Predicting Multivariate Air Pollution: A Gaussian-Mixture Nested Factorial Variational Autoencoder Approach

In recent years, global concern for human health has escalated due to the persistent threat of air pollution, resulting in a surge of chronic diseases and premature mortality. Poor air quality not only has adverse effects on human health but also poses negative impacts on vegetation, society, and the economy. Hence, it is imperative to invest more effort in accurately predicting multivariate air pollutants to offer practical and relevant solutions. However, many machine learning (ML) and deep learning (DL) models face significant challenges when dealing with the complexities of multivariate air pollution dynamics and the ill-posed nature of the data. In this letter, we propose a Gaussian-mixture nested factorial variational autoencoder (NF-VAE), specifically designed for multivariate air pollution prediction. To assess the performance of the proposed framework, we conducted experimental validation using air pollution data from six monitoring sites in Chinese cities. Three statistical indicators have been used to evaluate forecasting accuracy. The experimental results demonstrate the satisfactory performance of the NF-VAE model in predicting six pollutants for six different sites. Furthermore, the results indicate that the proposed NF-VAE model can effectively enhance efficiency gains, demonstrating improvements of at least 31% for RMSE, 22% for MAE, and 13% for
$R^{2}$
compared with popular DL models, namely, long short-term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM (BiLSTM), and bidirectional GRU (BiGRU).