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2019

Precipitation Nowcasting with Star-Bridge Networks

Cao, Yuan, Li, Qiuying, Shan, Hongming et al.

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Precipitation nowcasting, which aims to precisely predict the short-term rainfall intensity of a local region, is gaining increasing attention in the artificial intelligence community.

Existing deep learning-based algorithms use a single network to process various rainfall intensities together, compromising the predictive accuracy.
Therefore, this paper proposes a novel recurrent neural network (RNN) based star-bridge network (StarBriNet) for precipitation nowcasting.
The novelty of this work lies in the following three aspects.

Precipitation Nowcasting with Star-Bridge Networks · Around

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Convolutional lstm network: A machine learning approach for precipitation nowcasting
Xingjian Shi, Zhourong Chen, Hao Wang, Dit Yan Yeung, Waikin Wong, and Wangchun Woo · 2015
Earlier work this paper cites.
Unsupervised learning of video representations using lstms
Nitish Srivastava, Elman Mansimov, and Ruslan Salakhudinov · 2015
Earlier work this paper cites.
Deep learning for precipitation nowcasting: A benchmark and a new model
Xingjian Shi, Zhihan Gao, Leonard Lausen, Hao Wang, Dit-Yan Yeung, Wai-kin Wong, and Wang-chun Woo · 2017
Earlier work this paper cites.
Predrnn: Recurrent neural networks for predictive learning using spatiotemporal lstms
Yunbo Wang, Mingsheng Long, Jianmin Wang, Zhifeng Gao, and S Yu Philip · 2017
Earlier work this paper cites.

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Then

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