{"@context":{"@vocab":"https://cir.nii.ac.jp/schema/1.0/","rdfs":"http://www.w3.org/2000/01/rdf-schema#","dc":"http://purl.org/dc/elements/1.1/","dcterms":"http://purl.org/dc/terms/","foaf":"http://xmlns.com/foaf/0.1/","prism":"http://prismstandard.org/namespaces/basic/2.0/","cinii":"http://ci.nii.ac.jp/ns/1.0/","datacite":"https://schema.datacite.org/meta/kernel-4/","ndl":"http://ndl.go.jp/dcndl/terms/","jpcoar":"https://github.com/JPCOAR/schema/blob/master/2.0/"},"@id":"https://cir.nii.ac.jp/crid/1360848664605345280.json","@type":"Article","productIdentifier":[{"identifier":{"@type":"DOI","@value":"10.3847/1538-4357/aab9a7"}},{"identifier":{"@type":"URI","@value":"http://stacks.iop.org/0004-637X/858/i=2/a=113/pdf"}},{"identifier":{"@type":"URI","@value":"http://stacks.iop.org/0004-637X/858/i=2/a=113?key=crossref.2e5f4be3e5abcc4746baef5ff6b69e7d"}},{"identifier":{"@type":"URI","@value":"https://iopscience.iop.org/article/10.3847/1538-4357/aab9a7"}},{"identifier":{"@type":"URI","@value":"https://iopscience.iop.org/article/10.3847/1538-4357/aab9a7/pdf"}},{"identifier":{"@type":"DOI","@value":"10.48550/arxiv.1805.03421"}}],"resourceType":"学術雑誌論文(journal article)","dc:title":[{"@value":"Deep Flare Net (DeFN) Model for Solar Flare Prediction"}],"description":[{"type":"abstract","notation":[{"@value":"<jats:title>Abstract</jats:title>\n               <jats:p>We developed a solar flare prediction model using a deep neural network (DNN) named Deep Flare Net (DeFN). This model can calculate the probability of flares occurring in the following 24 hr in each active region, which is used to determine the most likely maximum classes of flares via a binary classification (e.g., ≥M class versus <M class or ≥C class versus <C class). From 3 × 10<jats:sup>5</jats:sup> observation images taken during 2010–2015 by the <jats:italic>Solar Dynamic Observatory</jats:italic>, we automatically detected sunspots and calculated 79 features for each region, to which flare occurrence labels of X-, M-, and C-class were attached. We adopted the features used in Nishizuka et al. (2017) and added some features for operational prediction: coronal hot brightening at 131 Å (<jats:italic>T</jats:italic> ≥ 10<jats:sup>7</jats:sup> K) and the X-ray and 131 Å intensity data 1 and 2 hr before an image. For operational evaluation, we divided the database into two for training and testing: the data set in 2010–2014 for training, and the one in 2015 for testing. The DeFN model consists of deep multilayer neural networks formed by adapting skip connections and batch normalizations. To statistically predict flares, the DeFN model was trained to optimize the skill score, i.e., the true skill statistic (TSS). As a result, we succeeded in predicting flares with TSS = 0.80 for ≥M-class flares and TSS = 0.63 for ≥C-class flares. Note that in usual DNN models, the prediction process is a black box. However, in the DeFN model, the features are manually selected, and it is possible to analyze which features are effective for prediction after evaluation.</jats:p>"}]}],"creator":[{"@id":"https://cir.nii.ac.jp/crid/1380290617391275782","@type":"Researcher","foaf:name":[{"@value":"N. Nishizuka"}]},{"@id":"https://cir.nii.ac.jp/crid/1380848664605344900","@type":"Researcher","foaf:name":[{"@value":"K. Sugiura"}]},{"@id":"https://cir.nii.ac.jp/crid/1380848664605344902","@type":"Researcher","foaf:name":[{"@value":"Y. Kubo"}]},{"@id":"https://cir.nii.ac.jp/crid/1380848664605344899","@type":"Researcher","foaf:name":[{"@value":"M. Den"}]},{"@id":"https://cir.nii.ac.jp/crid/1380848664605345027","@type":"Researcher","foaf:name":[{"@value":"M. Ishii"}]}],"publication":{"publicationIdentifier":[{"@type":"PISSN","@value":"0004637X"},{"@type":"EISSN","@value":"15384357"}],"prism:publicationName":[{"@value":"The