Application of an SVM-based regression model to the air quality study at local scale in the Aviles urban area (Spain)

作者：Suarez Sanchez A; Garcia Nieto P J; Riesgo Fernandez P; del Coz Diaz J J; Iglesias Rodriguez F J

来源：Mathematical and Computer Modelling, 2011, 54(5-6): 1453-1466.

DOI：10.1016/j.mcm.2011.04.017

• Air quality
• Pollutant substances
• Machine learning
• Support vector regression

The objective of this study is to build a regression model of air quality by using the support vector machine (SVM) technique in the Aviles urban area (Spain) at local scale. Hazardous air pollutants or toxic air contaminants refer to any substance that may cause or contribute to an increase in mortality or serious illness, or that may pose a present or potential hazard to human health. To accomplish the objective of this study, the experimental data of nitrogen oxides (NOx), carbon monoxide (CO), sulphur dioxide (SO2), ozone (O-3) and dust (PM10) for the years 2006-2008 are used to create a highly nonlinear model of the air quality in the Aviles urban nucleus (Spain) based on SVM techniques. One aim of this model is to obtain a preliminary estimate of the dependence between primary and secondary pollutants in the Aviles urban area at local scale. A second aim is to determine the factors with the greatest bearing on air quality with a view to proposing health and lifestyle improvements. The United States National Ambient Air Quality Standards (NAAQS) establishes the limit values of the main pollutants in the atmosphere in order to ensure the health of healthy people. They are known as criteria pollutants. This support vector regression model captures the main insight of statistical learning theory in order to obtain a good prediction of the dependence among the main pollutants in the Aviles urban area. Finally, on the basis of these numerical calculations, using the support vector regression (SVR) technique, conclusions of this work are drawn.

• 出版日期2011-9