Inductive Spatiotemporal Graph Convolutional Networks for Short-Term Quantitative Precipitation Forecasting-Reference-Cited by-同舟云学术

Inductive Spatiotemporal Graph Convolutional Networks for Short-Term Quantitative Precipitation Forecasting

Author:

Wu Yajing¹^ORCID,Yang Xuebing¹^ORCID,Tang Yongqiang¹^ORCID,Zhang Chenyang²^ORCID,Zhang Guoping³,Zhang Wensheng²^ORCID

Affiliation:

1. Research Center of Precision Sensing and Control, Institute of Automation, Chinese Academy of Sciences, Beijing, China

2. State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China

3. Public Meteorological Service Center of CMA, Beijing, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

Reference68 articles.

1. Application of deep convolutional neural networks for detecting extreme weather in climate datasets;liu;arXiv 1605 01156,2016

3. Switching ARIMA model based forecasting for traffic flow;yu;Proc IEEE Int Conf Acoust Speech Signal Process,2004

Cited by 7 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

4. MM-RNN: A Multimodal RNN for Precipitation Nowcasting;IEEE Transactions on Geoscience and Remote Sensing;2023