{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T20:47:23Z","timestamp":1788814043203,"version":"build-2803163510"},"reference-count":56,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2021,4,30]],"date-time":"2021-04-30T00:00:00Z","timestamp":1619740800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61872047,61720106007"],"award-info":[{"award-number":["61872047,61720106007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013314","name":"Higher Education Discipline Innovation Project","doi-asserted-by":"publisher","award":["B18008"],"award-info":[{"award-number":["B18008"]}],"id":[{"id":"10.13039\/501100013314","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Funds for Creative Research Groups of China","award":["61921003"],"award-info":[{"award-number":["61921003"]}]},{"DOI":"10.13039\/501100005090","name":"Beijing Nova Program","doi-asserted-by":"publisher","award":["Z201100006820124"],"award-info":[{"award-number":["Z201100006820124"]}],"id":[{"id":"10.13039\/501100005090","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Fast and accurate prediction of the geoeffectiveness of coronal mass ejections (CMEs) and the arrival time of the geoeffective CMEs is urgent, to reduce the harm caused by CMEs. In this paper, we present a new deep learning framework based on time series of satellites\u2019 optical observations that can give both the geoeffectiveness and the arrival time prediction of the CME events. It is the first time combining these two demands in a unified deep learning framework with no requirement of manually feature selection and get results immediately. The only input of the deep learning framework is the time series images from synchronized solar white-light and EUV observations. Our framework first uses the deep residual network embedded with the attention mechanism to extract feature maps for each observation image, then fuses the feature map of each image by the feature map fusion module and determines the geoeffectiveness of CME events. For the geoeffective CME events, we further predict its arrival time by the deep residual regression network based on group convolution. In order to train and evaluate our proposed framework, we collect 2400 partial-\/full-halo CME events and its corresponding images from 1996 to 2018. The F1 score and Accuracy of the geoeffectiveness prediction can reach 0.270% and 75.1%, respectively, and the mean absolute error of the arrival time prediction is only 5.8 h, which are both significantly better than well-known deep learning methods and can be comparable to, or even better than, the best performance of traditional methods.<\/jats:p>","DOI":"10.3390\/rs13091738","type":"journal-article","created":{"date-parts":[[2021,4,30]],"date-time":"2021-04-30T05:10:55Z","timestamp":1619759455000},"page":"1738","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Joint Geoeffectiveness and Arrival Time Prediction of CMEs by a Unified Deep Learning Framework"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5276-4366","authenticated-orcid":false,"given":"Huiyuan","family":"Fu","sequence":"first","affiliation":[{"name":"Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9804-2721","authenticated-orcid":false,"given":"Yuchao","family":"Zheng","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1854-8459","authenticated-orcid":false,"given":"Yudong","family":"Ye","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Lunar and Planetary Sciences, Macau University of Science and Technology, Macao 999078, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xueshang","family":"Feng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Space Weather, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaoxu","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Space Weather, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huadong","family":"Ma","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1002\/swe.20044","article-title":"Probability of occurrence of extreme magnetic storms","volume":"11","author":"Kataoka","year":"2013","journal-title":"Space Weather"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1023\/A:1005160129098","article-title":"Interplanetary origin of geomagnetic storms","volume":"88","author":"Gonzalez","year":"1999","journal-title":"Space Sci. 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