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Solar Flare Forecasting with Deep Neural Networks using Compressed Full-disk HMI Magnetograms
Chetraj Pandey; Rafal A. Angryk; Berkay Aydin
We present a solution to full-disk flare prediction using compressed magnetogram images, which was performed by training a set of CNNs to perform operations-ready flare forecasts. We selected two prediction modes, which are both binary for predicting the occurrence of ≥M1.0 and ≥C1.0 class flares within the next 24 hours.
For this, we used the pre-trained AlexNet model and collected compressed images derived from solar magnetograms provided by the Helioseismic and Magnetic Imager (HMI) instrument onboard Solar Dynamics Observatory (SDO).
Figure. Architecture of our AlexNet-based flare prediction model.
Solar Flare Forecasting with Deep Learning-based Time Series Classifiers
Anli Ji , Junzhi Wen , Rafal Angryk , Berkay Aydin
Over the past two decades, machine learning and deep learning techniques for forecasting solar flares have generated great impact due to their ability to learn from a high dimensional data space. However, lack of high quality data from flaring phenomena becomes a constraining factor for such tasks. One of the methods to tackle this complex problem is utilizing trained classifiers with multivariate time series of magnetic field parameters.
In this work, we compare the exceedingly popular multivariate time series classifiers applying deep learning techniques with commonly used machine learning classifiers (i.e., SVM).
We intend to explore the role of data augmentation on time series oriented flare prediction techniques, specifically the deep learning-based ones. We utilize four time series data augmentation techniques and couple them with selected multi- variate time series classifiers to understand how each of them affects the outcome. In the end, we show that the deep learning algorithms as well as augmentation techniques improve our classifiers performance. The resulting classifiers’ performance after augmentation outplayed the traditional flare forecasting techniques.
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