Machine learning techniques applied to solar flares forecasting

Ribeiro, Fernanda Vieira; Gradvohl, André Leon Sampaio

doi:10.1016/j.ascom.2021.100468

Cited by 26 publications

(20 citation statements)

References 27 publications

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“…Methods to tackle class imbalance can be categorized into three types: data-level methods, algorithm-level methods, and a hybrid of the two (Krawczyk 2016;Johnson & Khoshgoftaar 2019). Data-level methods rebalance the class distribution by oversampling the minority class and/or undersampling the majority class-both have been used in flare forecasting (e.g., Yu et al 2010;Ribeiro & Gradvohl 2021). Classifiers trained on rebalanced data sets, without being biased toward the majority class, are more likely to effectively detect the event of interest.…”

Section: Random Undersamplingmentioning

confidence: 99%

Predicting Solar Flares Using CNN and LSTM on Two Solar Cycles of Active Region Data

Sun

Bobra

Wang

et al. 2022

ApJ

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We consider the flare prediction problem that distinguishes flare-imminent active regions that produce an M- or X-class flare in the succeeding 24 hr, from quiet active regions that do not produce any flares within ±24 hr. Using line-of-sight magnetograms and parameters of active regions in two data products covering Solar Cycles 23 and 24, we train and evaluate two deep learning algorithms—a convolutional neural network (CNN) and a long short-term memory (LSTM)—and their stacking ensembles. The decisions of CNN are explained using visual attribution methods. We have the following three main findings. (1) LSTM trained on data from two solar cycles achieves significantly higher true skill scores (TSSs) than that trained on data from a single solar cycle with a confidence level of at least 0.95. (2) On data from Solar Cycle 23, a stacking ensemble that combines predictions from LSTM and CNN using the TSS criterion achieves a significantly higher TSS than the “select-best” strategy with a confidence level of at least 0.95. (3) A visual attribution method called “integrated gradients” is able to attribute the CNN’s predictions of flares to the emerging magnetic flux in the active region. It also reveals a limitation of CNNs as flare prediction methods using line-of-sight magnetograms: it treats the polarity artifact of line-of-sight magnetograms as positive evidence of flares.

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Section: Random Undersamplingmentioning

confidence: 99%

Predicting Solar Flares Using CNN and LSTM on Two Solar Cycles of Active Region Data

Sun

Bobra

Wang

et al. 2022

ApJ

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show abstract

“…Data-level methods rebalance the class distribution by oversampling the minority class and/or undersampling the majority class-both have been used in flare forecasting (e.g. Ribeiro & Gradvohl 2021;Yu et al 2010). Classifiers trained on rebalanced datasets, without being biased towards the majority class, are more likely to effectively detect the event of interest.…”

Section: Random Undersamplingmentioning

confidence: 99%

Predicting Solar Flares Using CNN and LSTM on Two Solar Cycles of Active Region Data

Sun,

Bobra,

Wang

et al. 2022

Preprint

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We consider the flare prediction problem that distinguishes flare-imminent active regions that produce an M-or X-class flare in the future 24 hours, from quiet active regions that do not produce any flare within ±24 hours. Using line-of-sight magnetograms and parameters of active regions in two data products covering Solar Cycle 23 and 24, we train and evaluate two deep learning algorithms-CNN and LSTM-and their stacking ensembles. The decisions of CNN are explained using visual attribution methods. We have the following three main findings. (1) LSTM trained on data from two solar cycles achieves significantly higher True Skill Scores (TSS) than that trained on data from a single solar cycle with a confidence level of at least 0.95. (2) On data from Solar Cycle 23, a stacking ensemble that combines predictions from LSTM and CNN using the TSS criterion achieves significantly higher TSS than the "select-best" strategy with a confidence level of at least 0.95. (3) A visual attribution method called Integrated Gradients is able to attribute the CNN's predictions of flares to the emerging magnetic flux in the active region. It also reveals a limitation of CNN as a flare prediction method using line-of-sight magnetograms: it treats the polarity artifact of line-of-sight magnetograms as positive evidence of flares.

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“…Liu et al (2017) attempted a multiclass classification using a random forest; Nishizuka et al (2017) and Florios et al (2018) compared various ML algorithms, which included SVM, multilayer perceptrons, random forest, and the k nearest neighbors (KNN) algorithm; Nishizuka et al (2018Nishizuka et al ( , 2020 trained a deep neural network for binary classification; and Campi et al (2019) used hybrid LASSO and random forest algorithms on features derived during the FLARECAST project. In a recent study, Ribeiro & Gradvohl (2021) used LightGBM for flare forecasting and showed a nice comparison with existing ML models. Classification using KNN was attempted by Hamdi et al (2017) for univariate time series and by Filali Boubrahimi & Angryk (2018) for multivariate time series.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Machine-learning Models for Solar Flare Forecasting: Identifying High-performing Active Region Flare Indicators

Sinha¹,

Gupta

Singh

et al. 2022

ApJ

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Solar flares create adverse space weather impacting space- and Earth-based technologies. However, the difficulty of forecasting flares, and by extension severe space weather, is accentuated by the lack of any unique flare trigger or a single physical pathway. Studies indicate that multiple physical properties contribute to active region flare potential, compounding the challenge. Recent developments in machine learning (ML) have enabled analysis of higher-dimensional data leading to increasingly better flare forecasting techniques. However, consensus on high-performing flare predictors remains elusive. In the most comprehensive study to date, we conduct a comparative analysis of four popular ML techniques (k nearest neighbors, logistic regression, random forest classifier, and support vector machine) by training these on magnetic parameters obtained from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory for the entirety of solar cycle 24. We demonstrate that the logistic regression and support vector machine algorithms perform extremely well in forecasting active region flaring potential. The logistic regression algorithm returns the highest true skill score of 0.967 ± 0.018, possibly the highest classification performance achieved with any strictly parametric study. From a comparative assessment, we establish that magnetic properties like total current helicity, total vertical current density, total unsigned flux, R_VALUE, and total absolute twist are the top-performing flare indicators. We also introduce and analyze two new performance metrics, namely, severe and clear space weather indicators. Our analysis constrains the most successful ML algorithms and identifies physical parameters that contribute most to active region flare productivity.

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Machine learning techniques applied to solar flares forecasting

Cited by 26 publications

References 27 publications

Predicting Solar Flares Using CNN and LSTM on Two Solar Cycles of Active Region Data

Predicting Solar Flares Using CNN and LSTM on Two Solar Cycles of Active Region Data

Predicting Solar Flares Using CNN and LSTM on Two Solar Cycles of Active Region Data

A Comparative Analysis of Machine-learning Models for Solar Flare Forecasting: Identifying High-performing Active Region Flare Indicators

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