Prediction of Deep Ice Layer Thickness Using Adaptive Recurrent Graph Neural Networks
Prediction of Deep Ice Layer Thickness Using Adaptive Recurrent Graph Neural Networks
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DOI：
10.1109/icip49359.2023.10222391
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发表时间：
2023-06
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和全球大气温度上升的影响，对极地冰盖内冰层的准确跟踪和预测变得越来越重要。研究这些冰层可以揭示气候趋势，降雪量如何随时间变化，以及未来气候和降水的轨迹。在本文中，我们提出了一种机器学习模型，该模型使用自适应的递归图卷积网络，当给定近年来通过机载雷达数据收集的积雪量时，通过深层冰层的厚度预测历史积雪量。我们发现，我们的模型表现得更好，比我们以前的模型以及等效的非时间，非几何和非自适应模型具有更大的一致性。
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.