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.
Highlights
In recent times, urban air pollution has been a growing problem especially for urban communities
This paper presents a prediction pollution model using support vector machines and kernel functions, which are: Gaussian, Polynomial and Spline
This method presents a feasible modeling technique of the monthly atmospheric pollution by applying the support vector machine with Gaussian, Polynomial and Spline kernels functions working in regression mode
Summary
Urban air pollution has been a growing problem especially for urban communities. PM10 (particles less or equal than 10 micrometers) and PM2.5 (particles less or equal than 2.5 micrometers), Ozone and Nitrogen dioxide are considered due to its effect on human health This is the primary reason why this research has been done: to monitor, and model the levels and spread of harmful particles in urban environments. There are other harmful particles such as Ozone and Nitrogen dioxide, making it essential to accurately model the nonlinear behavior of the system, by designing a more robust model with an enhanced method to reduce the error between the raw data and the model For this reason, support vector machines (SVM) are chosen for this work. Three categories of sources may be defined: 1) natural (those that are not associated with human activities); 2) anthropogenic (those produced by human activities); and 3) secondary (those formed in the atmosphere from natural and anthropogenic air pollutants) [13]
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