Solar Flare Intensity Prediction with Machine Learning Models

Abstract

We develop a mixed Long Short Term Memory (LSTM) regression model to predict the maximum solar flare intensity within a 24-hour time window 0$\sim$24, 6$\sim$30, 12$\sim$36 and 24$\sim$48 hours ahead of time using 6, 12, 24 and 48 hours of data (predictors) for each Helioseismic and Magnetic Imager (HMI) Active Region Patch (HARP). The model makes use of (1) the Space-weather HMI Active Region Patch (SHARP) parameters as predictors and (2) the exact flare intensities instead of class labels recorded in the Geostationary Operational Environmental Satellites (GOES) data set, which serves as the source of the response variables. Compared to solar flare classification, the model offers us more detailed information about the exact maximum flux level, i.e. intensity, for each occurrence of a flare. We also consider classification models built on top of the regression model and obtain better results in solar flare classifications. Our results suggest that the most efficient time period for predicting the solar activity is within 24 hours before the prediction time using the SHARP parameters and the LSTM model.

28 pages, 15 figures

References in corpus (12)

• How transferable are features in deep neural networks?
• The Helioseismic and Magnetic Imager (HMI) Vector Magnetic Field Pipeline: SHARPs -- Space-weather HMI Active Region Patches
• The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting
• Solar Flare Prediction Using SDO/HMI Vector Magnetic Field Data with a Machine-Learning Algorithm
• Toward Reliable Benchmarking of Solar Flare Forecasting Methods
• Driving major solar flares and eruptions: a review
• A Comparison of Flare Forecasting Methods, I: Results from the "All-Clear" Workshop
• Forecasting Solar Flares Using Magnetogram-based Predictors and Machine Learning
• Predicting Solar Flares Using a Long Short-Term Memory Network
• A Comparison of Flare Forecasting Methods. II. Benchmarks, Metrics and Performance Results for Operational Solar Flare Forecasting Systems
• Identifying Solar Flare Precursors Using Time Series of SDO/HMI Images and SHARP Parameters
• A Comparison of Flare Forecasting Methods. III. Systematic Behaviors of Operational Solar Flare Forecasting Systems

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