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2018 | OriginalPaper | Chapter

Short-Term Precipitation Prediction with Skip-Connected PredNet

Authors : Ryoma Sato, Hisashi Kashima, Takehiro Yamamoto

Published in: Artificial Neural Networks and Machine Learning – ICANN 2018

Publisher: Springer International Publishing

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Abstract

Short-term forecasting of rainfall in a local area is called precipitation nowcasting, and it has been traditionally addressed using rule-based or numerical approaches. Recently, deep neural network models have started to be used for precipitation nowcasting; however, their utility has not been extensively explored yet. Especially, the existing efforts focus only on the choice of their building blocks and pay little attention to the design of the whole network structure. In this paper, we propose a new precipitation nowcasting model based on the PredNet network architecture, which was originally proposed for short-term video prediction tasks. The proposed model outperforms the state-of-the-art models in the MovingMNIST++ dataset in terms of MSE, and it also shows a good predictive performance on a real dataset of precipitation in Kyoto City.

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previous chapter Research on Fight the Landlords’ Single Card Guessing Based on Deep Learning
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Footnotes
1
Actually, this modification is suggested in the appendix of the original paper [6].
 
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Metadata
Title
Short-Term Precipitation Prediction with Skip-Connected PredNet
Authors
Ryoma Sato
Hisashi Kashima
Takehiro Yamamoto
Copyright Year
2018
Book
Artificial Neural Networks and Machine Learning – ICANN 2018

Print ISBN: 978-3-030-01423-0

Electronic ISBN: 978-3-030-01424-7

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-01424-7_37

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