| 刊名 |
Meteorological and Environmental Research |
| 作者 |
Xiangru KONG1, Jiajia ZHANG1, Luntao YAO1, Tianning YANG2, Rongfang YANG1,3,4* |
| 作者单位 |
1.Hebei Meteorological Technology and Equipment Center, Shijiazhuang 050021, China; 2. Weichang Meteorological Bureau, Weichang 068450, China; 3. Xiong'an Key Laboratory of Atmospheric Boundary Layer, China Meteorological Administration, Xiong’an New Area 071800, China; 4. Hebei Key Laboratory of Meteorology and Ecological Environment, Shijiazhuang 050021, China |
| DOI |
10.19547/j.issn2152-3940.2025.03.010 |
| 年份 |
2025 |
| 刊期 |
3 |
| 页码 |
44-50 |
| 关键词 |
Ozone (O3); Multiple Linear Regression Model; Back Propagation Neural Network model; Auto Regressive Integrated Moving Average model; TS |
| 摘要 |
Firstly, based on the data of air quality and the meteorological data in Baoding City from 2017 to 2021, the correlations of meteorological elements and pollutants with O3 concentration were explored to determine the forecast factors of forecast models. Secondly, the O3-8h concentration in Baoding City in 2021 was predicted based on the constructed models of multiple linear regression (MLR), backward propagation neural network (BPNN), and auto regressive integrated moving average (ARIMA), and the predicted values were compared with the observed values to test their prediction effects. The results showed that overall, the MLR, BPNN and ARIMA models were able to forecast the changing trend of O3-8h concentration in Baoding in 2021, but the BPNN model gave better forecast results than the ARIMA and MLR models, especially for the prediction of the high values of O3-8h concentration, and the correlation coefficients between the predicted values and the observed values were all higher than 0.9 during June-September. The mean error (ME), mean absolute error (MAE), and root mean square error (RMSE) of the predicted values and the observed values of daily O3-8h concentration based on the BPNN model were 0.45, 19.11 and 24.41 µg/m3, respectively, which were significantly better than those of the MLR and ARIMA models. The prediction effects of the MLR, BPNN and ARIMA models were the best at the pollution level, followed by the excellent level, and it was the worst at the good level. In comparison, the prediction effect of BPNN model was better than that of the MLR and ARIMA models as a whole, especially for the pollution and excellent levels. The TS scores of the BPNN model were all above 66%, and the PC values were above 86%. The BPNN model can forecast the changing trend of O3 concentration more accurately, has a good practical application value, but at the same time, the predicted high values of O3 concentration should be appropriately increased according to error characteristics of the model.
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