A Framework for Designing and Evaluating Solar Flare Forecasting Systems
This addresses the need for adaptable forecasting tools in space weather prediction, which is incremental as it builds on existing methods.
The paper tackles the problem of customizing solar flare forecasting systems by proposing a framework for designing, training, and evaluating them, achieving recalls of 0.70-0.75 for forecasting M-class flares up to 96 hours ahead with high ROC AUC scores.
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 $\geq M$ class flares up to 96 hours ahead while keeping the area under the ROC curve score at high levels.