Prediction of Solar Flare Size and Time-to-Flare Using Support Vector Machine Regression

Boucheron, Laura E.; Al-Ghraibah, Amani; McAteer, R. T. James

doi:10.1088/0004-637x/812/1/51

Cited by 51 publications

(51 citation statements)

References 47 publications

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“…We next compare our LSTM framework with five closely related machine learning methods including multilayer perceptrons (MLP) (Haykin & Network 2004;Florios et al 2018), Jordan network (JN) (Jordan 1997), support vector machines (SVM) (Qahwaji & Colak 2007;Yuan et al 2010;Bobra & Couvidat 2015;Boucheron et al 2015;Muranushi et al 2015;Florios et al 2018), random forests (RF) (Barnes et al 2016;Liu et al 2017;Florios et al 2018), and a recently published deep learning-based method, Deep Flare Net (DeFN; Nishizuka et al 2018). All these methods including ours (LSTM) can be used as a binary classification model (Nishizuka et al 2018;Jonas et al 2018) or a probabilistic forecasting model (Florios et al 2018).…”

Section: Model Evaluationmentioning

confidence: 99%

“…Machine learning is a subfield of artificial intelligence, which grants computers abilities to learn from the past data and make predictions on unseen future data (Alpaydin 2009). Commonly used machine learning methods for flare prediction include decision trees (Yu et al 2009(Yu et al , 2010, random forests (Barnes et al 2016;Liu et al 2017;Florios et al 2018;Breiman 2001), k-nearest neighbors Huang et al 2013;Winter & Balasubramaniam 2015;Nishizuka et al 2017), support vector machines (Qahwaji & Colak 2007;Yuan et al 2010;Bobra & Couvidat 2015;Boucheron et al 2015;Muranushi et al 2015;Florios et al 2018), ordinal logistic regression (Song et al 2009), the least absolute shrinkage and selection operator (LASSO) (Benvenuto et al 2018;Jonas et al 2018), extremely randomized trees (Nishizuka et al 2017), and neural networks (Qahwaji & Colak 2007;Wang et al 2008;Colak & Qahwaji 2009;Higgins et al 2011;Ahmed et al 2013). Recently, Nishizuka et al (2018) adopted a deep neural network, named Deep Flare Net, for flare prediction.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Solar Flares Using a Long Short-term Memory Network

Líu

Liu

Wang

et al. 2019

ApJ

117

126

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We present a long short-term memory (LSTM) network for predicting whether an active region (AR) would produce a Υ-class flare within the next 24 hours. We consider three Υ classes, namely ≥M5.0 class, ≥M class, and ≥C class, and build three LSTM models separately, each corresponding to a Υ class. Each LSTM model is used to make predictions of its corresponding Υ-class flares. The essence of our approach is to model data samples in an AR as time series and use LSTMs to capture temporal information of the data samples. Each data sample has 40 features including 25 magnetic parameters obtained from the Space-weather HMI Active Region Patches (SHARP) and related data products as well as 15 flare history parameters. We survey the flare events that occurred from 2010 May to 2018 May, using the GOES X-ray flare catalogs provided by the National Centers for Environmental Information (NCEI), and select flares with identified ARs in the NCEI flare catalogs. These flare events are used to build the labels (positive vs. negative) of the data samples. Experimental results show that (i) using only 14-22 most important features including both flare history and magnetic parameters can achieve better performance than using all the 40 features together; (ii) our LSTM network outperforms related machine learning methods in predicting the labels of the data samples. To our knowledge, this is the first time that LSTMs have been used for solar flare prediction.

