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| Content Provider | World Health Organization (WHO)-Global Index Medicus |
|---|---|
| Author | Zhang, Guisheng Qin, Zuodong Liu, Yong Wang, Ping |
| Spatial Coverage | China |
| Description | Author Affiliation: Wang P ( Institute of Loess Plateau, Shanxi University, Taiyuan, 030006, PR China.); Liu Y ( Institute of Loess Plateau, Shanxi University, Taiyuan, 030006, PR China. Electronic address: liuyong@sxu.edu.cn.); Qin Z ( Institute of Loess Plateau, Shanxi University, Taiyuan, 030006, PR China.); Zhang G ( School of Economics and Management, Shanxi University, Taiyuan, 030006, PR China.) |
| 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. |
| ISSN | 00489697 |
| Volume Number | 505 |
| e-ISSN | 18791026 |
| Journal | Science of The Total Environment |
| Language | English |
| Publisher | Elsevier |
| Publisher Date | 2015-02-01 |
| Publisher Place | Netherlands |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Air Pollution Statistics & Numerical Data Environmental Monitoring Methods Models, Theoretical Particulate Matter Analysis Sulfur Dioxide Air Pollutants China Forecasting Neural Networks (computer) Uncertainty Journal Article Research Support, Non-u.s. Gov't Discipline Environmental Science |
| Content Type | Text |
| Resource Type | Article |
| Subject | Environmental Chemistry Waste Management and Disposal Pollution Environmental Engineering |
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