Forecast Urban Air Pollution in Mexico City by Using Support Vector Machines: A Kernel Performance Approach

DOI: 10.4236/ijis.2013.33014   PDF   HTML     4,735 Downloads   8,498 Views   Citations


The development of forecasting models for pollution particles shows a nonlinear dynamic behavior; hence, implementation is a non-trivial process. In the literature, there have been multiple models of particulate pollutants, which use softcomputing techniques and machine learning such as: multilayer perceptrons, neural networks, support vector machines, kernel algorithms, and so on. This paper presents a prediction pollution model using support vector machines and kernel functions, which are: Gaussian, Polynomial and Spline. Finally, the prediction results of ozone (O3), particulate matter (PM10) and nitrogen dioxide (NO2) at Mexico City are presented as a case study using these techniques.

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A. Sotomayor-Olmedo, M. Aceves-Fernández, E. Gorrostieta-Hurtado, C. Pedraza-Ortega, J. Ramos-Arreguín and J. Vargas-Soto, "Forecast Urban Air Pollution in Mexico City by Using Support Vector Machines: A Kernel Performance Approach," International Journal of Intelligence Science, Vol. 3 No. 3, 2013, pp. 126-135. doi: 10.4236/ijis.2013.33014.

Conflicts of Interest

The authors declare no conflicts of interest.


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