Prediction of Deep Ice Layer Thickness Using Adaptive Recurrent Graph Neural Networks
This work addresses climate change monitoring by improving predictions of polar ice sheet dynamics, though it appears incremental as it builds on prior models.
The paper tackles the problem of predicting historic snow accumulation via deep ice layer thickness using adaptive recurrent graph neural networks, achieving better and more consistent performance than previous and baseline models.
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.