Flare Prediction Using Photospheric and Coronal Image Data

@article{Jonas2017FlarePU,
  title={Flare Prediction Using Photospheric and Coronal Image Data},
  author={Eric Jonas and Monica G. Bobra and Vaishaal Shankar and J. Todd Hoeksema and Benjamin Recht},
  journal={Solar Physics},
  year={2017},
  volume={293},
  pages={1-22}
}
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References

SHOWING 1-10 OF 47 REFERENCES
Statistical Assessment of Photospheric Magnetic Features in Imminent Solar Flare Predictions
In this study we use the ordinal logistic regression method to establish a prediction model, which estimates the probability for each solar active region to produce X-, M-, or C-class flares during
Photospheric Magnetic Field Properties of Flaring versus Flare-quiet Active Regions. II. Discriminant Analysis
We apply statistical tests based on discriminant analysis to the wide range of photospheric magnetic parameters described in a companion paper by Leka & Barnes, with the goal of identifying those
The Statistical Relationship between the Photospheric Magnetic Parameters and the Flare Productivity of Active Regions
Using line-of-sight Michelson Doppler Imager (MDI) magnetograms of 89 active regions and Solar Geophysical Data (SGD) flare reports, we explored, for the first time, the magnitude scaling
PHOTOSPHERIC MAGNETIC FIELD PROPERTIES OF FLARING VERSUS FLARE-QUIET ACTIVE REGIONS. IV. A STATISTICALLY SIGNIFICANT SAMPLE
Statistical tests based on linear discriminant analysis are applied to numerous photospheric magnetic parameters, continuing toward the goal of identifying properties important for the production of
TESTING AUTOMATED SOLAR FLARE FORECASTING WITH 13 YEARS OF MICHELSON DOPPLER IMAGER MAGNETOGRAMS
Flare occurrence is statistically associated with changes in several characteristics of the line-of-sight magnetic field in solar active regions (ARs). We calculated magnetic measures throughout the
Prediction of Solar Flare Size and Time-to-Flare Using Support Vector Machine Regression
We study the prediction of solar flare size and time-to-flare using 38 features describing magnetic complexity of the photospheric magnetic field. This work uses support vector regression to
Solar Flare Prediction Model with Three Machine-learning Algorithms using Ultraviolet Brightening and Vector Magnetograms
TLDR
A flare prediction model using machine learning, which is optimized to predict the maximum class of flares occurring in the following 24 h, was developed and it was found that k-NN has the highest performance among the three algorithms.
EVALUATING THE PERFORMANCE OF SOLAR FLARE FORECASTING METHODS
The number of published approaches to solar flare forecasting using photospheric magnetic field observations has proliferated recently, with widely varying claims about how well each works. As
Solar Flare Prediction Using SDO/HMI Vector Magnetic Field Data with a Machine-Learning Algorithm
We attempt to forecast M-and X-class solar flares using a machine-learning algorithm, called Support Vector Machine (SVM), and four years of data from the Solar Dynamics Observatory's Helioseismic
A Bayesian Approach to Solar Flare Prediction
A number of methods of flare prediction rely on classification of physical characteristics of an active region, in particular optical classification of sunspots, and historical rates of flaring for a
...
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