Air quality prediction model based on genetic algorithm and weighted extreme learning machine

Yang Xue; Qifei Wu; Haoying Sun

doi:10.1117/12.2641287

19 October 2022 Air quality prediction model based on genetic algorithm and weighted extreme learning machine

Yang Xue, Qifei Wu, Haoying Sun

Proceedings Volume 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering; 1229454 (2022) https://doi.org/10.1117/12.2641287
Event: 7th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2022), 2022, Xishuangbanna, China

Abstract

Air quality forecasting is closely related to people's daily life. At present, the WRF-CMAQ simulation system is commonly used to forecast air quality. However, due to the uncertainty of the simulated weather field and the emission inventory, the results of the WRF-CMAQ forecast model are not ideal. In order to solve the above problems, this paper proposes an air quality prediction model based on Genetic Algorithm (GA) to optimize the parameters of Weighted Extreme Learning Machine (WELM). The experimental results show that the new GA-WELM model has good generalization ability and effectively improves the forecast accuracy

Citation Download Citation

Yang Xue, Qifei Wu, and Haoying Sun "Air quality prediction model based on genetic algorithm and weighted extreme learning machine", Proc. SPIE 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering, 1229454 (19 October 2022); https://doi.org/10.1117/12.2641287

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