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Section: Model Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predicting Solar Flares Using a Long Short-term Memory Network

Líu

Liu

Wang

et al. 2019

ApJ

117

126

View full text Add to dashboard Cite

show abstract

“…These approaches were used to forecast whether or not a flare will happen. By using the regressor, continuous flare size can be forecasted in Boucheron et al (2015) and Muranushi et al (2015).…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Based Solar Flare Forecasting Model. I. Results for Line-of-sight Magnetograms

Huang

Wang

et al. 2018

ApJ

169

130

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Solar flares originate from the release of the energy stored in the magnetic field of solar active regions, the triggering mechanism for these flares, however, remains unknown. For this reason, the conventional solar flare forecast is essentially based on the statistic relationship between solar flares and measures extracted from observational data. In the current work, the deep learning method is applied to set up the solar flare forecasting model, in which forecasting patterns can be learned from line-of-sight magnetograms of solar active regions. In order to obtain a large amount of observational data to train the forecasting model and test its performance, a data set is created from line-of-sight magnetogarms of active regions observed by SOHO/MDI and SDO/HMI from 1996 April to 2015 October and corresponding soft X-ray solar flares observed by GOES. The testing results of the forecasting model indicate that (1) the forecasting patterns can be automatically reached with the MDI data and they can also be applied to the HMI data; furthermore, these forecasting patterns are robust to the noise in the observational data; (2) the performance of the deep learning forecasting model is not sensitive to the given forecasting periods (6, 12, 24, or 48 hr); (3) the performance of the proposed forecasting model is comparable to that of the state-of-the-art flare forecasting models, even if the duration of the total magnetograms continuously spans 19.5 years. Case analyses demonstrate that the deep learning based solar flare forecasting model pays attention to areas with the magnetic polarity-inversion line or the strong magnetic field in magnetograms of active regions.

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“…In ML-based prediction applications, characteristic "features" of the photospheric magnetic field, sometimes combined with features seen in simultaneous Extreme UltraViolet (EUV) images of the solar corona, are used in a statistical sense to "train" a computational model to predict the probability of an eruption within a given time period (usually 24 hours). One example is a support vector machine (SVM) architecture to perform a binary classification of magnetograms as flaring or non-flaring (Bobra and Couvidat, 2015;Nishizuka et al, 2017;Boucheron et al, 2015;Yang et al, 2013;Yuan et al, 2010). Nishizuka et al (2017) have also applied decision trees and clustering to the same task.…”

Section: Introductionmentioning

confidence: 99%

Leveraging the mathematics of shape for solar magnetic eruption prediction

Deshmukh

Berger²,

Bradley

et al. 2020

J. Space Weather Space Clim.

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Current operational forecasts of solar eruptions are made by human experts using a combination of qualitative shape-based classification systems and historical data about flaring frequencies. In the past decade, there has been a great deal of interest in crafting machine-learning (ML) flareprediction methods to extract underlying patterns from a training set-e.g., a set of solar magnetogram images, each characterized by features derived from the magnetic field and labeled as to whether it was an eruption precursor. These patterns, captured by various methods (neural nets, support vector machines, etc.), can then be used to classify new images. A major challenge with any ML method is the featurization of the data: pre-processing the raw images to extract higherlevel properties, such as characteristics of the magnetic field, that can streamline the training and use of these methods. It is key to choose features that are informative, from the standpoint of the task at hand. To date, the majority of ML-based solar eruption methods have used physics-based magnetic and electric field features such as the total unsigned magnetic flux, the gradients of the fields, the vertical current density, etc. In this paper, we extend the relevant feature set to include characteristics of the magnetic field that are based purely on the geometry and topology of 2D magnetogram images and show that this improves the prediction accuracy of a neural-net based flare-prediction method.

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Prediction of Solar Flare Size and Time-to-Flare Using Support Vector Machine Regression

Cited by 51 publications

References 47 publications

Predicting Solar Flares Using a Long Short-term Memory Network

Predicting Solar Flares Using a Long Short-term Memory Network

Deep Learning Based Solar Flare Forecasting Model. I. Results for Line-of-sight Magnetograms

Leveraging the mathematics of shape for solar magnetic eruption prediction

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