[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118005-en":3,"doc-seo-118005-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118005,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","CME Arrival Modeling with Machine Learning","Space weather phenomena increasingly affect Earth’s technological infrastructure, with coronal mass ejections (CMEs) recognized as key drivers. Developing fast, accurate arrival information is therefore essential for assessing when CMEs reach near-Earth space. Building on the CAT-PUMA tool proposed by Liu et al., this study expands the framework using supervised learning for both regression and classification to predict CME transit time and determine Earth-impact likelihood using CME features and solar-wind parameters measured at take-off. Model interpretation with Shap values clarifies limitations and practical applicability. ","This is a repository copy of CME arrival modeling with machine learning.  \nWhite Rose Research Online URL for this paper:  \n[https://eprints.whiterose.ac.uk/210137/](https://eprints.whiterose.ac.uk/210137/)  \nVersion: Published Version  \nArticle:  \nChierichini, [S. orcid.org/0009-0005-6746-2917](S. orcid.org/0009-0005-6746-2917) , Liu 刘, J 刘 [orcid.org/0000-0003-2569-1840](orcid.org/0000-0003-2569-1840) ,  \nKorsós, [M.B. orcid.org/0000-0002-0049-4798](M.B. orcid.org/0000-0002-0049-4798) et al. (2 more authors) (2024) CME arrival modeling with machine learning. The Astrophysical Journal, 963 (2) . 121. ISSN 0004-637X  \n[https://doi.org/10.3847/1538-4357/ad1cee](https://doi.org/10.3847/1538-4357/ad1cee)  \nReuse  \nThis article is distributed under the terms of the Creative Commons Attribution (CC BY) licence. This licence allows you to distribute, remix, tweak, and build upon the work, even commercially, as long as you credit the authors for the original work. More information and the full terms of the licence here: [https://creativecommons.org/licenses/](https://creativecommons.org/licenses/)  \nTakedown  \nIf you consider content in White Rose Research Online to be in breach of UK law, please notify us by  \nemailing [eprints@whiterose.ac.uk](eprints@whiterose.ac.uk) including the URL of the record and the reason for the withdrawal request.  \n[eprints@whiterose.ac.uk](eprints@whiterose.ac.uk)[ ](eprints@whiterose.ac.uk)[https://eprints.whiterose.ac.uk/](https://eprints.whiterose.ac.uk/)  \nThe Astrophysical Journal, 963:121 (12pp), 2024 March 10 © 2024 . The Author(s) . Published by the American Astronomical Society.  \n[https:](https://doi.org/10.3847/1538-4357/ad1cee)[//](https://doi.org/10.3847/1538-4357/ad1cee)[doi.org](https://doi.org/10.3847/1538-4357/ad1cee)[/](https://doi.org/10.3847/1538-4357/ad1cee)[10.3847](https://doi.org/10.3847/1538-4357/ad1cee)[/](https://doi.org/10.3847/1538-4357/ad1cee)[1538-4357](https://doi.org/10.3847/1538-4357/ad1cee)[/](https://doi.org/10.3847/1538-4357/ad1cee)[ad1cee](https://doi.org/10.3847/1538-4357/ad1cee)  \nCME Arrival Modeling with Machine Learning  \nSimone Chierichini 1,2 , Jiajia Liu (刘佳佳)3,4 , Marianna B. Korsós5,6,7 , Dario Del Moro2 , and Robertus Erdélyi 1,6,7  1 Solar Physics and Space Plasma Research Centre (SP2RC), School of Mathematics & Statistics, The University of Sheffield Sheffield S3 7RH, UK  \n2 Department of Physics, University of Rome “Tor Vergata,” Rome, Italy  \n3 De4epCASSpaceKeyExLpabloration Loratory ob/SGeochspoaocofEEanvirrhnamdntS/pace SCAS CennteesfoUrnEiecliy oncefiScnieCncomepaanradtiTveechPlotgolyoogyf/ChMnang,cHefheneigN, 3ti0n6l,PGeeoopphleyʼssicRalepubObsc of Chrvatory,ina  \n7  \n5  \nUniversity of Science and Technology of China, Hefei, 230026, Peopleʼs Republic of China  \nDipartimento di Fisica e Astronomia “Ettore Majorana,” Università di Catania, Via S. Sofia 78, 6 Department of Astronomy, Eötvös Loránd University, Pázmány Péter sétány 1/A, H-1112  \nI-95123 Catania, Italy Budapest, Hungary  \nGyula Bay Zoltán Solar Observatory (GSO), Hungarian Solar Physics Foundation (HSPF), Petőfi tér 3, H-5700 Gyula, Hungary Received 2023 November 3; accepted 2024 January 7; published 2024 March 6  \nAbstract  \nSpace weather phenomena have long captured the attention of the scientific community, and along with recent technological developments, the awareness that such phenomena can interfere with human activities on Earth has grown considerably. Coronal mass ejections (CMEs) are among the main drivers of space weather. Therefore, developing tools to provide information on their arrival at Earth's nearby space has become increasingly important. Liu et al. developed a tool, called CME Arrival Time Prediction Using Machine Learning Algorithms (CATPUMA), to obtain fast and accurate predictions of CME transit time. This present work aims at the expansion of the CAT-PUMA concept, employing supervised learning to obtain vital information about the arrival of CMEs at Earth. In this s","cbCaicTvUF8VOgYj","https://ap.wps.com/l/cbCaicTvUF8VOgYj","pdf",564587,1,13,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What problem does this paper address in CME research?\",\"answer\":\"It addresses the need for fast, accurate information about when CMEs arrive near Earth and whether they will impact Earth, which is important for space-weather risk assessment.\"},{\"question\":\"How does the study extend the CAT-PUMA concept?\",\"answer\":\"It expands the CAT-PUMA framework by applying supervised learning, including both supervised regression and supervised classification models.\"},{\"question\":\"What approach is used to interpret model limitations?\",\"answer\":\"The study uses model interpretation techniques, specifically Shap values, to quantify insights into limitations affecting the machine-learning models.\"}]","CME Arrival Modeling with Machine Learning | PDF",1785680718,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"cme-arrival-modeling-with-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/cme-arrival-modeling-with-machine-learning/118005/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does this paper address in CME research?","Question",{"text":75,"@type":76},"It addresses the need for fast, accurate information about when CMEs arrive near Earth and whether they will impact Earth, which is important for space-weather risk assessment.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study extend the CAT-PUMA concept?",{"text":80,"@type":76},"It expands the CAT-PUMA framework by applying supervised learning, including both supervised regression and supervised classification models.",{"name":82,"@type":73,"acceptedAnswer":83},"What approach is used to interpret model limitations?",{"text":84,"@type":76},"The study uses model interpretation techniques, specifically Shap values, to quantify insights into limitations affecting the machine-learning models.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]