J/ApJ/774/L27ivo://CDS.VizieR/J/ApJ/774/L27doi:10.26093/cds/vizier.17749027CDSYang X.Lin G.Zhang H.Mao X.2015-05-02T16:44:40Z2015-04-07T14:49:48ZCDS support team

CDS, Observatoire de Strasbourg, 11 rue de l'Universite, F-67000 Strasbourg, France

cds-question@unistra.frthe-sunastronomical-modelsBased on several magnetic nonpotentiality parameters obtained from the vector photospheric active region magnetograms obtained with the Solar Magnetic Field Telescope at the Huairou Solar Observing Station over two solar cycles, a machine learning model has been constructed to predict the occurrence of flares in the corresponding active region within a certain time window. The Support Vector Classifier, a widely used general classifier, is applied to build and test the prediction models. Several classical verification measures are adopted to assess the quality of the predictions. We investigate different flare levels within various time windows, and thus it is possible to estimate the rough classes and erupting times of flares for particular active regions. Several combinations of predictors have been tested in the experiments. The True Skill Statistics are higher than 0.36 in 97% of cases and the Heidke Skill Scores range from 0.23 to 0.48. The predictors derived from longitudinal magnetic fields do perform well, however, they are less sensitive in predicting large flares. Employing the nonpotentiality predictors from vector fields improves the performance of predicting large flares of magnitude >=M5.0 and >=X1.0.2013ApJ...774L..27Yhttps://cdsarc.cds.unistra.fr/viz-bin/cat/J/ApJ/774/L27CatalogResearchIsServedByTAP VizieR generic servicerelated-toJ/A+A/574/A37 : Solar flares movies (Dalmasse+, 2015)J/ApJ/747/L41 : Solar flares probabilities (Bloomfield+, 2012)J/ApJ/757/94 : Solar flares observed with GOES and AIA (Aschwanden, 2012)J/ApJ/759/69 : Solar electron events (1995-2005) with WIND/3DP (Wang+, 2012)J/A+A/304/563 : Cool X-ray flares of Sun with GOES (Phillips+, 1995)https://cds.unistra.fr/vizier-org/licences_vizier.htmlhttps://vizier.cds.unistra.fr/viz-bin/VizieR-2?-source=J/ApJ/774/L27https://vizier.iucaa.in/viz-bin/VizieR-2?-source=J/ApJ/774/L27http://vizieridia.saao.ac.za/viz-bin/VizieR-2?-source=J/ApJ/774/L27https://vizier.cds.unistra.fr/viz-bin/votable?-source=J/ApJ/774/L27https://vizier.iucaa.in/viz-bin/votable?-source=J/ApJ/774/L27http://vizieridia.saao.ac.za/viz-bin/votable?-source=J/ApJ/774/L27GETtext/xml+votablehttps://tapvizier.cds.unistra.fr/TAPVizieR/tapdefaultJ/ApJ/774/L27/table4Verification results from testing Support Vector Machine (SVM) ClassifierTwin[6/48] Time windowhtime.intervalintrecnoRecord number assigned by the VizieR team. Should Not be used for identification.meta.recordintFlevFlare level (minimal)src.classcharPredPredictor combination (1)meta.id;stat.fitcharGM[0.2/0.6] The Geometric mean (2)src.sample;stat.meanfloatPOD[0.3/0.9] Probability of Detectionstat.fit.goodnessfloate_PODUncertainty in PODstat.errorfloatFOH[0.1/0.8] Frequency of Hitsstat.fit.goodnessfloate_FOHUncertainty in FOHstat.errorfloatFOCN[0.7/1] Frequency of Correct Null forecastsstat.fit.goodnessfloate_FOCNUncertainty in FOCNstat.errorfloatCSI[0.1/0.6] Critical Success Indexstatfloate_CSIUncertainty in CSIstat.errorfloatF1[0.2/0.7] The F_1_ measurestatfloate_F1Uncertainty in F1stat.errorfloatTSS[0.3/0.9] True Skill Statisticstatfloate_TSSUncertainty in TSSstat.errorfloatCSS[0.1/0.5] Clayton Skill Scorestatfloate_CSSUncertainty in CSSstat.errorfloatHSS[0.2/0.5] Heidke Skill Scorestatfloate_HSSUncertainty in HSSstat.errorfloatGSS[0.1/0.4] Gilbert Skill Scorestatfloate_GSSUncertainty in GSSstat.errorfloatACC[0.6/1] Percentage of correct predictionsmeta.notefloate_ACCUncertainty in ACCstat.errorfloatN0/N[0.5/1] Percentage of non-eventsstatfloat