Journal of Guangdong University of Technology ›› 2022, Vol. 39 ›› Issue (03): 89-94.doi: 10.12052/gdutxb.210055

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PM2.5 Concentration Improving Prediction Modeling of Seasonal Index

Zeng Jiang-yi, Li Zhi-sheng, Ou Yao-chun, Jin Yu-kai   

  1. School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2021-06-03 Online:2022-05-10 Published:2022-05-19

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Abstract: In recent years, China's economy and urbanization have developed rapidly, and the development of cities often comes at the expense of the environment. Regional air pollution dominated by PM2.5 has become the most pressing and prominent environmental problem in China. According to relevant studies, PM2.5 concentrations vary greatly in different seasons. Based on the PM2.5 monthly mean concentration data of Guangzhou from 2015 to 2019, combined with atmospheric pollutants and meteorological factors, with the seasonal index introduced, an improved multiple linear regression and multi-layer perceptual combination prediction model for PM2.5 concentration is established, to analyze the variation law and future development trend of PM2.5 concentration in Guangzhou. The results show that the combined prediction model with the improved seasonal index is used to predict and analyze PM2.5, and the fitting results are good. Compared with the multi-layer perceptron prediction model by using different evaluation indexes, the RMSE(Root Mean Square Error), MAPE(Mean Absolute Percentage Error) and MAE(Mean Absolute Error) of the combined model are reduced by 23.1%, 31% and 24.2% . Compared with the multiple linear regression model, the reduction is 35.3%, 41.3% and 41% . The accuracy of the model is better than the traditional multiple linear regression model and multi-layer perceptron model, which can better predict environmental PM2.5 concentration and provide reference for optimizing the environment.

Key words: PM2.5 concentration forecasting, multiple linear regression, multilayer perceptron, neural networks

CLC Number: 

  • X831
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