Computational deep air quality prediction techniques: a systematic review

Abstract

The escalating population and rapid industrialization have led to a significant rise in environmental pollution, particularly air pollution. This has detrimental effects on both the environment and human health, resulting in increased morbidity and mortality. As a response to this pressing issue, the development of air quality prediction models has emerged as a critical research area. In this systematic literature review, we focused on reviewing 203 potential articles published between 2017 and May 2023 obtained from major databases. Our review specifically targeted keywords such as air quality prediction, air pollution prediction, and air quality classification. The review addressed five key research questions, including the types of deep learning (DL) models employed, the performance metrics considered, the best-performing models based on quantitative analysis, and the existing challenges and future prospects in the field. Additionally, we highlighted the limitations of current air quality prediction models and proposed various future research directions to foster further advancements in this area.