Accurate precipitation prediction is crucial for a range of sectors, including agriculture, water resource management, and disaster preparedness. Traditional meteorological models often struggle to capture the complex spatial and temporal patterns associated with precipitation events. To address this gap, this study introduces a groundbreaking approach that combines Transformer and Generative Adversarial Network (GAN) technologies. The objective is to downscale low-resolution (25km) precipitation data to a finer resolution (8km) specifically for the Beijing region in China. Our proposed model enhances the accuracy of precipitation forecasts by leveraging a hybrid architecture that combines the strengths of Transformers and Generative Adversarial Networks (GANs). The model is particularly effective in downscaling low-resolution meteorological data to high-resolution precipitation forecasts. Comparative analyses with existing models like CorrectorGAN and ResDeepD indicate a significant improvement in forecast accuracy, validating the efficacy of our novel approach.
This paper proposes a prediction approach based on MLP-Mixer with FFT (The fast Fourier transformation). The wind speed series dataset was transformed using the FFT. Extract high dimensional features initially, then a deep learning time series prediction based on MLP mixer is introduced to explore and exploit the implicit information of wind speed time series for wind speed forecasting. We compared the different wind speed forcasting results by setting the lookback premeter to 4, 8, 12, and 16 hours. On the basis of two years of test dataset, the performance of the proposed FFT-MLP-mixer is effectively validated for short-term wind speed forecasting one hour in advance. The best wind speed prediction results are obtained when the lookback is 4, where the wind speed has an inflection point and the prediction results have slightly later features than the observation data.
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