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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Veröffentlicht in:	Applied mathematics and computation Jg. 217; H. 12; S. 5318 - 5327
Hauptverfasser:	Lin, Kuo-Ping, Pai, Ping-Feng, Yang, Shun-Ling
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier Inc 15.02.2011 Elsevier
Schlagworte:	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
Online-Zugang:	Volltext
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