![]() Thus, the VAE-GAN is a cost-effective tool for large data-driven predictions, which can potentially reinforce air pollution prediction efforts in providing risk assessment and management in a timely manner. Results show that the VAE-GAN achieves a reasonable accuracy in the prediction of both the spatial and temporal evolution patterns of hourly and daily ozone fields, as compared to the Nested Air Quality Prediction Modelling System (commonly used in China), the reanalysis data and observations during the validation period. With the use of VAE, large dataset sizes are decreased by three orders of magnitude, enabling hourly and daily forecasts to be computed in seconds. The training datasets from 20 and validation datasets from 2018 to 2019 are the collection of data from the air quality reanalysis datasets. The performance of VAE-GAN is demonstrated in hourly and daily spatio-temporal ozone forecasts over China. The VAE-GAN model can not only decipher the complex nonlinear relationship between the inputs (the past states/ozone and meteorological factors) and outputs (ozone), but also provide ozone forecasts for a long lead-time beyond the training period. In this work, we introduce a hybrid model (VAE-GAN) combining a generative adversarial network (GAN) with a variational autoencoder (VAE) to learn the dynamic ozone distributions in spatial and temporal spaces. Traditional numerical air quality models require a high computational cost associated with running large-scale numerical simulations. Efficient and accurate real-time forecasting of national spatial ozone distribution is critical to the provision of effective early warning.
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