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L. A. Diaz-Robles, J. C. Ortega, J. S. Fu, G. D. Reed, J. C. Chow, J. G. Watson and J. A. Moncada-Herrera, “A Hybrid ARIMA and Artificial Neural Networks Model to Forecast Particulate Matter in Urban Areas: The Case of Temuco, Chile,” Atmospheric Environment, Vol. 42, No. 35, 2008, pp. 8331-8340. doi:10.1016/j.atmosenv.2008.07.020

has been cited by the following article:

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

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

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

  JOURNAL NAME: International Journal of Intelligence Science, Vol.3 No.3, July 5, 2013

  ABSTRACT: 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.