基于长短期记忆网络的大气污染质量浓度预测方法OA

This paper proposes a deep learning prediction model integrating multi-source spatiotemporal features and seasonal trend decomposition.The model utilizes the graph convolutional network(GCN)and the time-domain convolutional network with attention mechanism to extract the spatial-dependent and temporal features of particulate matter 2.5(PM2.5)concentration.After the gate mechanism of the two,they are concatenated with the key meteorological and pollutant factors selected by random forest to form multi-source spatiotemporal features.Then,the seasonal trend decomposition algorithm is used to decompose the PM2.5 concentration sequence of the target site into trend,season,and residual sub-sequences.The multi-source spatiotemporal features are concatenated with each sub-sequence respectively,and input into the long short-term memory(LSTM)network-sequence-to-sequence-attention mechanism model for prediction,obtaining the final prediction result of PM2.5 concentration.The multi-source spatiotemporal feature seasonal trend decomposition model is named M5.The variant models obtained after removing the time,space,spatiotemporal feature fusion and seasonal trend decomposition feature extraction modules are named M1,M2,M3,and M4.Compared with M1 to M4,the root mean square error(RMSE)values of M5 in the 1-hour prediction are reduced by 12.1%,15.9%,14.7%,and 9.4%,respectively;in the 3-hour prediction,they are reduced by 18.5%,11.7%,9.4%,and 13.1%,respectively;in the 6-hour prediction,they are reduced by 15.4%,8.7%,10.5%,and 20.3%,respectively;compared with M1 to M4,at the end of each sliding window,the overall RMSE(caculated from the sum of squared errors)for M5's 1-hour to 6-hour ahead prediction is reduced by 17.4%,10.7%,10.7%,and 16.8%,respectively.