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Evaluating Machine Learning and Statistical Models for Greenland Subglacial Bed Topography

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dc.contributor.authorYi, Katherine
dc.contributor.authorDewar, Angelina
dc.contributor.authorTabassum, Tartela
dc.contributor.authorLu, Jason
dc.contributor.authorChen, Ray
dc.contributor.authorAlam, Homayra
dc.contributor.authorFaruque, Omar
dc.contributor.authorLi, Sikan
dc.contributor.authorMorlighem, Mathieu
dc.contributor.authorWang, Jianwu
dc.date.accessioned2023-11-28T15:49:42Z
dc.date.available2023-11-28T15:49:42Z
dc.date.issued2023-11-05
dc.description22nd International Conference on Machine Learning and Applications; Jacksonville, FL, USA; December 15-17, 2023
dc.description.abstractThe purpose of this research is to study how different machine learning and statistical models can be used to predict bedrock topography under the Greenland ice sheet using ice-penetrating radar and satellite imagery data. Accurate bed topography representations are crucial for understanding ice sheet stability and vulnerability to climate change. We explore nine predictive models including dense neural network, longshort term memory, variational auto-encoder, extreme gradient boosting (XGBoost), gaussian process regression, and kriging based residual learning. Model performance is evaluated with mean absolute error (MAE), root mean squared error (RMSE), coefficient of determination (R²), and terrain ruggedness index (TRI). In addition to testing various models, different interpolation methods, including nearest neighbor, bilinear, and kriging, are also applied in preprocessing. The XGBoost model with kriging interpolation exhibit strong predictive capabilities but demands extensive resources. Alternatively, the XGBoost model with bilinear interpolation shows robust predictive capabilities and requires fewer resources. These models effectively capture the complexity of the terrain hidden under the Greenland ice sheet with precision and efficiency, making them valuable tools for representing spatial patterns in diverse landscapes.
dc.description.sponsorshipThis work is supported by the grants OAC–2050943 and OAC–2118285 from the National Science Foundation.
dc.description.urihttps://hpcf-files.umbc.edu/research/papers/BigDataREU2023Team1_ICMLA.pdf
dc.format.extent8 pages
dc.genreconference papers and proceedings
dc.genrepostprints
dc.identifier.urihttp://hdl.handle.net/11603/30865
dc.language.isoen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department
dc.relation.ispartofUMBC Joint Center for Earth Systems Technology (JCET)
dc.relation.ispartofUMBC Data Science
dc.relation.ispartofUMBC Center for Accelerated Real Time Analysis
dc.relation.ispartofUMBC Center for Real-time Distributed Sensing and Autonomy
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.subjectUMBC High Performance Computing Facility (HPCF)
dc.titleEvaluating Machine Learning and Statistical Models for Greenland Subglacial Bed Topography
dc.typeText
dcterms.creatorhttps://orcid.org/0009-0006-8650-4366
dcterms.creatorhttps://orcid.org/0000-0002-9933-1170

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