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基于LSTM-GCN的PM2.5浓度预测模型
Prediction Model of PM2.5 Concentration Based on LSTM-GCN
投稿时间:2021-07-12  修订日期:2022-06-05
DOI:10.19316/j.issn.1002-6002.2022.05.17
中文关键词:  长短期记忆网络|图卷积网络|细颗粒物浓度预测
英文关键词:LSTM|GCN|PM2.5 prediction
基金项目:北京市科学技术委员会新一代信息通信技术创新项目(Z201100004220011)
作者单位
马俊文 北京市生态环境监测中心, 大气颗粒物监测技术北京市重点实验室, 北京 100048 
严京海* 北京市生态环境监测中心, 大气颗粒物监测技术北京市重点实验室, 北京 100048 
孙瑞雯 北京市生态环境监测中心, 大气颗粒物监测技术北京市重点实验室, 北京 100048 
刘保献 北京市生态环境监测中心, 大气颗粒物监测技术北京市重点实验室, 北京 100048 
通讯作者:严京海*  北京市生态环境监测中心, 大气颗粒物监测技术北京市重点实验室, 北京 100048  
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中文摘要:
      应用机器学习算法开展空气质量预测已成为当前研究热点之一,空气质量监测数据具有显著的时空特征,即具有时间维度时序特征和空间维度传输演化特征。面向空气质量监测数据,联合LSTM提取的时间特征和GCN提取的空间特征,提出预测PM2.5浓度的LSTM-GCN组合模型。以北京市35个空气质量监测站2018—2020年监测数据进行仿真实验,并将LSTM-GCN模型与LSTM模型、GCN模型以及时空地理加权回归模型(GTWR)进行对比,结果显示:LSTM-GCN模型相较于LSTM模型均方根误差(RMSE)、平均绝对误差(MAE)分别降低了11.68%、7.34%;相较于GCN模型RMSE、MAE分别降低了40.22%、36.37%;相较于GTWR模型RMSE、MAE分别降低了17.52%、23.69%,表明所提出LSTM-GCN模型在准确率上有所提升。用LSTM-GCN模型预测2021年1—7月PM2.5浓度,结果显示预测效果较好。
英文摘要:
      The application of machine learning algorithm to air quality prediction has become one of the current research hotspots.The air quality monitoring data has significant spatial-temporal characteristics,that is,time-series characteristics and spatial evolution characteristics.Based on the air quality monitoring data,LSTM is used to extract temporal features and GCN is used to extract spatial features.A combined LSTM-GCN model is proposed to predict PM2.5 concentration.The simulation experiment was carried out with the monitoring data of 35 air quality monitoring stations from 2018 to 2020 in Beijing,and the LSTM-GCN model was compared with LSTM model,GCN model,Geographically and Temporally Weighted Regression model(GTWR).The results showed that the model compared with LSTM model,Mean Square Root Error(RMSE),Mean Absolute Error(MAE) decreased by 11.68% and 7.34% respectively.Compared with GCN model,RMSE and MAE decreased by 40.22% and 36.37% respectively.Compared with GTWR model,RMSE and MAE decreased by 17.52% and 23.69% respectively.It showed that the LSTM-GCN model proposed in this study could effectively improve the prediction accuracy.Finally,the LSTM-GCN model was used to predict the PM2.5 concentration from January to July 2021.The results showed that the prediction effect was satisfactory.
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