International Journal of Intelligence Science, 2014, 4, 81-90 Published Online October 2014 in SciRes. http://www.scirp.org/journal/ijis http://dx.doi.org/10.4236/ijis.2014.44010 How to cite this paper: Martinez-Zeron, E., Aceves-Fernandez, M.A., Gorrostieta-Hurtado, E., Sotomayor-Olmedo, A. and Ramos-Arreguín, J.M. (2014) Method to Improve Airborne Pollution Forecasting by Using Ant Colony Optimization and Neuro-Fuzzy Algorithms. International Journal of Intelligence Science, 4, 81-90. http://dx.doi.org/10.4236/ijis.2014.44010 Method to Improve Airborne Pollution Forecasting by Using Ant Colony Optimization and Neuro-Fuzzy Algorithms Elizabeth Martinez-Zeron 1 , Marco A. Aceves-Fernandez 1* , Efren Gorrostieta-Hurtado 1 , Artemio Sotomayor-Olmedo 2 , Juan Manuel Ramos-Arreguín 1 1 Facultad de Informática, Universidad Autónoma de Querétaro, Querétaro, México 2 Laboratory of Artificial Intelligence, Faculty of Engineering, Universidad Autónoma de Querétaro, Querétaro, México Email: * marco.aceves@gmail.com Received 3 August 2014; revised 5 September 2014; accepted 12 September 2014 Copyright © 2014 by authors and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/ Abstract This contribution shows the feasibility of improving the modeling of the non-linear behavior of airborne pollution in large cities. In previous works, models have been constructed using many machine learning algorithms. However, many of them do not work for all the pollutants, or are not consistent or robust for all cities. In this paper, an improved algorithm is proposed using Ant Co- lony Optimization (ACO) employing models created by a neuro-fuzzy system. This method results in a reduction of prediction error, which results in a more reliable prediction models obtained. Keywords Neuro-Fuzzy models, Ant Colony Optimization, Airborne Pollution 1. Introduction In recent years, the environment has been affected by the presence of particulate pollutants such as the Ozone (O 3 ), NO 2 nitrogen oxide, carbon monoxide CO, sulfur dioxide (SO 2 ) and particulate matter less than 10 microns PM 10 (≤10 microns) and Particles less than 2.5 micrometers PM 2.5 (≤2.5 microns) [1]. For this reason, pollution monitoring has been necessary for large cities with high concentration of population and industries. During several years the air quality in Mexico City for prevention of toxicity levels in health and the envi- ronment have been observed and evaluated [1]. * Corresponding author.