Forecasting concentrations of air pollutants by logarithm support vector regression with immune algorithms

Forecasting concentrations of air pollutants by logarithm support vector regression with immune algorithms

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Article ID: iaor20113259
Volume: 217
Issue: 12
Start Page Number: 5318
End Page Number: 5327
Publication Date: Feb 2011
Journal: Applied Mathematics and Computation
Authors: , ,
Keywords: statistics: regression
Abstract:

The need to minimize the potential impact of air pollutants on humans has made the accurate prediction of concentrations of air pollutants a crucial subject in environmental research. Support vector regression (SVR) models have been successfully employed to solve time series problems in many fields. The use of SVR models for forecasting concentrations of air pollutants has not been widely investigated. Data preprocessing procedures and the parameter selection of SVR models can radically influence forecasting performance. This study proposes a support vector regression with logarithm preprocessing procedure and immune algorithms (SVRLIA) model which takes advantage of the structural risk minimization of SVR models, the data smoothing of preprocessing procedures, and the optimization of immune algorithms, in order to more accurately forecast concentrations of air pollutants. Three pollutants, namely particulate matter (PM10), nitrogen oxide, (NOx), and nitrogen dioxide (NO2), are collected and examined to determine the feasibility of the developed SVRLIA model. Experimental results reveal that the SVRLIA model can accurately forecast concentrations of air pollutants.

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