Abstract:

The 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.

Researchers should cite this work as follows:

• Katherine Yi; Angelina Dewar; Tartela Tabassum; Jason Lu; Ray Chen; Homayra Alam; Omar Faruque; Sikan Li; Mathieu Morlighem; Jianwu Wang (2024), "Evaluating Machine Learning and Statistical Models for Greenland Bed Topography," https://theghub.org/resources/5153.

  BibTex | EndNote

1. Ghub
2. Greenland