Method for air pollutant concentration prediction based on graph attention network and temporal convolutional network
An innovative model that integrated graph attention networks(GAT)and attention-based temporal convolutional networks(ATCN),named GAT-ATCN was proposed to improve the accuracy and efficiency of air pollutant concentration prediction.Firstly,the complex spatial dependencies between monitoring stations through GAT were captured,using an attention mechanism to adaptively strengthen the connections between important nodes,thereby extracting spatial features.Secondly,the ATCN part was used to process time series data,learning long-term dependencies in the time dimension to capture the dynamic characteristics of pollutant concentration changes over time.Finally,actual air quality monitoring data and meteorological data from seven cities in the Jiangsu-Zhejiang-Shanghai region of China from 2018 to 2020 were selected to build a dataset and conduct experiments,which verified the effectiveness of the GAT-ATCN model.Experimental results showed that the GAT-ATCN model performed excellently across multiple evaluation metrics and could predict air pollutant concentration more accurately.
air pollution concentration predictiongraph attention network(GAT)attention-based temporal convolutional network(ATCN)deep learning
NETL
NSTL
万方数据
