title: A hybrid model for PM₂.₅ forecasting based on ensemble empirical mode decomposition and a general regression neural network.
abstract: Exposure to high concentrations of fine particulate matter (PM₂.₅) can cause serious health problems because PM₂.₅ contains microscopic solid or liquid droplets that are sufficiently small to be ingested deep into human lungs. Thus, daily prediction of PM₂.₅ levels is notably important for regulatory plans that inform the public and restrict social activities in advance when harmful episodes are foreseen. A hybrid EEMD-GRNN (ensemble empirical mode decomposition-general regression neural network) model based on data preprocessing and analysis is firstly proposed in this paper for one-day-ahead prediction of PM₂.₅ concentrations. The EEMD part is utilized to decompose original PM₂.₅ data into several intrinsic mode functions (IMFs), while the GRNN part is used for the prediction of each IMF. The hybrid EEMD-GRNN model is trained using input variables obtained from principal component regression (PCR) model to remove redundancy. These input variables accurately and succinctly reflect the relationships between PM₂.₅ and both air quality and meteorological data. The model is trained with data from January 1 to November 1, 2013 and is validated with data from November 2 to November 21, 2013 in Xi'an Province, China. The experimental results show that the developed hybrid EEMD-GRNN model outperforms a single GRNN model without EEMD, a multiple linear regression (MLR) model, a PCR model, and a traditional autoregressive integrated moving average (ARIMA) model. The hybrid model with fast and accurate results can be used to develop rapid air quality warning systems.
authors.affiliation: School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China.
authors.forename: Qingping
authors.initials: Q
authors.lastname: Zhou
authors.affiliation: School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China. Electronic address: jianghaiyan.cn@gmail.com.
authors.forename: Haiyan
authors.initials: H
authors.lastname: Jiang
authors.affiliation: School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China.
authors.forename: Jianzhou
authors.initials: J
authors.lastname: Wang
authors.affiliation: School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China.
authors.forename: Jianling
authors.initials: J
authors.lastname: Zhou
mesh_terms: D000393:Air Pollutants / Q000032:analysis*; D000397:Air Pollution / Q000706:statistics & numerical data*; D002681:China; D004784:Environmental Monitoring / Q000379:methods*; D005544:Forecasting; D008956:Models, Chemical; D015233:Models, Statistical; D016571:Neural Networks, Computer; D052638:Particulate Matter / Q000032:analysis*
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter
keywords: Ensemble empirical mode decomposition; General regression neural network; Multiple linear regression; PM(2.5); Principal components regression