Solar Flare Intensity Prediction With Machine Learning Models

Jiao, Zhenbang; Sun, Hu; Wang, Xiantong; Manchester, W.; Gombosi, T. I.; Hero, Alfred O.; Chen, Yang

doi:10.1029/2020sw002440

Cited by 36 publications

(34 citation statements)

References 20 publications

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“…Second, we developed an innovative mixed LSTM regression model, which combines binary classification of flaring and LSTM regression for flare intensity, that was used to predict the flare onset jointly with the maximum solar flare intensity observed by the GOES satellites within a 24-hr time window (Jiao et al, 2020). The predicted intensity peaks are shown by light blue curves in Figure 26.…”

Section: Neural Network Predictions Of Solar Flaresmentioning

confidence: 99%

What sustained multi-disciplinary research can achieve: The space weather modeling framework

Gombosi

Chen

Glocer

et al. 2021

J. Space Weather Space Clim.

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MHD-based global space weather models have mostly been developed and maintained at academic institutions. While the ``free spirit'' approach of academia enables the rapid emergence and testing of new ideas and methods, the lack of long-term stability and support makes this arrangement very challenging. This paper describes a successful example of a university-based group, the Center of Space Environment Modeling (CSEM) at the University of Michigan that developed and maintained the Space Weather Modeling Framework (SWMF) and its core element, the BATS-R-US extended MHD code. It took a quarter of a century to develop this capability and reach its present level of maturity that makes it suitable for research use by the space physics community through the Community Coordinated Modeling Center (CCMC) as well as operational use by the NOAA Space Weather Prediction Center (SWPC).

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Section: Neural Network Predictions Of Solar Flaresmentioning

confidence: 99%

What sustained multi-disciplinary research can achieve: The space weather modeling framework

Gombosi

Chen

Glocer

et al. 2021

J. Space Weather Space Clim.

Self Cite

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“…Zhenbang et al developed a mixed LSTM regression model to predict the maximum solar flare intensity within a 24-hr time window 0-24, 6-30, 12-36, and 24-48 hr ahead of time using 6, 12, 24, and 48 hr of data for each Helioseismic and Magnetic Imager (HMI) Active Region Patch (HARP) [27].…”

Section: B Solar Flaresmentioning

confidence: 99%

Using Support Vector Machine (SVM) and Ionospheric Total Electron Content (TEC) Data for Solar Flare Predictions

Asaly

Gottlieb

Reuveni

2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Recently, the application of supervised machine learning methods, especially deep neural networks (DNNs), to solar flare prediction has been a hot topic, and their successful application in research has been reported (Huang et al 2018;Nishizuka et al 2018;Park et al 2018;Chen et al 2019;Domijan et al 2019;Liu et al 2019;Zheng et al 2019;Bhattacharjee et al 2020;Jiao et al 2020;Li et al 2020;Panos & Kleint 2020;Yi et al 2020). However, there is insufficient discussion on how to develop the methods available to real-time operations in space weather forecasting offices, including the methods for validation and verification of the models.…”

Section: Introductionmentioning

confidence: 99%

Operational solar flare prediction model using Deep Flare Net

Nishizuka

Kubo

Sugiura

et al. 2021

Earth Planets Space

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We developed an operational solar flare prediction model using deep neural networks, named Deep Flare Net (DeFN). DeFN can issue probabilistic forecasts of solar flares in two categories, such as ≥ M-class and < M-class events or ≥ C-class and < C-class events, occurring in the next 24 h after observations and the maximum class of flares occurring in the next 24 h. DeFN is set to run every 6 h and has been operated since January 2019. The input database of solar observation images taken by the Solar Dynamic Observatory (SDO) is downloaded from the data archive operated by the Joint Science Operations Center (JSOC) of Stanford University. Active regions are automatically detected from magnetograms, and 79 features are extracted from each region nearly in real time using multiwavelength observation data. Flare labels are attached to the feature database, and then, the database is standardized and input into DeFN for prediction. DeFN was pretrained using the datasets obtained from 2010 to 2015. The model was evaluated with the skill score of the true skill statistics (TSS) and achieved predictions with TSS = 0.80 for ≥ M-class flares and TSS = 0.63 for ≥ C-class flares. For comparison, we evaluated the operationally forecast results from January 2019 to June 2020. We found that operational DeFN forecasts achieved TSS = 0.70 (0.84) for ≥ C-class flares with the probability threshold of 50 (40)%, although there were very few M-class flares during this period and we should continue monitoring the results for a longer time. Here, we adopted a chronological split to divide the database into two for training and testing. The chronological split appears suitable for evaluating operational models. Furthermore, we proposed the use of time-series cross-validation. The procedure achieved TSS = 0.70 for ≥ M-class flares and 0.59 for ≥ C-class flares using the datasets obtained from 2010 to 2017. Finally, we discuss the standard evaluation methods for operational forecasting models, such as the preparation of observation, training, and testing datasets, and the selection of verification metrics.

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Solar Flare Intensity Prediction With Machine Learning Models

Cited by 36 publications

References 20 publications

What sustained multi-disciplinary research can achieve: The space weather modeling framework

What sustained multi-disciplinary research can achieve: The space weather modeling framework

Using Support Vector Machine (SVM) and Ionospheric Total Electron Content (TEC) Data for Solar Flare Predictions

Operational solar flare prediction model using Deep Flare Net

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