Evaluating Machine Learning and Statistical Models for Greenland Subglacial Bed Topography

By Homayra Alam1, Jianwu Wang2, Tartela Tabassum2, Katherine Yi3, Angelina Dewar4, Jason Lu5, Ray Chen6, Omar Faruque2, Mathieu Morlighem7, Sikan LI8

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

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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.

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Researchers should cite this work as follows:

  • Homayra Alam; Jianwu Wang; Tartela Tabassum; Katherine Yi; Angelina Dewar; Jason Lu; Ray Chen; Omar Faruque; Mathieu Morlighem; Sikan LI (2024), "Evaluating Machine Learning and Statistical Models for Greenland Subglacial Bed Topography," https://theghub.org/resources/5147.

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