Temporal convolutional denoising autoencoder network for air pollution prediction with missing values

Abstract

In recent years, people are paying more attention to improve air quality levels to mitigate its negative impact on human health. So, effective air pollution control has become one of the hottest environmental issues. Accurate pollution prediction plays a vital role in air pollution control. However, air quality modeling faces challenges like long-term pollutant variations due to meteorological variables impact and missing values due to natural disaster or sensor shutdown. These issues make air quality models more complex and challenging to introspect. So, we developed Temporal Convolutional Denoising Autoencoder (TCDA) network, a hybrid PM2.5 prediction framework that can perform rapid extraction of complex dataset's features, handle missing values and improve PM2.5 prediction results. This research paper includes Temporal Convolutional Network (TCN) and Denoising Autoencoder (DAE) network to handle nonlinear multivariate massive datasets. The former utilizes parallel feature processing of Convolutional Neural Network (CNN) and temporal component modeling ability of Recurrent Neural Network (RNN), which helps for features extraction from complex dataset. The latter is to reconstruct the error to fine-tune the prediction results and handle missing values. The proposed model's prediction capability is evaluated by comparing its performance with other baseline models, which shows its performance superiority over other models.