A Framework for Designing and Evaluating Solar Flare Forecasting Systems

Abstract

Disturbances in space weather can negatively affect several fields, including aviation and aerospace, satellites, oil and gas industries, and electrical systems, leading to economic and commercial losses. Solar flares are the most significant events that can affect the Earth's atmosphere, thus leading researchers to drive efforts on their forecasting. The related literature is comprehensive and holds several systems proposed for flare forecasting. However, most techniques are tailor-made and designed for specific purposes, not allowing researchers to customize them in case of changes in data input or in the prediction algorithm. This paper proposes a framework to design, train, and evaluate flare prediction systems which present promising results. Our proposed framework involves model and feature selection, randomized hyper-parameters optimization, data resampling, and evaluation under operational settings. Compared to baseline predictions, our framework generated some proof-of-concept models with positive recalls between 0.70 and 0.75 for forecasting $\ge M$ class flares up to 96 hours ahead while keeping the area under the ROC curve score at high levels.

References in corpus (25)

• SMOTE: Synthetic Minority Over-sampling Technique
• The Helioseismic and Magnetic Imager (HMI) Vector Magnetic Field Pipeline: SHARPs -- Space-weather HMI Active Region Patches
• Solar Flare Prediction Using SDO/HMI Vector Magnetic Field Data with a Machine-Learning Algorithm
• Toward Reliable Benchmarking of Solar Flare Forecasting Methods
• Supermassive Black Holes with High Accretion Rates in Active Galactic Nuclei. IX 10 New Observations of Reverberation Mapping and Shortened H$\beta$ Lags
• Solar Flare Prediction Model with Three Machine-Learning Algorithms Using Ultraviolet Brightening and Vector Magnetogram
• Deep Flare Net (DeFN) model for solar flare prediction
• Predicting Solar Flares Using SDO/HMI Vector Magnetic Data Product and Random Forest Algorithm
• Flare Prediction Using Photospheric and Coronal Image Data
• A Statistical Solar Flare Forecast Method
• A hybrid supervised/unsupervised machine learning approach to solar flare prediction
• Flare forecasting at the Met Office Space Weather Operations Centre
• The Magnetic Classification of Solar Active Regions 1992 - 2015
• Turbulence, Complexity, and Solar Flares
• Prediction of Solar Flares Using Unique Signatures of Magnetic Field Images
• YNOGK: A new public code for calculating null geodesics in the Kerr spacetime
• Ensemble Forecasting of Major Solar Flares -- First Results
• UFCORIN: A Fully Automated Predictor of Solar Flares in GOES X-Ray Flux
• An automated classification approach to ranking photospheric proxies of magnetic energy build-up
• Relationships Between Characteristics of the Line-of-Sight Magnetic Field and Solar Flare Forecasts
• Verification of operational solar flare forecast: Case of Regional Warning Center Japan
• A broadband X-ray spectral study of the intermediate-mass black hole candidate M82 X-1 with NuSTAR, Chandra and Swift
• Magnetic Nonpotentiality in Photospheric Active Regions as a Predictor of Solar Flares
• Using the Maximum X-ray Flux Ratio and X-ray Background to Predict Solar Flare Class
• MOA 2011-BLG-028Lb: a Neptune-mass Microlensing Planet in the Galactic Bulge

Cited by in corpus (5)

• Predicting Solar Flares Using CNN and LSTM on Two Solar Cycles of Active Region Data
• How to Train Your Flare Prediction Model: Revisiting Robust Sampling of Rare Events
• Operational solar flare prediction model using Deep Flare Net
• Towards Coupling Full-disk and Active Region-based Flare Prediction for Operational Space Weather Forecasting
• Solar Active Region Magnetogram Image Dataset for Studies of Space Weather