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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Actually, this modification is suggested in the appendix of the original paper [6].
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Acknowledgment
This research was supported by JSPS KAKENHI Grant Numbers 15H01704, 18H03243.
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Sato, R., Kashima, H., Yamamoto, T. (2018). Short-Term Precipitation Prediction with Skip-Connected PredNet. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11141. Springer, Cham. https://doi.org/10.1007/978-3-030-01424-7_37
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