Prediction of Annual Snow Accumulation Using a Recurrent Graph
  Convolutional Approach
Benjamin Zalatan Maryam Rahnemoonfar
The precise tracking and prediction of polar ice layers can unveil historic
trends in snow accumulation. In recent years, airborne radar sensors, such as
the Snow Radar, have been shown to be able to measure these internal ice layers
over large areas with a fine vertical resolution. In our previous work, we
found that temporal graph convolutional networks perform reasonably well in
predicting future snow accumulation when given temporal graphs containing deep
ice layer thickness. In this work, we experiment with a graph attention
network-based model and used it to predict more annual snow accumulation data
points with fewer input data points on a larger dataset. We found that these
large changes only very slightly negatively impacted performance.