Abstract:

【Objective】 We predict the concentration of PM_2.5 during different seasons and use the Principal Component Analysis (PCA) method to reduce the dimensionality of the data, while also improving the accuracy of the prediction and reducing the time complexity, to serve as a reference for travel by people and government decision-making. 【Method】 The PM_2.5 concentration was forecast based on the values of PM₁₀, SO₂, CO₂, CO and O₃ concentration in Hefei from 2014 to 2017, and the meteorological factors during the same period. The data analysis found that the concentration of PM_2.5 varies greatly across seasons; therefore, this study is focused on the forecasting during different seasons. To improve the accuracy of forecasting, influencing meteorological factors such as wind, temperature, humidity and air pressure were added to the forecasting. The PCA method was used for data dimensionality reduction, and then, the data were input into the BP neural network model for prediction. 【Result】 The experiment used three groups of assessments for comparison: five types of pollutant indicators to predict PM_2.5 (PM_2.5-5), addition of twelve comprehensive indicators of meteorological factors to predict PM_2.5 (PM_2.5-12), and use of comprehensive indicators which were processed by PCA (PM_2.5-PCA) to predict PM_2.5. The experimental results showed that the PM_2.5 concentration in the four seasons had large changes, and the difference in Root Mean Square Error(RMSE) is large, when using the PM_2.5-PCA method. We found that if the RMSE is reduced in any season, the correlation coefficient (r) value is increased. 【Conclusion】 The PM_2.5 concentration value has seasonal characteristics, and the seasonal prediction method can improve prediction accuracy. Moreover, adopting the PCA method to reduce data dimensionality can ensure accuracy and decrease the time complexity at the same time.

Key words: PM_2.5, neural networks, prediction, principal component analysis(PCA)