Forecast of air quality based on ozone by decision trees and neural networks

  • Authors:

  • Nahun Loya;Iván Olmos Pineda;David Pinto;Helena Gómez-Adorno;Yuridiana Alemán

  • Affiliations:

  • Benemérita Universidad Autónoma de Puebla, Puebla, México;Benemérita Universidad Autónoma de Puebla, Puebla, México;Benemérita Universidad Autónoma de Puebla, Puebla, México;Benemérita Universidad Autónoma de Puebla, Puebla, México;Benemérita Universidad Autónoma de Puebla, Puebla, México

  • Venue:
  • MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
  • Year:
  • 2012

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Abstract

In this paper we explore models based on decision trees and neural networks models for predicting levels of ozone. We worked with a data set of the Atmospheric Monitoring System of Mexico City (SIMAT), which includes measurements hour by hour, between 2010 to 2011. The data come from of three meteorological stations: Pedregal, Tlalnepantla and Xalostoc in Mexico city. The data set includes 8 parameters: four chemical variables and four meteorological variables. Based on our results, it's possible to predict ozone levels with these parameters, with an accuracy of 94.4%.