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

Abstract

The development of forecasting models for pollution particles showsa 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: multilayerperceptrons, neural networks, support vector machines, kernel algorithms, andso on. This paper presents a prediction pollution model using support vectormachines and kernel functions, which are: Gaussian, Polynomial and Spline. Finally,the prediction results of ozone (O3), particulate matter (PM10) andnitrogen dioxide (NO2) at Mexico City are presented as a case studyusing these techniques.