Solar Flare Prediction Model with Three Machine-learning Algorithms using Ultraviolet Brightening and Vector Magnetograms

2016/11/06 by N. Nishizuka, Naoto Nishizuka, K. Sugiura +8 · 195 citations
Computer Science · Physics and Astronomy · #Algorithm #Artificial intelligence #Astrophysics #Computer science #Flare #Geostationary Operational Environmental Satellite #Ionosphere and magnetosphere dynamics #Machine learning #Magnetic field #Magnetic flux #Magnetogram #Physics #Satellite #Solar Radiation and Photovoltaics #Solar and Space Plasma Dynamics #Solar flare #Support vector machine #astro-ph.SR

paper · pdf · doi:10.3847/1538-4357/835/2/156

published in The Astrophysical Journal 835(2), 156 (IOP Publishing) · 27 pages, 3 figures

arxiv created 2016/11/06 · openalex created_date 2016/11/30 · openalex publication_date 2017/01/25 · arxiv updated 2017/02/01 · openalex updated_date 2026/08/05

Abstract

Abstract We developed a flare prediction model using machine learning, which is optimized to predict the maximum class of flares occurring in the following 24 hr. Machine learning is used to devise algorithms that can learn from and make decisions on a huge amount of data. We used solar observation data during the period 2010–2015, such as vector magnetograms, ultraviolet (UV) emission, and soft X-ray emission taken by the Solar Dynamics Observatory and the Geostationary Operational Environmental Satellite . We detected active regions (ARs) from the full-disk magnetogram, from which ∼60 features were extracted with their time differentials, including magnetic neutral lines, the current helicity, the UV brightening, and the flare history. After standardizing the feature database, we fully shuffled and randomly separated it into two for training and testing. To investigate which algorithm is best for flare prediction, we compared three machine-learning algorithms: the support vector machine, k-nearest neighbors (k-NN), and extremely randomized trees. The prediction score, the true skill statistic, was higher than 0.9 with a fully shuffled data set, which is higher than that for human forecasts. It was found that k-NN has the highest performance among the three algorithms. The ranking of the feature importance showed that previous flare activity is most effective, followed by the length of magnetic neutral lines, the unsigned magnetic flux, the area of UV brightening, and the time differentials of features over 24 hr, all of which are strongly correlated with the flux emergence dynamics in an AR.

Citations

• The classification of sunspot groups
• The magnetic nature of solar flares
• TOWARD RELIABLE BENCHMARKING OF SOLAR FLARE FORECASTING METHODS
• Solar magnetic feature detection and tracking for space weather monitoring
• The Solar Dynamics Observatory (SDO)
• The Helioseismic and Magnetic Imager (HMI) Investigation for the Solar Dynamics Observatory (SDO)
• The Atmospheric Imaging Assembly (AIA) on the Solar Dynamics Observatory (SDO)
• The Elements of Statistical Learning: Data Mining, Inference, and Prediction

Cited by

• The Deep Learning-Based Dual-Branch Multimodal Fusion Model for Solar Flare Prediction
• Brightenings AnD Polarity Inversion Tracking (BADPIT) method for studying solar active region evolution before major solar flares
• A Convolutional Neural Network-Derived Catalog of Solar Flares from Soft X-Ray Observations
• ALEXIS: Recreating the X-Ray Emission from the Full Solar Disk as a Linear Combination of Discrete Regions in the Extreme Ultraviolet and Soft X-Rays

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