兰州市空气质量指数预测——基于LSTM的混合模型研究

Lanzhou Air Quality Index Prediction—A Mixed Model Study Based on LSTM

本文旨在通过构建EEMD-GWO-LSTM混合模型,对兰州市空气质量指数(AQI)进行准确预测。兰州市作为中国西北地区重要的工业基地和交通枢纽,其空气质量受工业排放、交通污染及地理环境等因素影响,常年处于高污染等级。针对兰州市AQI监测数据突变性强的特点,文章首先对数据进行预处理,包括填补缺失值、归一化处理等,以提高数据质量。随后,采用集合经验模态分解(EEMD)对数据进行分解,提取出本征模态函数(IMF),并利用灰狼优化算法(GWO)对长短期记忆网络(LSTM)模型的超参数进行优化,以提高预测精度。实验结果表明,EEMD-GWO-LSTM混合模型在预测兰州市AQI时,相较于单一模型和其他混合模型,具有更低的均方根误差(RMSE)和更高的决定系数(R2),显示出更好的预测性能。最后,文章提出了增加监测站点、采用先进技术提高监测频率、跨区域合作及数据公开共享等建议,以促进兰州市空气质量的持续改善和预测模型的进一步优化。This paper aims to accurately predict the Air Quality Index (AQI) of Lanzhou City by constructing a mixed model of EEMD-GGO-LSTM. Lanzhou City is an important industrial base and transportation hub in northwest China. Its air quality is affected by industrial emissions, traffic pollution, and the geographical environment, and it always has a high pollution level. In view of the strong mutability of AQI monitoring data in Lanzhou City, the paper first preprocessed the data, including filling in missing values and normalization processing, so as to improve the data quality. Then, the data is decomposed by ensemble empirical Mode decomposition (EEMD), extracting the intrinsic mode function (IMF), and the hyperparameters of the long short-term memory network (LSTM) model are optimized by Grey Wolf optimization algorithm (GWO) to improve the prediction accuracy. The experimental results show that the EEMD-GGO-LSTM mixed model has a lower root-mean-square error (RMSE) and higher determination coefficient (R2) when predicting AQI in Lanzhou compared with the single model and other mixed models, showing better prediction performance. Finally, the paper puts forward some suggestions, such as increasing monitoring stations, using advanced technology to improve monitoring frequency, cross-regional cooperation and open data sharing, so as to promote the continuous improvement of Lanzhou air quality and further optimization of a prediction model.