基于CEEMD-BiGRU模型的徐州市大气污染物浓度预测

目前大气污染物对于地区经济以及人体健康的影响不容忽视。选取徐州市2016-01-01—2021-01-24大气污染物和气象要素数据,针对大气污染物浓度波动性强等特点,运用互补集成经验模态分解(CEEMD)将污染物数据分解为本征模态分量,提取出原始数据的各项特征,再对分解出的各本征模态分量构建双向门控循环单元模型(BiGRU),通过双向循环训练,学习各分量的特征趋势并获得最优训练参数,将输出结果重构,得到最终的预测值。结果表明:与BiGRU、BP模型相比,CEEMD-BiGRU模型预测各项大气污染物的平均绝对误差(MAE)、均方根误差(RMSE)和平均绝对百分比误差(MAPE)分别下降15%、20%和2百分点以上,预测精度有较大提升。在此基础上,利用CEEMD-BiGRU模型预测后一时间段残差,以修正原预测值,得到大气污染物预测区间上界,进一步扩展模型的适用性。

PREDICTION OF AIR POLLUTANT CONCENTRATIONS IN XUZHOU BASED ON CEEMD-BiGRU MODEL

The current impact of air pollutants on the regional economy and human health cannot be ignored. This paper selected data on atmospheric pollutants and meteorological elements in Xuzhou from January 1, 2016 to January 24, 2021. Given the characteristics of strong fluctuation of atmospheric pollutant concentration, the pollutant data were decomposed into intrinsic mode fuction by using complementary ensemble empirical mode decomposition(CEEMD), and various features of the original data were extracted. Each decomposed intrinsic mode fuction was used as the input layer of the bidirectional gated recurrent model(BiGRU). Through bidirectional cyclic training, the characteristic trend of each component was learned and the optimal training parameters were obtained. The output results of each intrinsic mode fuction were reconstructed to obtain the final predicted value. The results showed that compared with the BiGRU and BP models, MAE, RMSE and MAPE of each air pollutant predicted by CEEMD-BiGRU model were reduced by more than 15%, 20% and 2 percentage points, and the prediction accuracy was greatly improved. On this basis, CEEMD-BiGRU model was used to predict the residuals of the latter period to correct the original prediction values and obtain the upper bound of the prediction interval for air pollutants, further to extend the applicability of the model.