Air quality forecasting using a hybrid autoregressive and nonlinear model

被引:46
作者
Chelani, AB [1 ]
Devotta, S [1 ]
机构
[1] Natl Environm Engn Res Inst, Nagpur 440020, Maharashtra, India
关键词
time-series forecasting; ARIMA; nonlinear dynamics; hybrid model;
D O I
10.1016/j.atmosenv.2005.11.019
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The usual practices of air quality time-series forecasting are based on applying the models that deal with either the linear or nonlinear patterns. As the linear or nonlinear behavior of the time series is not known in advance, one applies the number of models and finally selects the one, which provides the most accurate results. The air pollutant concentration time series contain patterns that are not purely linear or nonlinear and applying either technique may give inadequate results. This study aims to develop a hybrid methodology that can deal with both the linear and nonlinear structure of the time series. The hybrid model is developed using the combination of autoregressive integrated moving average model, which deals with linear patterns and nonlinear dynamical model. To demonstrate the utility of the proposed technique, nitrogen dioxide concentration observed at a site in Delhi during 1999 to 2003 was utilized. The individual linear and nonlinear models were also applied in order to examine the performance of the hybrid model. The performance is compared for one-step and multi-step ahead forecasts using the error statistics such as mean absolute percentage error and relative error. It is observed that hybrid model outperforms the individual linear and nonlinear models. The exploitation of unique features of linear and nonlinear models makes it a powerful technique to predict the air pollutant concentrations. (c) 2005 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1774 / 1780
页数:7
相关论文
共 28 条
[1]   THE ANALYSIS OF OBSERVED CHAOTIC DATA IN PHYSICAL SYSTEMS [J].
ABARBANEL, HDI ;
BROWN, R ;
SIDOROWICH, JJ ;
TSIMRING, LS .
REVIEWS OF MODERN PHYSICS, 1993, 65 (04) :1331-1392
[2]  
BEALE M, 1997, MATLAB USERES GUIDE
[3]   THE LIMITS OF AIR-POLLUTION MODELING [J].
BENARIE, MM .
ATMOSPHERIC ENVIRONMENT, 1987, 21 (01) :1-5
[4]  
BOX GEP, 1970, TIME SERIES ANAL FOR
[5]  
Cambel A., 1993, APPL CHAOS THEORY PA
[6]   Prediction of ambient PM10 and toxic metals using artificial neural networks [J].
Chelani, AB ;
Gajghate, DG ;
Hasan, MZ .
JOURNAL OF THE AIR & WASTE MANAGEMENT ASSOCIATION, 2002, 52 (07) :805-810
[7]   Nonlinear dynamics of hourly ozone concentrations: Nonparametric short term prediction [J].
Chen, JL ;
Islam, S ;
Biswas, P .
ATMOSPHERIC ENVIRONMENT, 1998, 32 (11) :1839-1848
[8]   COMBINING FORECASTS - A REVIEW AND ANNOTATED-BIBLIOGRAPHY [J].
CLEMEN, RT .
INTERNATIONAL JOURNAL OF FORECASTING, 1989, 5 (04) :559-583
[9]  
FARMER DJ, 1987, PHYS REV LETT, V59, P85
[10]   INDEPENDENT COORDINATES FOR STRANGE ATTRACTORS FROM MUTUAL INFORMATION [J].
FRASER, AM ;
SWINNEY, HL .
PHYSICAL REVIEW A, 1986, 33 (02) :1134-1140