Astrophysical Journal"}],"dc:publisher":[{"@value":"American Astronomical Society"}],"prism:publicationDate":"2018-05-10","prism:volume":"858","prism:number":"2","prism:startingPage":"113"},"reviewed":"false","dc:rights":["https://iopscience.iop.org/page/copyright","https://iopscience.iop.org/info/page/text-and-data-mining"],"url":[{"@id":"http://stacks.iop.org/0004-637X/858/i=2/a=113/pdf"},{"@id":"http://stacks.iop.org/0004-637X/858/i=2/a=113?key=crossref.2e5f4be3e5abcc4746baef5ff6b69e7d"},{"@id":"https://iopscience.iop.org/article/10.3847/1538-4357/aab9a7"},{"@id":"https://iopscience.iop.org/article/10.3847/1538-4357/aab9a7/pdf"}],"createdAt":"2018-05-14","modifiedAt":"2024-01-10","foaf:topic":[{"@id":"https://cir.nii.ac.jp/all?q=Astrophysics%20-%20Solar%20and%20Stellar%20Astrophysics","dc:title":"Astrophysics - Solar and Stellar Astrophysics"},{"@id":"https://cir.nii.ac.jp/all?q=FOS:%20Physical%20sciences","dc:title":"FOS: Physical sciences"},{"@id":"https://cir.nii.ac.jp/all?q=Solar%20and%20Stellar%20Astrophysics%20(astro-ph.SR)","dc:title":"Solar and Stellar Astrophysics (astro-ph.SR)"}],"project":[{"@id":"https://cir.nii.ac.jp/crid/1040000781860764544","@type":"Project","projectIdentifier":[{"@type":"KAKEN","@value":"15K16074"},{"@type":"JGN","@value":"JP15K16074"},{"@type":"URI","@value":"https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-15K16074/"}],"notation":[{"@language":"ja","@value":"クラウドロボティクス基盤を用いた大規模データからの動作と対話の学習"},{"@language":"en","@value":"Learning Motions and Responses from Large-Scale Data on a Cloud-Robotics Platform"}]},{"@id":"https://cir.nii.ac.jp/crid/1040000781994468992","@type":"Project","projectIdentifier":[{"@type":"KAKEN","@value":"18H04451"},{"@type":"JGN","@value":"JP18H04451"},{"@type":"URI","@value":"https://kaken.nii.ac.jp/grant/KAKENHI-PUBLICLY-18H04451/"}],"notation":[{"@language":"ja","@value":"太陽衛星画像の機械学習による太陽風起因の宇宙嵐予測モデル開発"}]},{"@id":"https://cir.nii.ac.jp/crid/1040282256839167872","@type":"Project","projectIdentifier":[{"@type":"KAKEN","@value":"15K17620"},{"@type":"JGN","@value":"JP15K17620"},{"@type":"URI","@value":"https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-15K17620/"}],"notation":[{"@language":"ja","@value":"太陽ベクトル磁場観測データのリアルタイム解析によるフレア予測モデル開発"},{"@language":"en","@value":"Solar Flare Prediction by Real-time Observation Data Analysis of Solar Vector Magnetograms"}]}],"relatedProduct":[{"@id":"https://cir.nii.ac.jp/crid/1050856893077653760","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["references"],"jpcoar:relatedTitle":[{"@language":"en","@value":"Coalescence of black hole–neutron star binaries"},{"@value":"Solar flares: Magnetohydrodynamic processes"}]},{"@id":"https://cir.nii.ac.jp/crid/1360002217424462976","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"MAGNETIC FIELD STRUCTURES TRIGGERING SOLAR FLARES AND CORONAL MASS EJECTIONS"}]},{"@id":"https://cir.nii.ac.jp/crid/1360002220784969344","@type":"Article","resourceType":"学術雑誌論文(journal article)","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Numerical Simulations of Flare-productive Active Regions: δ-sunspots, Sheared Polarity Inversion Lines, Energy Storage, and Predictions"}]},{"@id":"https://cir.nii.ac.jp/crid/1360011142937650304","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"The Helioseismic and Magnetic Imager (HMI) Investigation for the Solar Dynamics Observatory (SDO)"}]},{"@id":"https://cir.nii.ac.jp/crid/1360011143613459968","@type":"Article","relationType":["references"],"jpcoar:relatedTitle":[{"@value":"Deep Learning Based Solar Flare Forecasting Model. 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