Accelerating Subglacial Bed Topography Prediction in Greenland: A Performance Evaluation of Spark-Optimized Machine Learning Models

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dc.contributor.authorCham, Mostafa
dc.contributor.authorTabassum, Tartela
dc.contributor.authorShakeri, Ehsan
dc.contributor.authorWang, Jianwu
dc.date.accessioned2024-12-11T17:02:04Z
dc.date.available2024-12-11T17:02:04Z
dc.date.issued2024
dc.description.sponsorshipThe authors would like to thank NSF (Big Data REU), UMBC, HPCF, and IHARP.The hardware used in the computational studies is part of the UMBC High-Performance Computing Facility (HPCF). The facility is supported by the U.S. National Science Foundation through the MRI program (grant nos. CNS–0821258, CNS–1228778, OAC–1726023, and CNS–1920079).
dc.description.urihttps://theghub.org/resources/5269/download/FastML-Poster-MCham-115.pdf
dc.format.extent1 page
dc.genreposters
dc.identifierdoi:10.13016/m2b8gz-2m4b
dc.identifier.urihttp://hdl.handle.net/11603/37023
dc.language.isoen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Joint Center for Earth Systems Technology (JCET)
dc.relation.ispartofUMBC Center for Real-time Distributed Sensing and Autonomy
dc.relation.ispartofUMBC GESTAR II
dc.relation.ispartofUMBC Center for Accelerated Real Time Analysis
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Information Systems Department
dc.relation.ispartofUMBC Mathematics and Statistics Department
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.subjectExtreme Gradient Boosting (XGB)
dc.subjectSubglacial Bed Topography
dc.subjectUMBC Big Data Analytics Lab
dc.subjectApache Spark
dc.subjectDistributed Computing
dc.titleAccelerating Subglacial Bed Topography Prediction in Greenland: A Performance Evaluation of Spark-Optimized Machine Learning Models
dc.typeText
dcterms.creatorhttps://orcid.org/0000-0002-9933-1170

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UMBC Computer Science and Electrical Engineering Department
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UMBC Center for Real-time Distributed Sensing and Autonomy
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UMBC Information Systems Department
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