A short-time PM2.5 concentration forecasting method based on support vector regression and genetic algorithm

Rujia Wang; Weifeng Yang

doi:10.1117/12.2639401

27 June 2022 A short-time PM_2.5 concentration forecasting method based on support vector regression and genetic algorithm

Rujia Wang, Weifeng Yang

Author Affiliations +

Proceedings Volume 12253, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022); 122530O (2022) https://doi.org/10.1117/12.2639401
Event: Second International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022), 2022, Qingdao, China

Abstract

To improve the short-time forecasting accuracy of PM2.5 concentration, this paper presents a GA-SVR forecasting method based on support vector regression (SVR) and genetic algorithm (GA). GA can optimize the hyper-parameters of the SVR model to obtain higher forecasting accuracy. The inputs of the model contain air pollutant data, meteorological data and seasonal features. To confirm the effectiveness of the proposed method, models have been trained and tested based on two public data sets and compared to other machine learning methods.

Citation Download Citation

Rujia Wang and Weifeng Yang "A short-time PM_2.5 concentration forecasting method based on support vector regression and genetic algorithm", Proc. SPIE 12253, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2022), 122530O (27 June 2022); https://doi.org/10.1117/12.2639401

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