Recurrent Graph Convolutional Networks for Spatiotemporal Prediction of Snow Accumulation Using Airborne Radar-Reference-Cited by-同舟云学术

Recurrent Graph Convolutional Networks for Spatiotemporal Prediction of Snow Accumulation Using Airborne Radar

Published:2023-05-01 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE Radar Conference (RadarConf23)
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Author:

Zalatan Benjamin¹,Rahnemoonfar Maryam²

Affiliation:

1. Lehigh University,Department of Computer Science and Engineering,Bethlehem,PA,USA

2. Lehigh University,Department of Computer Science and Engineering Department of Civil and Environmental Engineering,Bethlehem,PA,USA

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10149457/10149458/10149562.pdf?arnumber=10149562

Reference32 articles.

1. Spatio-temporal graph con-volutional networks: A deep learning framework for traffic forecasting;bing;Proceedings of the 27th International Joint Conference on Artificial Intelligence,0

2. Multistream graph attention networks for wind speed forecasting;dogan;CoRR,2021

3. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting;shengnan;Proceedings of the AAAI Conference on Artificial Intelligence,0

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3. Prediction of Annual Snow Accumulation Using a Recurrent Graph Convolutional Approach;IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium;2023-07-16