Author(s)

Artemio Sotomayor-Olmedo, Marco A. Aceves-Fernández, Efrén Gorrostieta-Hurtado, Carlos Pedraza-Ortega, Juan M. Ramos-Arreguín, J. Emilio Vargas-Soto

Universidad Autónoma de Querétaro, Cerro de las Campanas S/N, Querétaro, México.

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.

Predictive Models; Airborne Pollution; Support Vector Machines; Kernel Functions

Sotomayor-Olmedo, A. , Aceves-Fernández, M. , Gorrostieta-Hurtado, E. , Pedraza-Ortega, C. , Ramos-Arreguín, J. and Vargas-Soto, J. (2013) Forecast Urban Air Pollution in Mexico City by Using Support Vector Machines: A Kernel Performance Approach. International Journal of Intelligence Science, 3, 126-135. doi: 10.4236/ijis.2013.33014.