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• W4319945313 endingPage "101677" @default.
• W4319945313 startingPage "101677" @default.
• W4319945313 abstract "With the development of industrial economy, air pollution has become a problem that cannot be ignored. While pursuing short prediction time, high prediction accuracy also needs to be guaranteed. Therefore, this paper build a prediction model to get more accurate and effective prediction result, based on secondary decomposition and multiple influencing factors, with the air quality index data of Beijing and Taiyuan from January 1, 2020 to December 31, 2021. Firstly, improved variational mode decomposition based on GSA (GVMD) is proposed to obtain decomposed component and decomposition residual (Res). At the same time, secondary decomposition model is constructed for Res. Secondly, improved support vector machine for regression by AO (AOSVR) is proposed. At the same time, prediction model based on multiple influencing factors is proposed for reconstructed component with strong correlation. Then, input characteristic variables of prediction model are obtained by analyzing all decomposed component and SRes. Appropriate prediction models are selected for all component. Finally, prediction results of all components are reconstructed, and the model test is done. The result shows that the proposed model has the best prediction effect for Beijing (R2 = 0.99807, MAPE = 0.04852, MAE = 1.17993, MSE = 2.18732, RMSE = 1.47896) and Taiyuan (R2 = 0.99901, MAPE = 0.02291, MAE = 0.66372, MSE = 1.07440, RMSE = 1.03653) respectively." @default.
• W4319945313 created "2023-02-11" @default.
• W4319945313 creator A5013178268 @default.
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• W4319945313 date "2023-03-01" @default.
• W4319945313 modified "2023-10-16" @default.
• W4319945313 title "Air quality index prediction using a new hybrid model considering multiple influencing factors: A case study in China" @default.
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• W4319945313 doi "https://doi.org/10.1016/j.apr.2023.101677" @default.
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