基于最优定权组合法的大气污染物SO2预测

为了提高对大气污染物SO₂的预测准确率，基于多个空气质量预测模式（WRF-CHEM、CMAQ、CAMx），以过去一段时间内各单项空气质量预测模式的组合预测误差平方和最小为原则，构建出针对大气污染物SO₂的最优定权组合预测模型.选取2018年云南省楚雄、昭通、蒙自三个站点1至5月份的实际观测数据和前述三个空气质量模式的预测数据作为实验样本，然后分别采用多元线性回归法和动态权重更新法在相同的实验条件下与所提的最优定权组合预测法进行预测对比实验.实验结果表明，所提方法的预测值相较其他两种方法更加贴近实际观测值，其两项误差评估指标值均最小.总体而言，最优定权组合预测模型很好地综合了各单项空气质量预测模式的优势，提高了SO₂的预测精度.

Prediction of Atmospheric Pollutant SO2 Based on Optimal Weighted Combination Method

To improve the prediction accuracy of atmospheric pollutant SO₂, a combined forecasting model with optimal weights for atmospheric pollutant SO₂ was constructed, which was based on multiple air quality prediction (WRF-CHEM, CMAQ, CAMx) modes according to the principle of minimum square sum of combined forecasting errors of each single air quality prediction model in the past years. The actual observation data and the aforementioned three air quality modes prediction data of Chuxiong, Zhaotong, and Mengzi stations in Yunnan Province from January to May in 2018 were selected as the experimental samples. Then the multivariate linear regression method and the dynamic weight updating method were used to compare the prediction results with the optimal weighted combination prediction method proposed in this study under the same experimental conditions. The experimental results show that the predicted values of the proposed method are closer to the observed values than those of the other two methods, and the two error evaluation indexes are the smallest. Generally speaking, the combined forecasting model with optimal weights synthesizes the advantages of each single air quality forecasting model and improves the forecasting accuracy of SO₂.