K-means aided spatio-temporal CNN-LSTM network for air quality forecasting

Forecasting air quality is a crucial technical approach to effectively respond to severe pollution conditions. The evolution of pollutant concentration has spatial correlation Due to the challenge of identifying monitoring stations with significant spatial correlation, a method utilizing the K-means clustering algorithm is proposed for partitioning air quality monitoring stations. Taking Nantong as an example, based on the selection of historical pollutant data in the target area, combined with meteorological data, the hybrid CNN-LSTM model, which consists of the convolutional neural network(CNN) and the long short-term memory(LSTM) neural network, is used to predict the pollutants, and finally realize the extraction of the temporal and spatial evolution characteristics of the pollutant concentration to complete the high accuracy of air quality forecast. Experimental results show that, after adding historical pollutant concentration data from stations in the cluster, the CNN-LSTM model can forecast PM2.5 concentrations precisely.