Evaluating Machine Learning and Statistical Models for Greenland Bed Topography

Simple item page
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.accessioned2024-07-12T14:57:07Z
dc.date.available2024-07-12T14:57:07Z
dc.description.abstractThe purpose of this research is to study how different machine learning and statistical models can be used to predict bed topography in Greenland using ice-penetrating radar and satellite imagery data. Accurate bed topography representations are crucial for understanding ice sheet stability, melt, and vulnerability to climate change. We explored nine predictive models including dense neural network, LSTM, variational auto-encoder (VAE), extreme gradient boosting (XGBoost), gaussian process regression, and kriging based residual learning. Model performance was 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 predictive models, different interpolation methods, including Nearest Neighbor interpolation, Bilinear Interpolation, and Universal Kriging were used to obtain estimates the values of ice surface features at the ice bed observation locations. The XGBoost model with Universal Kriging interpolation exhibited strong predictive capabilities but demands extensive resources. Alternatively, the XGBoost model with bilinear interpolation showed robust predictive capabilities and required fewer resources. These models effectively captured the complexity of the Greenland ice sheet terrain with precision and efficiency, making them valuable tools for representing spatial patterns in diverse landscapes.
dc.description.sponsorshipThis work is supported by the grants “REU Site: Online Interdisciplinary Big Data Analytics in Science and Engineering (grant no. OAC–2050943)” and “HDR Institute: HARP- Harnessing Data and Model Revolution in the Polar Regions (grant no. OAC–2118285)” from the National Science Foundation. 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) and the SCREMS program (grant no. DMS– 0821311), with additional substantial support from the University of Maryland, Baltimore County (UMBC). See hpcf.umbc.edu for more information on HPCF and the projects using its resources
dc.description.urihttps://theghub.org/resources/5153
dc.format.extent23 pages
dc.genretechnical reports
dc.genrepreprints
dc.identifierdoi:10.13016/m2idd7-j0dr
dc.identifier.urihttp://hdl.handle.net/11603/34844
dc.language.isoen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Student Collection
dc.relation.ispartofUMBC Information Systems Department
dc.rightsATTRIBUTION-NONCOMMERCIAL-SHAREALIKE 4.0 INTERNATIONAL
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/4.0/deed.en
dc.titleEvaluating Machine Learning and Statistical Models for Greenland Bed Topography
dc.typeText
dcterms.creatorhttps://orcid.org/0009-0006-8650-4366
dcterms.creatorhttps://orcid.org/0000-0002-9933-1170

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
BigDataREU2023Team1.pdf
Size:
842.58 KB
Format:
Adobe Portable Document Format
Download

Collections

UMBC Student Collection
UMBC Information Systems Department