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M. Kolehmainen, H. Martikainen, T. Hiltunen and J. Ruuskanen, “Forecasting Air Quality Parameters Using Hybrid Neural Network Modeling,” Environmental Monitoring and Assessment, Vol. 65, No. 1-2, 2000, pp. 277-286. doi:10.1023/A:1006498914708

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.