Author(s)

Elizabeth Martinez-Zeron¹, Marco A. Aceves-Fernandez^1*, Efren Gorrostieta-Hurtado¹, Artemio Sotomayor-Olmedo², Juan Manuel Ramos-Arreguín¹

¹Facultad de Informática, Universidad Autónoma de Querétaro, Querétaro, México.
²Laboratory of Artificial Intelligence, Faculty of Engineering, Universidad Autónoma de Querétaro, Querétaro, México.

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 Colony 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.

Neuro-Fuzzy models, Ant Colony Optimization, Airborne Pollution

Martinez-Zeron, E. , Aceves-Fernandez, M. , Gorrostieta-Hurtado, E. , Sotomayor-Olmedo, A. and Ramos-Arreguín, J. (2014) Method to Improve Airborne Pollution Forecasting by Using Ant Colony Optimization and Neuro-Fuzzy Algorithms. International Journal of Intelligence Science, 4, 81-90. doi: 10.4236/ijis.2014.44010.