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- W4285357644 abstract "The accuracy and resolution of the short-term precipitation nowcasting have a profound impact on people's life and production. Taking advantage of high resolution and sensitivity, radar echo images has been prioritized data used in weather nowcasting. Regarding the prediction models, deep learning approaches are becoming more and more attractive because of their success in many other fields. In this paper, we proposed yet another novel deep learning model, called SelfAttUNet, based on UNet network architecture. The prefix ‘SelfAtt-’ comes from the fact that we introduce a self-attention mechanism into the model so that it can focus on the most influential areas of an image to track changes. Extensive empirical evaluation is conducted on a real-life dataset using precipitation maps from the region of the Netherlands. The experimental results show that our model can get a bit higher accuracy than several other state-of-the-art models with the same model size settings." @default.
- W4285357644 created "2022-07-14" @default.
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- W4285357644 date "2021-10-01" @default.
- W4285357644 modified "2023-10-16" @default.
- W4285357644 title "Self-attention UNet Model for Radar Based Precipitation Nowcasting" @default.
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- W4285357644 doi "https://doi.org/10.1109/dsc53577.2021.00079" @default.
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