顾及地理环境各向异性的PM2.5浓度时空预测模型

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中文摘要：PM2.5浓度预测对于空气污染的防控和治理具有重要意义.传统图卷积神经网络(Graph Convolutional Network,GCN)等时空预测模型主要通过监测站点间的欧氏距离来度量PM2.5分布的空间相关性,未顾及地形和风向等因素对大气污染物传输过程的各向异性影响,导致地形复杂区域内的预测结果精度偏低.为此,本文提出了一种顾及地理环境各向异性的PM2.5浓度时空图卷积网络预测模型.首先,考虑地理环境的各向异性特征,利用地形和风向等对站点间PM2.5传播的各向异性影响构建GCN的边,将站点PM2.5浓度、土地利用和其他气象因子建模为GCN的节点特征.其次,通过GCN提取站点PM2.5浓度的空间特征.最后,通过门控循环单元(Gate Recurrent Unit,GRU)建模站点PM2.5浓度的时间特征并进行预测.本文以山地型省份贵州省2017年逐小时PM2.5浓度记录进行实验,并采用一系列时空预测基线模型(GTWR、STSVR和GCN+GRU)与本文模型进行对比.实验结果表明:① 本文模型的PM2.5浓度预测结果的RMSE、MAE别为10.047、6.848,相比基线模型平均下降了11.29%和12.16%;R2为0.883,比基线平均提升了3.72%;② 通过不同地形对站点PM2.5浓度相关性影响分析,论证了山脉沟谷等地形会显著影响站点间的PM2.5浓度的相关性,进而影响站点PM2.5浓度预测结果;③ 充分考虑地形和风向对PM2.5传播产生的各向异性影响,能够显著提升存在山脉和沟谷地形区域内PM2.5预测精度.

外文标题：A Spatiotemporal Prediction Model of PM2.5 Concentration Incorporating Geographic Environmental Anisotropy

外文摘要：PM2.5 concentration prediction plays a pivotal role in the prevention and control of air pollution.Traditional forecasting models,such as the Graph Convolutional Network(GCN)and other spatial-temporal prediction models,measure the spatial correlation of PM2.5 distribution primarily by monitoring the Euclidean distance between monitoring stations.However,these models often fail to account for the anisotropy effects of terrain,wind direction,and other factors that significantly influence the transport process of air pollutants.This oversight can result in lower accuracy of prediction results,especially in areas with complex terrain.This paper proposes a novel spatiotemporal convolutional network prediction model for PM2.5 concentration that takes into account the anisotropy of the geographical environment.The model first constructs the edges of the GCN by incorporating the anisotropic effects of terrain and wind direction on PM2.5 propagation between stations.It then models the station PM2.5 concentration,land use,and other meteorological factors as node characteristics of the GCN.The model employs GCN to extract the spatial characteristics of PM2.5 concentration and subsequently uses a Gate Recurrent Unit(GRU)to model and predict the temporal characteristics of PM2.5 concentration at the station.The model's performance was evaluated using hourly PM2.5 concentration records from the mountainous Guizhou province in 2017.It was compared against several spatiotemporal prediction baseline models,including Geographically and Temporally Weighted Regression(GTWR),Spatio-Temporal Support Vector Regression(STSVR),and a combined GCN+GRU model.The experimental results demonstrate that the model proposed in this paper significantly outperforms the baseline models.The Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)ratios of PM2.5 concentration predicted by the model are 10.047 and 6.848,respectively,which represent decreases of 11.29%and 12.16%compared to the baseline models.The R-squared value of 0.883 indicates an average improvement of 3.72%from the baseline.Furthermore,the analysis of the influence of different terrains on the correlation of PM2.5 concentration at different stations reveals that mountainous,gully,and other terrain features can significantly affect the correlation of PM2.5 concentration between stations.This,in turn,impacts the prediction results of PM2.5 concentration at different stations.The study concludes that fully considering the anisotropic effects of terrain and wind direction on PM2.5 propagation can substantially enhance the prediction accuracy of PM2.5 in mountain and gully terrain areas.By integrating the anisotropic characteristics of the geographical environment into the prediction model,this research contributes to the development of more accurate forecasting tools for PM2.5 concentrations.This development is expected to ensure accurate prediction of PM2.5 concentrations in areas with complex terrain and support the development of effective air pollution control strategies.

外文关键词：

PM2.5 concentration predictionspatiotemporal graph convolutional networkspatial autocorrelationgeographic covariatesterrain distancewind directionanisotropygeographical environment feature

作者：

王军杰、王达、丁晨、唐建波、石岩、杨学习

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作者单位：

中南大学地理信息系,长沙 410083

湖南省地理空间信息工程技术研究中心,长沙 410018

关键词：

PM2.5浓度预测时空图卷积网络空间相关性地理协变量地形距离风向各向异性地理环境特征

基金：

国家自然科学基金面上项目湖南省自然科学基金项目湖南省自然科学基金项目中南大学中央高校基本科研业务费专项资金资助

项目编号：

422714852022JJ405852024JJ83432024ZZTS0638

出版年：

2024

DOI：

10.12082/dqxxkx.2024.240238

地球信息科学学报

中国科学院地理科学与资源研究所

地球信息科学学报

CSTPCD北大核心

影响因子：1.004

ISSN：1560-8999

年,卷(期)：2024.26(9)