Solar Flare Prediction Using Advanced Feature Extraction, Machine Learning, and Feature Selection

Novel machine-learning and feature-selection algorithms have been developed to study: i) the flare-prediction-capability of magnetic feature (MF) properties generated by the recently developed Solar Monitor Active Region Tracker (SMART); ii) SMART’s MF properties that are most significantly related to flare occurrence. Spatiotemporal association algorithms are developed to associate MFs with flares from April 1996 to December 2010 in order to differentiate flaring and non-flaring MFs and enable the application of machine-learning and feature-selection algorithms. A machine-learning algorithm is applied to the associated datasets to determine the flare-prediction-capability of all 21 SMART MF properties. The prediction performance is assessed using standard forecast-verification measures and compared with the prediction measures of one of the standard technologies for flare-prediction that is also based on machine-learning: Automated Solar Activity Prediction (ASAP). The comparison shows that the combination of SMART MFs with machine-learning has the potential to achieve more accurate flare-prediction than ASAP. Feature-selection algorithms are then applied to determine the MF properties that are most related to flare occurrence. It is found that a reduced set of six MF properties can achieve a similar degree of prediction accuracy as the full set of 21 SMART MF properties.

[1]  Hiroshi Motoda,et al.  Perspectives of Feature Selection , 1998 .

[2]  Haimin Wang,et al.  Statistical Assessment of Photospheric Magnetic Features in Imminent Solar Flare Predictions , 2009 .

[3]  Rami Qahwaji,et al.  Automated Solar Activity Prediction: A hybrid computer platform using machine learning and solar imaging for automated prediction of solar flares , 2009 .

[4]  P. W. Schuck,et al.  WHAT IS THE RELATIONSHIP BETWEEN PHOTOSPHERIC FLOW FIELDS AND SOLAR FLARES? , 2009, 0905.0529.

[5]  K. D. Leka,et al.  Evaluating the Performance of Solar Flare Forecasting Methods , 2008 .

[6]  Stanley S. Ipson,et al.  A new technique for the calculation and 3D visualisation of magnetic complexities on solar satellite images , 2010, The Visual Computer.

[7]  Hiroshi Motoda,et al.  Computational Methods of Feature Selection , 2022 .

[8]  J. P. Mason,et al.  TESTING AUTOMATED SOLAR FLARE FORECASTING WITH 13 YEARS OF MICHELSON DOPPLER IMAGER MAGNETOGRAMS , 2010 .

[9]  Neil Davey,et al.  Time Series Prediction and Neural Networks , 2001, J. Intell. Robotic Syst..

[10]  E. Priest,et al.  Free Magnetic Energy in Solar Active Regions above the Minimum-Energy Relaxed State , 2007, 0805.1619.

[11]  Liyun Zhang,et al.  Correlation Between Solar Flare Productivity and Photospheric Magnetic Field Properties , 2006 .

[12]  Christopher C. Balch,et al.  Updated verification of the Space Weather Prediction Center's solar energetic particle prediction model , 2008 .

[13]  Rami Qahwaji,et al.  Automated Prediction of Solar Flares , 2010 .

[14]  Mark A. Hall,et al.  Correlation-based Feature Selection for Machine Learning , 2003 .

[15]  Flare Frequency-Size Distributions for Individual Active Regions , 2000 .

[16]  Haimin Wang,et al.  The Statistical Relationship between the Photospheric Magnetic Parameters and the Flare Productivity of Active Regions , 2006 .

[17]  Hiroshi Motoda,et al.  Book Review: Computational Methods of Feature Selection , 2007, The IEEE intelligent informatics bulletin.

[18]  Haimin Wang,et al.  Active-Region Monitoring and Flare Forecasting – I. Data Processing and First Results , 2002 .

[19]  Carolus J. Schrijver,et al.  A Characteristic Magnetic Field Pattern Associated with All Major Solar Flares and Its Use in Flare Forecasting , 2007 .

[20]  D. S. Bloomfield,et al.  Solar magnetic feature detection and tracking for space weather monitoring , 2010, 1006.5898.

[21]  Daren Yu,et al.  SHORT-TERM SOLAR FLARE PREDICTION USING MULTIRESOLUTION PREDICTORS , 2010 .

[22]  Chris H. Q. Ding,et al.  Minimum Redundancy Feature Selection from Microarray Gene Expression Data , 2005, J. Bioinform. Comput. Biol..

[23]  M. S. Wheatland A statistical solar flare forecast method , 2005 .

[24]  G. Barnes,et al.  Photospheric Magnetic Field Properties of Flaring versus Flare-quiet Active Regions. IV. A Statistically Significant Sample , 2007 .

[25]  Ron Kohavi,et al.  Feature Selection for Knowledge Discovery and Data Mining , 1998 .

[26]  Xin Huang,et al.  SHORT-TERM SOLAR FLARE LEVEL PREDICTION USING A BAYESIAN NETWORK APPROACH , 2010 .

[27]  Daren Yu,et al.  Short-Term Solar Flare Prediction Using a Sequential Supervised Learning Method , 2009 .

[28]  Tom Fawcett,et al.  An introduction to ROC analysis , 2006, Pattern Recognit. Lett..

[29]  Rami Qahwaji,et al.  Automatic Short-Term Solar Flare Prediction Using Machine Learning and Sunspot Associations , 2007 .

[30]  Mitzi Adams,et al.  THE “MAIN SEQUENCE” OF EXPLOSIVE SOLAR ACTIVE REGIONS: DISCOVERY AND INTERPRETATION , 2009 .

[31]  P. McIntosh The classification of sunspot groups , 1990 .

[32]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[33]  R. Skoug,et al.  Introduction to the special section: Violent Sun‐Earth connection events of October–November 2003 , 2005 .

[34]  S. White,et al.  On the Temporal Relationship between Coronal Mass Ejections and Flares , 2001 .

[35]  Haimin Wang,et al.  Automated flare forecasting using a statistical learning technique , 2010 .

[36]  Mauro Messerotti,et al.  Solar Weather Event Modelling and Prediction , 2009 .