FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3136243271> ?p ?o ?g. }

• W3136243271 endingPage "8" @default.
• W3136243271 startingPage "8" @default.
• W3136243271 abstract "Abstract In this study, we present a visual explanation of a deep learning solar flare forecast model and its relationship to physical parameters of solar active regions (ARs). For this, we use full-disk magnetograms at 00:00 UT from the Solar and Heliospheric Observatory/Michelson Doppler Imager and the Solar Dynamics Observatory/Helioseismic and Magnetic Imager, physical parameters from the Space-weather HMI Active Region Patch (SHARP), and Geostationary Operational Environmental Satellite X-ray flare data. Our deep learning flare forecast model based on the Convolutional Neural Network (CNN) predicts “Yes” or “No” for the daily occurrence of C-, M-, and X-class flares. We interpret the model using two CNN attribution methods (guided backpropagation and Gradient-weighted Class Activation Mapping [Grad-CAM]) that provide quantitative information on explaining the model. We find that our deep learning flare forecasting model is intimately related to AR physical properties that have also been distinguished in previous studies as holding significant predictive ability. Major results of this study are as follows. First, we successfully apply our deep learning models to the forecast of daily solar flare occurrence with TSS = 0.65, without any preprocessing to extract features from data. Second, using the attribution methods, we find that the polarity inversion line is an important feature for the deep learning flare forecasting model. Third, the ARs with high Grad-CAM values produce more flares than those with low Grad-CAM values. Fourth, nine SHARP parameters such as total unsigned vertical current, total unsigned current helicity, total unsigned flux, and total photospheric magnetic free energy density are well correlated with Grad-CAM values." @default.
• W3136243271 created "2021-03-29" @default.
• W3136243271 creator A5025355860 @default.
• W3136243271 creator A5030062485 @default.
• W3136243271 creator A5055180653 @default.
• W3136243271 creator A5060786765 @default.
• W3136243271 creator A5069321579 @default.
• W3136243271 date "2021-03-01" @default.
• W3136243271 modified "2023-10-15" @default.
• W3136243271 title "Visual Explanation of a Deep Learning Solar Flare Forecast Model and Its Relationship to Physical Parameters" @default.
• W3136243271 cites W1541774929 @default.
• W3136243271 cites W1839630305 @default.
• W3136243271 cites W1931514541 @default.
• W3136243271 cites W1959998928 @default.
• W3136243271 cites W1966973332 @default.
• W3136243271 cites W1970929024 @default.
• W3136243271 cites W1990745647 @default.
• W3136243271 cites W1997015642 @default.
• W3136243271 cites W2003328568 @default.
• W3136243271 cites W2014209718 @default.
• W3136243271 cites W2064675550 @default.
• W3136243271 cites W2094483427 @default.
• W3136243271 cites W2110362175 @default.
• W3136243271 cites W2112796928 @default.
• W3136243271 cites W2113534407 @default.
• W3136243271 cites W2114125852 @default.
• W3136243271 cites W2122755404 @default.
• W3136243271 cites W2124410915 @default.
• W3136243271 cites W2127006916 @default.
• W3136243271 cites W2133665775 @default.
• W3136243271 cites W2150734399 @default.
• W3136243271 cites W2158612941 @default.
• W3136243271 cites W2295107390 @default.
• W3136243271 cites W2425671302 @default.
• W3136243271 cites W2509145218 @default.
• W3136243271 cites W2549449189 @default.
• W3136243271 cites W2613030791 @default.
• W3136243271 cites W2765793020 @default.
• W3136243271 cites W2793171700 @default.
• W3136243271 cites W2800761015 @default.
• W3136243271 cites W2888364149 @default.
• W3136243271 cites W2905502273 @default.
• W3136243271 cites W2911964244 @default.
• W3136243271 cites W2919115771 @default.
• W3136243271 cites W2946010671 @default.
• W3136243271 cites W2954809622 @default.
• W3136243271 cites W2962858109 @default.
• W3136243271 cites W2963446712 @default.
• W3136243271 cites W2969487577 @default.
• W3136243271 cites W2982593196 @default.
• W3136243271 cites W3008201674 @default.
• W3136243271 cites W3022810129 @default.
• W3136243271 cites W3033205903 @default.
• W3136243271 cites W3098016256 @default.
• W3136243271 cites W3098166861 @default.
• W3136243271 cites W3100539842 @default.
• W3136243271 cites W3102780218 @default.
• W3136243271 cites W3104481602 @default.
• W3136243271 cites W3121631677 @default.
• W3136243271 cites W3177773949 @default.
• W3136243271 cites W4245192593 @default.
• W3136243271 doi "https://doi.org/10.3847/1538-4357/abdebe" @default.
• W3136243271 hasPublicationYear "2021" @default.
• W3136243271 type Work @default.
• W3136243271 sameAs 3136243271 @default.
• W3136243271 citedByCount "18" @default.
• W3136243271 countsByYear W31362432712022 @default.
• W3136243271 countsByYear W31362432712023 @default.
• W3136243271 crossrefType "journal-article" @default.
• W3136243271 hasAuthorship W3136243271A5025355860 @default.
• W3136243271 hasAuthorship W3136243271A5030062485 @default.
• W3136243271 hasAuthorship W3136243271A5055180653 @default.
• W3136243271 hasAuthorship W3136243271A5060786765 @default.
• W3136243271 hasAuthorship W3136243271A5069321579 @default.
• W3136243271 hasBestOaLocation W31362432711 @default.
• W3136243271 hasConcept C104110773 @default.
• W3136243271 hasConcept C108583219 @default.
• W3136243271 hasConcept C121332964 @default.
• W3136243271 hasConcept C127313418 @default.
• W3136243271 hasConcept C1276947 @default.
• W3136243271 hasConcept C151325931 @default.
• W3136243271 hasConcept C153294291 @default.
• W3136243271 hasConcept C154945302 @default.
• W3136243271 hasConcept C185001636 @default.
• W3136243271 hasConcept C19269812 @default.
• W3136243271 hasConcept C2779588948 @default.
• W3136243271 hasConcept C2779919027 @default.
• W3136243271 hasConcept C41008148 @default.
• W3136243271 hasConcept C44870925 @default.
• W3136243271 hasConcept C50644808 @default.
• W3136243271 hasConcept C62649853 @default.
• W3136243271 hasConceptScore W3136243271C104110773 @default.
• W3136243271 hasConceptScore W3136243271C108583219 @default.
• W3136243271 hasConceptScore W3136243271C121332964 @default.
• W3136243271 hasConceptScore W3136243271C127313418 @default.
• W3136243271 hasConceptScore W3136243271C1276947 @default.
• W3136243271 hasConceptScore W3136243271C151325931 @default.
• W3136243271 hasConceptScore W3136243271C153294291 @default.

• next