Prediction of Deep Ice Layer Thickness Using Adaptive Recurrent Graph
  Neural Networks
Benjamin Zalatan Maryam Rahnemoonfar
As we deal with the effects of climate change and the increase of global
atmospheric temperatures, the accurate tracking and prediction of ice layers
within polar ice sheets grows in importance. Studying these ice layers reveals
climate trends, how snowfall has changed over time, and the trajectory of
future climate and precipitation. In this paper, we propose a machine learning
model that uses adaptive, recurrent graph convolutional networks to, when given
the amount of snow accumulation in recent years gathered through airborne radar
data, predict historic snow accumulation by way of the thickness of deep ice
layers. We found that our model performs better and with greater consistency
than our previous model as well as equivalent non-temporal, non-geometric, and
non-adaptive models.