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27 June 2022 A short-time PM2.5 concentration forecasting method based on support vector regression and genetic algorithm
Rujia Wang, Weifeng Yang
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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.
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Rujia Wang and Weifeng Yang "A short-time PM2.5 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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KEYWORDS
Data modeling
Atmospheric modeling
Machine learning
Genetic algorithms
Meteorology
Neural networks
Optimization (mathematics)
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