Recurrent Graph Convolutional Networks for Spatiotemporal Prediction of Snow Accumulation Using Airborne Radar
Recurrent Graph Convolutional Networks for Spatiotemporal Prediction of Snow Accumulation Using Airborne Radar
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DOI：
10.1109/radarconf2351548.2023.10149562
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发表时间：
2023-02
期刊：
影响因子：
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通讯作者：
Benjamin Zalatan;M. Rahnemoonfar
中科院分区：
文献类型：
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作者：
Benjamin Zalatan;M. Rahnemoonfar
随着我们应对气候变化的影响以及全球大气温度的升高，对年积雪量的准确预测和估算变得愈发重要。机载雷达传感器，比如雪雷达，能够大规模测量积雪速率模式，并监测正在发生的气候变化对格陵兰岛降水和径流的影响。雪雷达使用超宽带宽，可实现良好的垂直分辨率，有助于捕捉内部冰层。鉴于利用雷达数据得到的往年积雪量，在本文中，我们提出了一种基于递归图卷积网络的机器学习模型，用于预测某一地点连续近年的积雪量。我们发现，该模型比同等的非几何和非时间模型性能更好，且更具一致性。
The accurate prediction and estimation of annual snow accumulation has grown in importance as we deal with the effects of climate change and the increase of global atmospheric temperatures. Airborne radar sensors, such as the Snow Radar, are able to measure accumulation rate patterns at a large-scale and monitor the effects of ongoing climate change on Greenland's precipitation and run-off. The Snow Radar's use of an ultra-wide bandwidth enables a fine vertical resolution that helps in capturing internal ice layers. Given the amount of snow accumulation in previous years using the radar data, in this paper, we propose a machine learning model based on recurrent graph convolutional networks to predict the snow accumulation in recent consecutive years at a certain location. We found that the model performs better and with more consistency than equivalent nongeometric and nontemporal models.