Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation
By Xiao Feng, Qi Li, Yajie Zhu, J. Hou, Lingyan Jin, Jingjie Wang. 2015
534 citations recorded by the source corpus.
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.
Subfields: Atmospheric Science, Environmental Engineering, Health, Toxicology and Mutagenesis
View Paper: https://www.semanticscholar.org/paper/26cfa2d0882cbbd2024985afde6cc3b32f385306
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
Citations in the corpus
- A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities (2018, methodology): “Furthermore, using geospatial assessment tools [17] or more complex statistic algorithms [18,19] are also feasible and practical for the forecasting of PM2.”
- A review of artificial neural network models for ambient air pollution prediction (2019, unknown): “One popular example under this type is the Elman network (Biancofiore et al., 2017b; X. Feng et al., 2015; Peng et al., 2017).”