[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124292-en":3,"doc-seo-124292-105":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},124292,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Robust Edge Machine-Learning For The Real-Time Processing And Prediction of Geomagnetic Anomalies and Geomagnetically Induced Currents (GICs)","Doctoral dissertation on robust edge machine-learning methods for real-time processing and prediction of geomagnetic anomalies and geomagnetically induced currents (GICs). Motivates the need for accurate space-weather-related forecasting and monitoring, contrasts physics-based simulations with data-driven machine learning, and addresses challenges in event modeling and observation using magnetometers and baseline correction. Establishes research objectives and organization of the work, supported by academic committees, funding from NSF EPSCOR, and acknowledgements to collaborators and lab members.","University of New Hampshire  \nUniversity of New Hampshire Scholars Repository  \n\n| Doctoral Dissertations | Graduate and Undergraduate Student Scholarship |\n| --- | --- |\n| Spring 2025\u003Cbr>Robust Edge Machine-Learning For The Real-Time Processing And Prediction of Geomagnetic Anomalies and Geomagnetically Induced Currents (GICs)\u003Cbr>Talha Siddique\u003Cbr>University of New Hampshire\u003Cbr>Follow this and additional works at: [https://scholars.unh.edu/dissertation](https://scholars.unh.edu/dissertation) |  |\n\nRecommended Citation  \nSiddique, Talha, \"Robust Edge Machine-Learning For The Real-Time Processing And Prediction of Geomagnetic Anomalies and Geomagnetically Induced Currents (GICs)\" (2025) . Doctoral Dissertations. 2881.  \n[https://scholars.unh.edu/dissertation/2881](https://scholars.unh.edu/dissertation/2881)  \nThis Dissertation is brought to you for free and open access by the Graduate and Undergraduate Student Scholarship at University of New Hampshire Scholars Repository. It has been accepted for inclusion in Doctoral Dissertations by an authorized administrator of University of New Hampshire Scholars Repository. For more information, [please contact Scholarly.Communication@unh.edu](please contact Scholarly.Communication@unh.edu).  \nRobust Edge Machine-Learning For The Real-Time Processing And Prediction of Geomagnetic Anomalies and Geomagnetically Induced Currents (GICs)  \nBY  \nTALHA SIDDIQUE  \nBS Computer Science, BRAC University, 2016  \nMS Natural Resources: Environmental Economics, University of New Hampshire, 2019  \nPhD Dissertation  \nSubmitted to the University of New Hampshire  \nin Partial Fulfillment of  \nthe Requirements for the Degree of  \nDoctor of Philosophy  \nin  \nElectrical and Computer Engineering  \nMay, 2025  \nThis dissertation will be examined and approved in partial fulfillment of the requirements for the degree of PhD in Electrical and Computer Engineering by:  \nThesis Director, Md Shaad Mahmud, PhD.  \nAssociate Professor  \nDepartment of Electrical and Computer Engineering University of New Hampshire  \nSe Young Yoon, PhD.  \nAssociate Professor  \nDepartment of Electrical and Computer Engineering University of New Hampshire  \nDiliang Chen, PhD.  \nAssistant Professor  \nDepartment of Electrical and Computer Engineering University of New Hampshire  \nDonpeng Xu, PhD.  \nAssociate Professor  \nDepartment of Computer Science  \nUniversity of New Hampshire  \nHyunju K Connor, PhD.  \nResearch Astrophysicist  \nNASA Goddard Space Flight Center  \nOn May, 2025  \nOriginal approval signatures are on file with the University of New Hampshire Graduate  \nSchool.  \nACKNOWLEDGEMENTS  \nI would like to express my heartfelt gratitude to my advisor, Dr. MD Shaad Mahmud, for his exceptional guidance, unwavering support, and insightful mentorship throughout my graduate journey at UNH. His encouragement and patience have played a pivotal role in shaping both my academic and personal growth, and for that, I am deeply thankful. Iam also profoundly grateful to my respected committee members, Dr. Diliang Chen, Dr. Donpeng Xu, Dr. Hyunju K. Connor, and Dr. Se Young Yoon, for their valuable feedback and guidance, which have significantly enriched this research. The knowledge that I have gained under their guidance will undoubtedly benefit me in both my professional and personal life.  \nI extend my sincere appreciation to my colleagues in the Machine-learning Algorithms for GICs in Alaska and New Hampshire (MAGICIAN) team. Their collaborative spirit and intellectual contributions have made this research journey both stimulating and rewarding. I am also deeply thankful to my lab mates, Dr. Sajila Wickramaratne, MD Faishal Yousuf, Blaise O’Mara, and Sabby Clemmons, for their camaraderie, thoughtful discussions, and firm support throughout this process.  \nI gratefully acknowledge the financial support provided by NSF EPSCOR, which was instrumental in making this research possible. This funding laid the foundation for the progress and outcomes achiev","cbCaifhAl6biFkdQ","https://ap.wps.com/l/cbCaifhAl6biFkdQ","pdf",10381048,1,139,"English","en",105,"# Introduction\n## Motivation, Objective, and Research Overview\n## Research Impact\n## Organization of Dissertation\n# Geomagnetic Event Forecasting and Monitoring: Background, Challenges and Research Objective\n## Introduction\n## Overview of Geomagnetism and Geomagnetic Events\n## Approaches to Geomagnetic Event Forecasting: Physics-Based Simulations vs. Machine Learning (ML)-Driven Methods\n## Approaches to Geomagnetic Event Monitoring: Magnetometers and Baseline Correction","[{\"question\":\"What problem does the dissertation address?\",\"answer\":\"It targets real-time prediction of geomagnetic anomalies and geomagnetically induced currents (GICs) using robust edge machine-learning approaches.\"},{\"question\":\"How does the research position machine learning relative to physics-based simulations?\",\"answer\":\"It presents physics-based simulation approaches and compares them with data-driven machine learning (ML)-driven methods for geomagnetic event forecasting.\"},{\"question\":\"What monitoring and data-processing elements are emphasized?\",\"answer\":\"The work discusses geomagnetic monitoring through magnetometers and includes baseline correction as part of handling geomagnetic measurements.\"}]","Robust Edge Machine-Learning For The Real-Time Processing And Prediction of Geomagnetic Anomalies and Geomagnetically Induced Currents (GICs) | 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