexNet and GoogLeNet, and one newly proposed model). Our inputs are SOHO/Michelson Doppler Imager (from 1996 May to 2010 December) and SDO/Helioseismic and Magnetic Imager (from 2011 January to 2017 June) full-disk magnetograms at 00:00 UT. Model outputs are the “Yes or No” of daily flare occurrence (C, M, and X classes) and they are compared with GOES observations. We train the models using the input data and observations from 1996 to 2008, covering the entire solar cycle 23, and test them using the data sets from 2009 to 2017, covering solar cycle 24. Then we compare the results of the CNN models with those of three previous flare forecast models in view of statistical scores. The major results from this study are as follows. First, we successfully apply CNN to the full-disk solar magnetograms without any preprocessing or feature extraction. Second, the results of our CNN models are slightly better in Heidke skill score and true skill statistics, and considerably better in false alarm ratio (FAR) and critical success index than the previous solar flare forecasting models. Third, our proposed model has better values of all statistical scores except for FAR, than the other two pretrained models. Our results indicate a sufficient possibility that deep learning methods can improve the capability of the solar flare forecast as well as similar types of forecast problems.

Leveraging the mathematics of shape for solar magnetic eruption prediction

“…Approaches such as Guerra et al (2018) and Kontogiannis et al (2018), while not machine-learning approaches themselves, provide statistical tools for evaluating the engineered features in terms of their potential advantage in machine learning models. Finally, while most of the above methods focus on developing ML models using engineered features, recent methods have employed a convolutional neural network (CNN) approach which automatically extracts features from raw magnetogram data that are important to predicting flares (Huang et al, 2018;Park et al, 2018;Zheng et al, 2019).…”

Solar Event Detection Using Deep-Learning-Based Object Detection Methods

“…The total data size from SDO to date is approximately 5 PB. This big data set obtained by SDO enables various studies and the application of deep learning techniques in solar physics (Armstrong and Fletcher, 2019;Jeong et al, 2020;Kim et al, 2019;Kucuk, Aydin, and Angryk, 2017;Park et al, 2018Park et al, , 2020Rahman et al, 2020). We use SDO/AIA and HMI data to train the solar event auto-detection models.…”

Automatic analysis of magnetograms for identification and classification of active regions using Deep Learning

“…In turn, Park et al (2018) applied deep convolutional networks to try to predict the occurrence of solar flares. In this case, the work used the deep neural network to associate images to the probability of solar flares occurrence.…”