DOI: 10.1016/j.atmosenv.2015.02.030
Scopus记录号: 2-s2.0-84923017379
论文题名: Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation
作者: Feng X ; , Li Q ; , Zhu Y ; , Hou J ; , Jin L ; , Wang J
刊名: Atmospheric Environment
ISSN: 0168-2563
EISSN: 1573-515X
出版年: 2015
卷: 107 起始页码: 118
结束页码: 128
语种: 英语
英文关键词: Air mass trajectory based geographic model
; Artificial neural networks
; PM2.5 forecasting
; Wavelet transformation
Scopus关键词: Air pollution
; Air quality
; Backpropagation
; Forecasting
; Mean square error
; Neural networks
; Pollution
; Trajectories
; Wavelet decomposition
; Wavelet transforms
; Wind
; Air pollution indicators
; Air-pollution monitoring stations
; Back propagation neural networks
; Experimental verification
; Geographic model
; Root mean squared errors
; Wavelet transformations
; Wind speed and directions
; Atmospheric movements
; air mass
; artificial neural network
; atmospheric pollution
; concentration (composition)
; forecasting method
; geographical variation
; new record
; particulate matter
; pollution monitoring
; trajectory
; wavelet analysis
; air mass trajectory analysis
; air pollution
; air quality
; analysis
; Article
; artificial neural network
; back propagation
; China
; forecasting
; humidity
; meteorological phenomena
; particulate matter
; perceptron
; pollution monitoring
; priority journal
; root mean squared error
; statistical analysis
; temperature
; velocity
; wavelet analysis
; wind
; wind direction
; wind speed
; Beijing [China]
; China
; Hebei
; Tianjin
Scopus学科分类: Environmental Science: Water Science and Technology
; Earth and Planetary Sciences: Earth-Surface Processes
; Environmental Science: Environmental Chemistry
英文摘要: In the paper a novel hybrid model combining air mass trajectory analysis and wavelet transformation to improve the artificial neural network (ANN) forecast accuracy of daily average concentrations of PM2.5 two days in advance is presented. The model was developed from 13 different air pollution monitoring stations in Beijing, Tianjin, and Hebei province (Jing-Jin-Ji area). The air mass trajectory was used to recognize distinct corridors for transport of "dirty" air and "clean" air to selected stations. With each corridor, a triangular station net was constructed based on air mass trajectories and the distances between neighboring sites. Wind speed and direction were also considered as parameters in calculating this trajectory based air pollution indicator value. Moreover, the original time series of PM2.5 concentration was decomposed by wavelet transformation into a few sub-series with lower variability. The prediction strategy applied to each of them and then summed up the individual prediction results. Daily meteorological forecast variables as well as the respective pollutant predictors were used as input to a multi-layer perceptron (MLP) type of back-propagation neural network. The experimental verification of the proposed model was conducted over a period of more than one year (between September 2013 and October 2014). It is found that the trajectory based geographic model and wavelet transformation can be effective tools to improve the PM2.5 forecasting accuracy. The root mean squared error (RMSE) of the hybrid model can be reduced, on the average, by up to 40 percent. Particularly, the high PM2.5 days are almost anticipated by using wavelet decomposition and the detection rate (DR) for a given alert threshold of hybrid model can reach 90% on average. This approach shows the potential to be applied in other countries' air quality forecasting systems. © 2015 The Authors. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/81864
Appears in Collections: 气候变化事实与影响
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作者单位: Institute of Remote Sensing and GIS, Peking University, Beijing, China
Recommended Citation:
Feng X,, Li Q,, Zhu Y,et al. Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation[J]. Atmospheric Environment,2015-01-01,107