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

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 (O sub(3)), particulate matter (PM10) and nitrogen dioxide (NO sub(2)) at Mexico City are presented as a case study using these techniques.