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.
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机译:Do Regional Integration Plans Promote Joint Prevention and Control of Air Pollution? - Lessons from China’s Major City Clusters =区域一体化进程带给大气污染联防联控的契机和挑战 - 基于中国国家级城市群发展的研究