# Prediction of Annual Snow Accumulation Using a Recurrent Graph Convolutional Approach

arXiv: https://arxiv.org/abs/2306.13181 · HTML: https://mlsanity.com/paper/2306.13181/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 22 Jun 2023.

## Builds on (most cited first, 5 of 5)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [Deep Ice Layer Tracking and Thickness Estimation using Fully Convolutional Networks](https://mlsanity.com/paper/2009.00191/) (2020)
- [Recurrent Graph Convolutional Networks for Spatiotemporal Prediction of Snow Accumulation Using Airborne Radar](https://mlsanity.com/paper/2302.00817/) (2023)

## Built on by (most cited first, 8 of 8)

- [Learning Spatio-Temporal Patterns of Polar Ice Layers With Physics-Informed Graph Neural Network](https://mlsanity.com/paper/2406.15299/) (2024)
- [Multi-branch Spatio-Temporal Graph Neural Network For Efficient Ice Layer Thickness Prediction](https://mlsanity.com/paper/2411.04055/) (2024)
- [GRIT: Graph Transformer For Internal Ice Layer Thickness Prediction](https://mlsanity.com/paper/2507.07388/) (2025)
- [K-STEMIT: Knowledge-Informed Spatio-Temporal Efficient Multi-Branch Graph Neural Network for Subsurface Stratigraphy Thickness Estimation from Radar Data](https://mlsanity.com/paper/2604.09922/) (2026)
- [GRIT-LP: Graph Transformer with Long-Range Skip Connection and Partitioned Spatial Graphs for Accurate Ice Layer Thickness Prediction](https://mlsanity.com/paper/2511.18716/) (2025)
- [PACT: Peak-Aware Cross-Attention Graph Transformers for Efficient Storm-Surge Emulation](https://mlsanity.com/paper/2605.09036/) (2026)
- [Physics-Informed Machine Learning On Polar Ice: A Survey](https://mlsanity.com/paper/2404.19536/) (2024)
- [Physics-Conditioned Synthesis of Internal Ice-Layer Thickness for Incomplete Layer Traces](https://mlsanity.com/paper/2604.20783/) (2026)

## Abstract (arXiv)

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.