By Homayra Alam1, Katherine Yi2, Angelina Dewar3, Tartela Tabassum4, Jason Lu5, Ray Chen6, Omar Faruque4, Sikan Li7, Mathieu Morlighem8

1. University of Maryland ,Baltimore County 2. Purdue University, West Lafayette 3. University of Oregon 4. University of Maryland, Baltimore County 5. University of Maryland, College Park 6. Marriotts Ridge High School 7. Texas Advanced Computing Center 8. Dartmouth College

Download (PPTX)

Licensed according to this deed.

Published on



The 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.

Cite this work

Researchers should cite this work as follows:

  • Homayra Alam; Katherine Yi; Angelina Dewar; Tartela Tabassum; Jason Lu; Ray Chen; Omar Faruque; Sikan Li; Mathieu Morlighem (2024), "REU_Final_Presentation,"

    BibTex | EndNote