Abstract
With the Earth’s temperature rising and abnormal weather events becoming frequent, the mechanisms of precipitation formation have become increasingly complex, leading to more significant spatiotemporal variability. This increased variability often results in severe flooding events. Despite extensive research on deep learning methods for rainfall prediction, challenges such as forecasting uncertainty and inaccurate predictions persist. Therefore, this study addresses these issues by proposing temporal U2Net (TU2Net) within the GAN framework. TU2Net comprises a nested UNet with two layers designed to handle time series data. The model also connects multiple Conv-GRU decoding modules to enhance spatiotemporal performance and improve prediction quality using an improved loss function. Experimental results demonstrate that TU2Net with the improved loss function outperforms DGMR and UNet in terms of prediction accuracy. Our project is open source and available on GitHub at https://github.com/clearlyzerolxd/TU2Net.
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