title: A novel hybrid forecasting model for PM₁₀ and SO₂ daily concentrations.
abstract: Air-quality forecasting in urban areas is difficult because of the uncertainties in describing both the emission and meteorological fields. The use of incomplete information in the training phase restricts practical air-quality forecasting. In this paper, we propose a hybrid artificial neural network and a hybrid support vector machine, which effectively enhance the forecasting accuracy of an artificial neural network (ANN) and support vector machine (SVM) by revising the error term of the traditional methods. The hybrid methodology can be described in two stages. First, we applied the ANN or SVM forecasting system with historical data and exogenous parameters, such as meteorological variables. Then, the forecasting target was revised by the Taylor expansion forecasting model using the residual information of the error term in the previous stage. The innovation involved in this approach is that it sufficiently and validly utilizes the useful residual information on an incomplete input variable condition. The proposed method was evaluated by experiments using a 2-year dataset of daily PM₁₀ (particles with a diameter of 10 μm or less) concentrations and SO₂ (sulfur dioxide) concentrations from four air pollution monitoring stations located in Taiyuan, China. The theoretical analysis and experimental results demonstrated that the forecasting accuracy of the proposed model is very promising.
authors.affiliation: Institute of Loess Plateau, Shanxi University, Taiyuan, 030006, PR China.
authors.forename: Ping
authors.initials: P
authors.lastname: Wang
authors.affiliation: Institute of Loess Plateau, Shanxi University, Taiyuan, 030006, PR China. Electronic address: liuyong@sxu.edu.cn.
authors.forename: Yong
authors.initials: Y
authors.lastname: Liu
authors.affiliation: Institute of Loess Plateau, Shanxi University, Taiyuan, 030006, PR China.
authors.forename: Zuodong
authors.initials: Z
authors.lastname: Qin
authors.affiliation: School of Economics and Management, Shanxi University, Taiyuan, 030006, PR China.
authors.forename: Guisheng
authors.initials: G
authors.lastname: Zhang
mesh_terms: D000393:Air Pollutants / Q000032:analysis; D000397:Air Pollution / Q000706:statistics & numerical data*; D002681:China; D004784:Environmental Monitoring / Q000379:methods*; D005544:Forecasting; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D052638:Particulate Matter / Q000032:analysis*; D013458:Sulfur Dioxide / Q000032:analysis*; D035501:Uncertainty
chemical_list: D000393:Air Pollutants; D052638:Particulate Matter; D013458:Sulfur Dioxide
keywords: Air pollution forecasting; Artificial neural network; Hybrid forecasting model; Support vector machine; Taylor expansion forecasting model