Article citationsMore>>

S. S. Ahmad, P. Biiker, L. Emberson and R. Shabbir, “Monitoring Nitrogen Dioxide Levels in Urban Areas in Rawalpindi, Pakistan,” Water, Air & Soil Pollution, Vol. 220, No. 1-4, 2011, pp. 141-150. doi:10.1007/s11270-011-0741-9

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.