Deep learning algorithms for air pollution forecasting: an overview of recent developments

Hailong Shu; Zhen Song; Huichuang Guo; Xi Chen; Zhongdao Yao

doi:10.1117/12.2686356

10 August 2023 Deep learning algorithms for air pollution forecasting: an overview of recent developments

Hailong Shu, Zhen Song, Huichuang Guo, Xi Chen, Zhongdao Yao

Proceedings Volume 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023); 1275918 (2023) https://doi.org/10.1117/12.2686356
Event: 2023 3rd International Conference on Automation Control, Algorithm and Intelligent Bionics (ACAIB 2023), 2023, Xiamen, China

Abstract

Air pollution is a major environmental issue that affects human health and the environment. In recent years, deep learning has been applied to the prediction of air pollution expansion with promising results. This paper provides a comprehensive review of the recent literature on the application of deep learning related algorithms to the prediction of pollution expansion. The paper focuses on the use of deep learning models such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Hybrid models for air pollution forecasting. The literature review covers studies published between 2018 and 2023, and includes articles from various journals with high impact factors. The results of the reviewed studies show that deep learning models have outperformed traditional statistical models in terms of accuracy and robustness for air pollution forecasting. The paper concludes by highlighting the challenges and opportunities for further research in this area.

Citation Download Citation

Hailong Shu, Zhen Song, Huichuang Guo, Xi Chen, and Zhongdao Yao "Deep learning algorithms for air pollution forecasting: an overview of recent developments", Proc. SPIE 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023), 1275918 (10 August 2023); https://doi.org/10.1117/12.2686356

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available