Daily air quality index forecasting with hybrid models: A case in China

Air quality is closely related to quality of life. Air pollution forecasting plays a vital role in air pollution warnings and controlling. However, it is difficult to attain accurate forecasts for air pollution indexes because the original data are non-stationary and chaotic. The existing forecastin...

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Bibliographic Details
Published in:Environmental pollution (1987) Vol. 231; no. Pt 2; pp. 1232 - 1244
Main Authors: Zhu, Suling, Lian, Xiuyuan, Liu, Haixia, Hu, Jianming, Wang, Yuanyuan, Che, Jinxing
Format: Journal Article
Language:English
Published: England Elsevier Ltd 01.12.2017
Subjects:
air pollution
Air Pollution - statistics & numerical data
Air pollution indexes
air quality
China
Environmental Monitoring
Forecasting
Humans
Hybrid model
linear models
Models, Statistical
Quality of Life
China
Air pollution indexes
Forecasting
Hybrid model
ISSN:0269-7491, 1873-6424, 1873-6424
Online Access:Get full text
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Summary:Air quality is closely related to quality of life. Air pollution forecasting plays a vital role in air pollution warnings and controlling. However, it is difficult to attain accurate forecasts for air pollution indexes because the original data are non-stationary and chaotic. The existing forecasting methods, such as multiple linear models, autoregressive integrated moving average (ARIMA) and support vector regression (SVR), cannot fully capture the information from series of pollution indexes. Therefore, new effective techniques need to be proposed to forecast air pollution indexes. The main purpose of this research is to develop effective forecasting models for regional air quality indexes (AQI) to address the problems above and enhance forecasting accuracy. Therefore, two hybrid models (EMD-SVR-Hybrid and EMD-IMFs-Hybrid) are proposed to forecast AQI data. The main steps of the EMD-SVR-Hybrid model are as follows: the data preprocessing technique EMD (empirical mode decomposition) is utilized to sift the original AQI data to obtain one group of smoother IMFs (intrinsic mode functions) and a noise series, where the IMFs contain the important information (level, fluctuations and others) from the original AQI series. LS-SVR is applied to forecast the sum of the IMFs, and then, S-ARIMA (seasonal ARIMA) is employed to forecast the residual sequence of LS-SVR. In addition, EMD-IMFs-Hybrid first separately forecasts the IMFs via statistical models and sums the forecasting results of the IMFs as EMD-IMFs. Then, S-ARIMA is employed to forecast the residuals of EMD-IMFs. To certify the proposed hybrid model, AQI data from June 2014 to August 2015 collected from Xingtai in China are utilized as a test case to investigate the empirical research. In terms of some of the forecasting assessment measures, the AQI forecasting results of Xingtai show that the two proposed hybrid models are superior to ARIMA, SVR, GRNN, EMD-GRNN, Wavelet-GRNN and Wavelet-SVR. Therefore, the proposed hybrid models can be used as effective and simple tools for air pollution forecasting and warning as well as for management. [Display omitted] •Effective information in AQI series is extracted by EMD.•EMD-IMFs-Hybrid and EMD-SVR-Hybrid models are firstly proposed for AQI prediction.•The proposed hybrid models are superior to other forecasting models.•The hybrid model can be used to forecast other pollution indexes.
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ISSN:0269-7491
1873-6424
1873-6424
DOI:10.1016/j.envpol.2017.08.069