Yasser Abduallah, Jason T. L. Wang, Haimin Wang
Solar flare prediction plays an important role in understanding and forecasting space weather. The main goal of the Helioseismic and Magnetic Imager (HMI), one of the instruments on NASA's Solar Dynamics Observatory, is to study the origin of solar variability and characterize the Sun's magnetic activity. HMI provides continuous full-disk observations of the solar vector magnetic field with high cadence data that lead to reliable predictive capability; yet, solar flare prediction effort utilizing these data is still limited. Here we present a flare prediction system, named FlareML, for predicting solar flares using machine learning (ML) based on HMI’s data products. Specifically, we construct training data by utilizing the physical parameters provided by the Space-weather HMI Active Region Patches (SHARP) and categorize solar flares into four classes, namely B, C, M, X, according to the X-ray flare catalogs available at the National Centers for Environmental Information (NCEI). Thus, the solar flare prediction problem at hand is essentially a multi-class (i.e., four-class) classification problem. The FlareML system employs four machine learning methods to tackle this multi-class prediction problem. These four methods are: (i) ensemble (ENS), (ii) random forests (RF), (iii) multilayer perceptrons (MLP), and (iv) extreme learning machines (ELM). ENS works by taking the majority vote of the results obtained from RF, MLP and ELM.
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For the latest updates of FlareML refer to https://github.com/deepsuncode/Machine-learning-as-a-service
| Library | Version | Description |
|---|---|---|
| matplotlib | 3.4.2 | Graphics and visualization |
| numpy | 1.19.5 | Array manipulation |
| scikit-learn | 0.24.2 | Machine learning |
| sklearn-extensions | 0.0.2 | Extension for scikit-learn |
| pandas | 1.2.4 | Data loading and manipulation |