Predicting Ice-bed Topography using Predictive Modeling

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dc.contributor.authorAlam, Homayra
dc.contributor.authorYi, Katherine
dc.contributor.authorDewar, Angelina
dc.contributor.authorTabassum, Tartela
dc.contributor.authorLu, Jason
dc.contributor.authorChen, Ray
dc.contributor.authorFaruque, Omar
dc.contributor.authorLi, Sikan
dc.contributor.authorMorlighem, Mathieu
dc.date.accessioned2024-07-12T14:57:03Z
dc.date.available2024-07-12T14:57:03Z
dc.date.issued2024-05-14
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, long-short 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 (R ² ), 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.sponsorshipWe would like to thank Dr. Jianwu Wang, Homayra Alam and Omar Faruque as well as our collaborators Sikan Li and Mathieu Morlighem for their help throughout the project. We would also like to thank NSF (Big Data REU), UMBC, HPCF, and IHARP
dc.description.urihttps://theghub.org/resources/5149
dc.format.extent10 pages
dc.genrepresentations (communicative events)
dc.identifierdoi:10.13016/m2kghn-gijs
dc.identifier.urihttp://hdl.handle.net/11603/34833
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.titlePredicting Ice-bed Topography using Predictive Modeling
dc.typeText
dcterms.creatorhttps://orcid.org/0009-0006-8650-4366

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