Multigraph Convolutional Networks for Rainfall Estimation in Complex Terrain

Zhicheng Huang; Yagmur Derin; Pierre-Emmanuel Kirstetter; Yifu Li

doi:10.1109/LGRS.2022.3212644

Back

Multigraph Convolutional Networks for Rainfall Estimation in Complex Terrain

Journal article

Peer reviewed

Multigraph Convolutional Networks for Rainfall Estimation in Complex Terrain

Zhicheng Huang, Yagmur Derin, Pierre-Emmanuel Kirstetter and Yifu Li

IEEE geoscience and remote sensing letters, Vol.19, pp.1-5

01/01/2022

DOI: 10.1109/LGRS.2022.3212644

View Online

Abstract

Accurate rainfall estimation over complex terrain is critical for science and applications concerning life and economy, but it is challenging due to the multifactorial relationship between topography, environmental parameters, and rainfall intensity. In this work, a graph convolutional neural (GCN) network-based approach named multiGCN network (M-GCN) is used to interpolate precipitation at a 30-min temporal scale. Furthermore, to enable the model to adapt to the variabilities of spatial correlation, we cluster the ground radar nodes based on their geographical information and expand the network with the multigraph mechanism. Thus, we can avoid overfitting caused by varying conditions over a wide area, and the estimation accuracy can be improved. The method was tested on ground radar-gauge precipitation data over three months on the West Coast of the United States, in 2015. The experimental result confirms that our proposed method outperforms the state-of-the-art interpolation methods. Besides interpolation capacity, the M-GCN also has the advantage of computational efficiency. The distributed graphs in the M-GCN architecture make it possible to train the networks on edge servers and the cloud.

Engineering

Engineering, Electrical & Electronic

Geochemistry & Geophysics

Imaging Science & Photographic Technology

Physical Sciences

Remote Sensing

Science & Technology

Technology

Details

Title: Subtitle: Multigraph Convolutional Networks for Rainfall Estimation in Complex Terrain
Creators: Zhicheng Huang - University of Oklahoma
Yagmur Derin - University of Oklahoma
Pierre-Emmanuel Kirstetter - University of Oklahoma
Yifu Li - University of Oklahoma
Resource Type: Journal article
Publication Details: IEEE geoscience and remote sensing letters, Vol.19, pp.1-5
DOI: 10.1109/LGRS.2022.3212644
ISSN: 1545-598X
eISSN: 1558-0571
Publisher: IEEE
Number of pages: 5
Grant note: NNX16AL23G; 20004656 / NASA Global Precipitation Measurement Ground Validation Program 80NSSC19K0681 / Precipitation Measurement Missions Program
Language: English
Date published: 01/01/2022
Academic Unit: Civil and Environmental Engineering
Record Identifier: 9984945143402771

Metrics

1 Record Views