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

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....

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Bibliographic Details
Published in:Applied mathematics and computation Vol. 217; no. 12; pp. 5318 - 5327
Main Authors: Lin, Kuo-Ping, Pai, Ping-Feng, Yang, Shun-Ling
Format: Journal Article
Language:English
Published: Amsterdam Elsevier Inc 15.02.2011
Elsevier
Subjects:
Algorithms
Calculus of variations and optimal control
Concentrations of air pollutants
Data preprocessing
Exact sciences and technology
Forecasting
Immune algorithms
Mathematical analysis
Mathematical models
Mathematics
Numerical analysis
Numerical analysis. Scientific computation
Numerical methods in mathematical programming, optimization and calculus of variations
Numerical methods in optimization and calculus of variations
Parameter selection
Pollutants
Preprocessing
Regression
Sciences and techniques of general use
Support vector regression
Vectors (mathematics)
Support vector regression
Concentrations of air pollutants
Parameter selection
Data preprocessing
Immune algorithms
Human
Smoothing methods
Optimization method
Time series
Algorithm
Experimental result
Numerical analysis
Smoothing
Applied mathematics
Regression model
Variational calculus
Mathematical programming
ISSN:0096-3003, 1873-5649
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Summary: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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ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2010.11.055