[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123691-en":3,"doc-seo-123691-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},123691,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Predicting Swarm Equatorial Plasma Bubbles via Machine Learning and Shapley Values - Research Article","AI Prediction of Equatorial Plasma Bubbles (APE) is presented as a machine learning approach to forecast the Ionospheric Bubble Index (IBI) observed by the Swarm spacecraft. The study addresses the challenge posed by strong day-to-day variability of equatorial plasma bubbles (EPBs), which drive plasma-density and magnetic-field perturbations. APE uses 2014–2022, 1 s resolution data mapped into a six-dimensional feature space with EPB R2 as labels. Performance metrics indicate high predictive skill, with strongest results after sunset in American/Atlantic sectors near equinoxes during high solar activity. Shapley value analysis identifies F10.7 as the dominant driver, while feature interactions provide new insight into EPB climatology and inform potential EPB onset forecasting.","RESEARCH ARTICLE  \n10.1029/2022JA031183  \nSpecial Section:  \nMachine Learning in Heliophysics  \nKey Points:  \n• AI Prediction of EPBs (APE) can accurately predict the Swarm Ionospheric Bubble Index  \n• APE is an XGBoost regressor that outperforms similarly trained linear and random forest models  \n• Game theory techniques reveals the influence of solar and geomagnetic activity as well as geo-location, time, and season  \nCorrespondence to:  \nS. A. Reddy,  \n[sachin.reddy.18@ucl.ac.uk](sachin.reddy.18@ucl.ac.uk)  \nCitation:  \nReddy, S. A., Forsyth, C., Aruliah, A., Smith, A., Bortnik, J., Aa, E., et al.(2023). Predicting swarm equatorial plasma bubbles via machine learning and Shapley values. Journal of Geophysical Research: Space Physics, 128, e2022JA031183. [https://doi](https://doi). org/10.1029/2022JA031183  \nReceived 23 NOV 2022 Accepted 16 MAY 2023  \nAuthor Contributions:  \nConceptualization: S. A. Reddy, A. Smith, J. Bortnik  \nFormal analysis: S. A. Reddy, A. Smith, J. Bortnik, E. Aa, G. Lewis  \nFunding acquisition: S. A. Reddy, C. Forsyth  \nInvestigation: S. A. Reddy  \nMethodology: S. A. Reddy, A. Smith, J. Bortnik  \nProject Administration: S. A. Reddy  \nSoftware: S. A. Reddy  \nSupervision: C. Forsyth, A. Aruliah, D.  \nO. Kataria, G. Lewis  \nValidation: S. A. Reddy, E. Aa  \nVisualization: S. A. Reddy  \nWriting – original draft: S. A. Reddy  \n©2023 . The Authors.  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \nPredicting Swarm Equatorial Plasma Bubbles via Machine Learning and Shapley Values  \nS. A. Reddy1 , C. Forsyth1 , A. Aruliah2 , A. Smith3 , J. Bortnik4 , E. Aa5 , D. O. Kataria6, and G. Lewis1  \n1Mullard Space Science Laboratory, University College London, London, UK, 2Department of Physics and Astronomy, University College London, London, UK, 3Department of Mathematics, Physics and Electrical Engineering, Northumbria University, London, UK, 4Department of Atmospheric and Oceanic Sciences, University of California at Los Angeles (UCLA), Los Angeles, CA, USA, 5Haystack Observatory, Massachusetts Institute of Technology, Cambridge, MA, USA, 6Southwest Research Institute, San Antonio, TX, USA  \nAbstract In this study we present AI Prediction of Equatorial Plasma Bubbles (APE), a machine learning model that can accurately predict the Ionospheric Bubble Index (IBI) on the Swarm spacecraft.  \nIBI is a correlation (R2) between perturbations in plasma density and the magnetic field, whose source can be Equatorial Plasma Bubbles (EPBs). EPBs have been studied for a number of years, but their day-to-day variability has made predicting them a considerable challenge. We build an ensemble machine learning model to predict IBI. We use data from 2014 to 2022 at a resolution of 1s, and transform it from a time-series into a 6-dimensional space with a corresponding EPB R2 (0–1) acting as the label. APE performs well across all metrics, exhibiting a skill, association and root mean squared error score of 0.96, 0.98 and 0.08 respectively. The model performs best post-sunset, in the American/Atlantic sector, around the equinoxes, and when solar activity is high. This is promising because EPBs are most likely to occur during these periods. Shapley values reveal that F10.7 is the most important feature in driving the predictions, whereas latitude is the least. The analysis also examines the relationship between the features, which reveals new insights into EPB climatology. Finally, the selection of the features means that APE could be expanded to forecasting EPBs following additional investigations into their onset.  \n1. Introduction  \nIn the post sunset F region of the ionosphere, plumes of low density plasma, known as Equatorial Plasma Bubbles (EPBs) are prone to form. These bubbles were first observed in ionosonde traces, and have subsequently been captured by radar, air glow imag","cbCaib9qObcnugFP","https://ap.wps.com/l/cbCaib9qObcnugFP","pdf",1955800,1,11,"English","en",105,"# Abstract\n# 1. Introduction\n## EPB formation and physical mechanisms\n## Impacts on radio and navigation systems\n## Generalized Rayleigh-Taylor instability","[{\"question\":\"What does the APE model predict for the Swarm spacecraft?\",\"answer\":\"APE predicts the Ionospheric Bubble Index (IBI), which correlates plasma density perturbations with the magnetic field and whose source is equatorial plasma bubbles (EPBs).\"},{\"question\":\"How is the training data prepared for APE?\",\"answer\":\"The model uses Swarm data from 2014 to 2022 at a 1 s resolution and transforms the time series into a six-dimensional feature space, using the corresponding EPB R2 as the label.\"},{\"question\":\"Which factors are most influential according to Shapley value analysis?\",\"answer\":\"Shapley values show F10.7 is the most important feature driving the predictions, while latitude contributes the least.\"}]","Predicting Swarm Equatorial Plasma Bubbles via Machine Learning and Shapley Values - Research Article | PDF",1785818045,28,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"predicting-swarm-equatorial-plasma-bubbles-via-machine-learning-and-shapley-values-research-article","",{"@graph":36,"@context":86},[37,54,69],{"@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/predicting-swarm-equatorial-plasma-bubbles-via-machine-learning-and-shapley-values-research-article/123691/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What does the APE model predict for the Swarm spacecraft?","Question",{"text":76,"@type":77},"APE predicts the Ionospheric Bubble Index (IBI), which correlates plasma density perturbations with the magnetic field and whose source is equatorial plasma bubbles (EPBs).","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is the training data prepared for APE?",{"text":81,"@type":77},"The model uses Swarm data from 2014 to 2022 at a 1 s resolution and transforms the time series into a six-dimensional feature space, using the corresponding EPB R2 as the label.",{"name":83,"@type":74,"acceptedAnswer":84},"Which factors are most influential according to Shapley value analysis?",{"text":85,"@type":77},"Shapley values show F10.7 is the most important feature driving the predictions, while latitude contributes the least.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]