Aceves-Fernandez, M.A., Sotomayor-Olmedo, A., Gorrostieta-Hurtado, E., Pedraza-Ortega, J.C., Ramos-Arreguín, J.M, Canchola-Magdaleno, S. and Vargas-Soto, E. (2011) Advances in Airborne Pollution Forecasting Using Soft Computing Techniques. In: Popovic, D., Ed., Air Quality: Models and Applications, INTECH Publisher, Querétaro, 1-14. http://dx.doi.org/10.5772/16273

has been cited by the following article:

• TITLE: Method to Improve Airborne Pollution Forecasting by Using Ant Colony Optimization and Neuro-Fuzzy Algorithms

  AUTHORS: Elizabeth Martinez-Zeron, Marco A. Aceves-Fernandez, Efren Gorrostieta-Hurtado, Artemio Sotomayor-Olmedo, Juan Manuel Ramos-Arreguín

  KEYWORDS: Neuro-Fuzzy models, Ant Colony Optimization, Airborne Pollution

  JOURNAL NAME: International Journal of Intelligence Science, Vol.4 No.4, September 30, 2014

  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 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.