基于GCN-LSTM的空气质量预测

随着我国环境监测技术的不断发展,环境空气质量的网格化监测体系越来越受到相关工作人员的青睐,为应对空气污染的网格化监测体系中的小型、微型监测站的空气质量预测问题,本文提出了一种基于GCN和LSTM的空气质量预测模型.首先利用GCN网络提取网格化监测体系中的小微型监测站之间的空间特征,然后再使用LSTM提取时间特征,最后使用线性回归层来综合时空特征并产生空气质量的预测结果.为了验证本文提出的预测模型的性能,我们使用了沈阳市浑南区的14个小微型监测站的空气质量监测数据进行实验.实验结果显示,基于GCN-LSTM的空气质量预测模型在空间关联较强的网格化监测中的小微型监测站上的预测结果的精度要优于单一的LSTM预测模型.

Air Quality Prediction Based on GCN-LSTM

With the development of environmental monitoring technology in China, the grid monitoring system of ambient air quality has received more attention from environmental workers. In order to solve the air quality prediction of small and miniature monitoring stations in the grid monitoring system of air pollution, we propose an air quality prediction model based on GCN and LSTM. First, GCN is applied to extract the spatial features between the small and miniature monitoring stations in the grid monitoring system. Then, LSTM is employed to extract the relevant temporal features. Finally, the linear regression layer is used to integrate the spatial and temporal features and get the prediction results of air quality. Furthermore, experiments are carried out on the air quality monitoring data from 14 small and miniature monitoring stations in Hunnan District, Shenyang, verifying the prediction effect of the proposed model. The experimental results show that the air quality prediction model based on GCN-LSTM is more accurate than the LSTM prediction model in terms of the prediction results on the small and miniature monitoring stations in the grid monitoring with strong spatial association.