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

• 作者: Artemio Sotomayor-Olmedo Carlos Pedraza-Ortega Efrén Gorrostieta-Hurtado J. Emilio Vargas-Soto Juan M. Ramos-Arreguín Marco A. Aceves-Fernández
• 来源期刊: 智能科学国际期刊(英文)
• 学科: 工学
• 关键词: PREDICTIVE Models AIRBORNE POLLUTION Support Vector Machines KERNEL Functions
• 年，卷（期）: 2013,（3）
• 页码范围: 126-135
• 页数: 10页
• 分类号: TP39

摘要

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.