Prediction of Deep Ice Layer Thickness Using Adaptive Recurrent Graph Neural Networks

2023/06/22 by Benjamin Zalatan, Zalatan, Benjamin, Maryam Rahnemoonfar +1 · 2 citations
Earth and Planetary Sciences · #Arctic and Antarctic ice dynamics #Climate change and permafrost #Cryospheric studies and observations #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2306.13690

openalex publication_date 2023/06/22 · openalex created_date 2023/06/28 · openalex updated_date 2026/07/28

Abstract

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.

Cited by

• GRIT-LP: Graph Transformer with Long-Range Skip Connection and Partitioned Spatial Graphs for Accurate Ice Layer Thickness Prediction
• GRIT: Graph Transformer For Internal Ice Layer Thickness Prediction

Related

• Prediction of Annual Snow Accumulation Using a Recurrent Graph Convolutional Approach
• Recurrent Graph Convolutional Networks for Spatiotemporal Prediction of Snow Accumulation Using Airborne Radar
• Learning Spatio-Temporal Patterns of Polar Ice Layers With Physics-Informed Graph Neural Network
• Multi-branch Spatio-Temporal Graph Neural Network For Efficient Ice Layer Thickness Prediction
• GRIT: Graph Transformer For Internal Ice Layer Thickness Prediction