Tiny-RainNet: A Deep CNN-BiLSTM Model for Short-Term Rainfall Prediction
• +2
• changjiang zhang,
• HuiYuan Wang,
• Jing Zeng,
• Leiming Ma,
• Li Guan
changjiang zhang
Zhejiang Normal University

Corresponding Author:zcj74922@zjnu.edu.cn

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HuiYuan Wang
Zhejiang Normal University
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Jing Zeng
Zhejiang Normal University
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Leiming Ma
Shanghai Meteorological Bureau
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Li Guan
Shanghai Meteorological Center, Shanghai, China
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Abstract

A data-driven short-term prediction model called the Tiny-RainNet model is proposed to reduce the cumulative errors caused by multi-step prediction and the complexity of other models. We attempted to improve the accuracy of Doppler radar detection of short-term rainfall prediction using different radar echo maps and numerical model prediction. Rainfall prediction is a complicated temporal- spatial problem. Combined with the convolutional neural network in extracting image context information and the advantages of Bi-directional Long Short-Term Memory (BiLSTM) in processing timing information, 60×10×10 sequential radar echo maps were used as the input of Tiny-RainNet to predict the rainfall in the next 1 to 2 hours.. The proposed Tiny-RainNet, with a root mean square error (RMSE) of 9.67 mm/h, outperformed ConvLSTM, LSTM, FC-LSTM, and AlexNet, whose RMSE is 11.31, 11.50, 14.46, 15.88 mm/h respectively for rainfall prediction.