关键词:
PM_(2.5)interval forecasting
graph generative network
graph U-Nets
sparse Bayesian regression
kernel density estimation
spatial-temporal characteristics
摘要:
Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other *** the changes and trends of air pollution can provide a scientific basis for governance and prevention *** this paper,we propose an interval prediction method that considers the spatio-temporal characteristic information of PM_(2.5)signals from multiple stations.K-nearest neighbor(KNN)algorithm interpolates the lost signals in the process of collection,transmission,and storage to ensure the continuity of *** generative network(GGN)is used to process time-series meteorological data with complex *** graph U-Nets framework is introduced into the GGN model to enhance its controllability to the graph generation process,which is beneficial to improve the efficiency and robustness of the *** addition,sparse Bayesian regression is incorporated to improve the dimensional disaster defect of traditional kernel density estimation(KDE)interval *** the support of sparse strategy,sparse Bayesian regression kernel density estimation(SBR-KDE)is very efficient in processing high-dimensional large-scale *** PM_(2.5)data of spring,summer,autumn,and winter from 34 air quality monitoring sites in Beijing verified the accuracy,generalization,and superiority of the proposed model in interval prediction.