Application and prospect of deep learning in air quality prediction

In recent years, deep learning has demonstrated powerful potential in air pollution prediction due to its outstanding learning ability, generalization capability, and handling of high-dimensional data. This article systematically reviews deep learning models for predicting air pollutant concentrations. Firstly, it examines traditional prediction methods and analyzes their advantages and limitations. Subsequently, it focuses on the development trajectory of deep learning in this field. Furthermore, it delves into models based on attention mechanisms, such as Transformer and Informer, which enhance prediction performance by adaptively focusing on key features. Additionally, the importance of data decomposition in prediction is emphasized. By decomposing complex data, deep learning models can be more effectively utilized to improve prediction accuracy. The article concludes by summarizing the current research achievements and shortcomings and providing outlooks on future research directions. This study aims to inspire relevant researchers and promote further development of deep learning in this field.