A review of deep learning methods for mobile source emissions prediction

Zhenyi Xu; Kai Pan; Xiushan Xia; Yang Cao; Yu Kang

doi:10.1117/12.2637028

8 May 2022 A review of deep learning methods for mobile source emissions prediction

Zhenyi Xu, Kai Pan, Xiushan Xia, Yang Cao, Yu Kang

Proceedings Volume 12249, 2nd International Conference on Internet of Things and Smart City (IoTSC 2022）; 122492X (2022) https://doi.org/10.1117/12.2637028
Event: 2022 2nd International Conference on Internet of Things and Smart City (IoTSC 2022）, 2022, Xiamen, China

Abstract

Mobile source pollution has become an important source of air pollution in large and medium cities, and an important cause of fine particulate matter and photochemical smog pollution. There is an urgent need for suitable and effective emissions prediction tools in both scientific research and industry. In recent years, deep learning has outperformed traditional models in many machine learning tasks as the size and dimensionality of data volumes have increased. Many deep neural network models have been successfully applied to solve microscopic and macroscopic emissions modeling. In this paper, we provide a comprehensive review of recent work on mobile source emissions prediction using deep learning methods. Finally, we provide a deeper discussion of the future prospects and ongoing challenges.

Citation Download Citation

Zhenyi Xu, Kai Pan, Xiushan Xia, Yang Cao, and Yu Kang "A review of deep learning methods for mobile source emissions prediction", Proc. SPIE 12249, 2nd International Conference on Internet of Things and Smart City (IoTSC 2022）, 122492X (8 May 2022); https://doi.org/10.1117/12.2637028

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