A new hybrid optimization prediction model for PM2.5 concentration considering other air pollutants and meteorological conditions

With the development of economy, the problem of air pollution has become increasingly serious. As an important detection index of air pollutants, how to accurately and effectively predict PM2.5 concentration is a significant issue related to human health and development. In this paper, a new hybrid...

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Vydané v:Chemosphere (Oxford) Ročník 307; číslo Pt 3; s. 135798
Hlavní autori: Yang, Hong, Liu, Zehang, Li, Guohui
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.11.2022
Predmet:
air
air pollution
atmospheric pressure
China
COOT optimization Algorithm
human health
hybrids
JAYA optimization Algorithm
Least square support vector machine
PM2.5 concentration prediction
prediction
relative humidity
Secondary decomposition
solar radiation
support vector machines
temperature
wind speed
China
PM2.5 concentration prediction
Least square support vector machine
COOT optimization Algorithm
JAYA optimization Algorithm
Secondary decomposition
ISSN:0045-6535, 1879-1298, 1879-1298
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Shrnutí:With the development of economy, the problem of air pollution has become increasingly serious. As an important detection index of air pollutants, how to accurately and effectively predict PM2.5 concentration is a significant issue related to human health and development. In this paper, a new hybrid optimization prediction model for PM2.5 concentration based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), variational mode decomposition optimized by COOT optimization algorithm (COOT-VMD), and least square support vector machine (LSSVM) optimized by the JAYA optimization algorithm (JAYA-LSSVM), named CEEMDAN-COOT-VMD-JAYA-LSSVM, is proposed. To avoid artificially setting the limits of the decomposition layer and penalty factor of VMD parameters, an improved VMD by COOT optimization algorithm, named COOT-VMD, is proposed. First, the original sequence of PM2.5 concentration is decomposed by CEEMDAN. Second, the high complexity component with low prediction accuracy after once decomposition is decomposed by COOT-VMD. Third, a prediction model of optimized LSSVM by JAYA optimization algorithm, named JAYA-LSSVM, is proposed. JAYA-LSSVM is used to predict all components considering other air pollutants such as PM10, SO2, NO2, CO, and O3 and meteorological conditions such as wind speed, temperature, sunlight, relative humidity and average air pressure. Finally, all predicted values are reconstructed to obtain the final prediction results. PM2.5 concentration from April 1, 2016 to March 29, 2021 in Xi'an and Shenyang is used as the experimental data to verify the proposed model. The results of experiment in Xi'an show that the RMSE, MAE, MAPE and R2 are 2.843, 1.8344, 2.94%, and 0.99525 respectively. The results of experiment in Shenyang show that the RMSE, MAE, MAPE and R2 are 2.2714, 1.673, 3.13%, and 0.99573 respectively. Compared to other single and hybrid models, the proposed model can accurately predict PM2.5 concentration. Diebold Mariano test results display the proposed prediction model is superior to all comparison models at 99% confidence level. [Display omitted] •This paper proposes an improved VMD by COOT optimization algorithm.•This paper proposes a prediction model of optimized LSSVM by JAYA algorithm.•This paper considers five other air pollutants and five meteorological conditions.•This paper proposes a combined prediction model, named CEEMDAN-COOT-VMD-JAYA-LSSVM.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0045-6535
1879-1298
1879-1298
DOI:10.1016/j.chemosphere.2022.135798