Deep Precipitation Nowcasting With Dual Regions Displacement Information and Global Spatiotemporal Representations Learning-Reference-Cited by-同舟云学术

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Deep Precipitation Nowcasting With Dual Regions Displacement Information and Global Spatiotemporal Representations Learning

Published:2024 Issue: Volume:21 Page:1-5
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

Pan Zefeng¹^ORCID,Ji Fanfan¹^ORCID,Zhou Yunlong¹^ORCID,Hang Renlong¹^ORCID,Liu Qingshan²^ORCID,Yuan Xiao-Tong³^ORCID

Affiliation:

1. School of Computer Science, Nanjing University of Information Science and Technology, Nanjing, China

2. School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China

3. State Key Laboratory of Novel Software Technology, Nanjing University, Nanjing, China

Funder

National Key Research and Development Program of China

Natural Science Foundation of China

Postgraduate Research and Practice Innovation Program of Jiangsu Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/8859/10365397/10489979.pdf?arnumber=10489979

Reference11 articles.

1. Convolutional LSTM network: A machine learning approach for precipitation nowcasting;Shi

2. Deep learning for precipitation nowcasting: A benchmark and a new model;Shi

3. PredRNN: A Recurrent Neural Network for Spatiotemporal Predictive Learning

4. PFST-LSTM: A SpatioTemporal LSTM Model With Pseudoflow Prediction for Precipitation Nowcasting

5. MotionRNN: A Flexible Model for Video Prediction with Spacetime-Varying Motions

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