Method to Improve Airborne Pollution Forecasting by Using Ant Colony Optimization and Neuro-Fuzzy Algorithms
Author(s)
Elizabeth Martinez-Zeron1,
Marco A. Aceves-Fernandez1*,
Efren Gorrostieta-Hurtado1,
Artemio Sotomayor-Olmedo2,
Juan Manuel Ramos-Arreguín1
Affiliation(s)
1 Facultad de Informática, Universidad Autónoma de Querétaro, Querétaro, México. 2 Laboratory of Artificial Intelligence, Faculty of Engineering, Universidad Autónoma de Querétaro, Querétaro, México.
1 Facultad de Informática, Universidad Autónoma de Querétaro, Querétaro, México. 2 Laboratory of Artificial Intelligence, Faculty of Engineering, Universidad Autónoma de Querétaro, Querétaro, México.
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.
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.
Cite this paper
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.
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.
References
[1] http://www.calidadaire.df.gob.mx/calidadaire/index.php?opcion=4&opcionrecursostecnicos=3 [2] Cortina. M. (2012) Aplicación de inteligencia artificial a la predicción de contaminantes atmosféricos. Tesis Doctoral, E.T.S.I. Telecomunicación (UPM). [3] Aceves-Fernández, M.A., Sotomayor-Olmedo, A., Gorrostieta-Hurtado, E., Pedraza-Ortega, J.C., Tovar-Arriaga, S. and Ramos-Arreguin, J.M. (2011) Performance Assessment of Fuzzy Clustering Models Applied to Urban Airborne Pollution. 2011 21st International Conference on Electrical Communications and Computers (CONIELECOMP), San Andres Cholula, 28 February-2 March 2011, 212-216. [4] Sotomayor-Olmedo, A., Aceves-Fernández, M.A., Gorrostieta-Hurtado, E., Pedraza-Ortega, C., Ramos-Arreguín, J.M. and Vargas-Soto, J.E. (2013) Forecast Urban Air Pollution in Mexico City by Using Support Vector Machines: A Kernel Performance Approach. International Journal of Intelligence Science, 3, 126-135.
http://dx.doi.org/10.4236/ijis.2013.33014 [5] (2005) Adaptive Neuro-Fuzzy Inference Systems (ANFIS). In: Nedjah, N. and de Macedo Mourelle, L., Eds., Fuzzy Systems Engineering: Theory and Practice. Vol. 181, Springer, Rio de Janeiro, 53-83. [6] Adaptive Neuro-Fuzzy Inference Systems (ANFIS) Library for Simulink Ilias S. Konsoulas M.Sc., CEng, MIET. [7] 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 [8] Sugeno, M. and Kang, G.T. (1988) Structure Identification of Fuzzy Model. Fuzzy Sets and Systems, 28, 15-33.
http://dx.doi.org/10.1016/0165-0114(88)90113-3 [9] Takagi, T. and Sugeno, M. (1985) Fuzzy Identification of Systems and Its Application to Modeling and Control. IEEE Transactions on Systems, Man and Cybernetics, SMC-15, 116-132. [10] Bezdek, J.C. (1981) Pattern Recognition with Fuzzy Objective Function Algoritms. Plenum Press, New York.
http://dx.doi.org/10.1007/978-1-4757-0450-1 [11] Dunn, J.C. (1973) A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters. Journal of Cybernetics, 3, 32-57. http://dx.doi.org/10.1080/01969727308546046 [12] Jang, J.S.R. (1993) ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Transactions on Systems, Man and Cybernetics, 23, 665-685. [13] Ghanbari, A., Abbasian-Naghneh, S. and Hadavandi, E. (2011) An Intelligent Load Forecasting Expert System by Integration of Ant Colony Optimization, Genetic Algorithms and Fuzzy Logic. 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), Paris, 11-15 April 2011, 246-251. [14] Adaptive Neuro-Fuzzy Inference Systems (ANFIS) Library for Simulink [15] Munoz, M., López, J. and Caicedos, E. (2008) Inteligencia de enjambres: Sociedades para la solución de problemas (una revisión). Ingeniería e Investigación, 28, 119-130. [16] Carretero, J.C.O., Cánovas, D.G. and Pereira, F.D.Q. (2008) Técnicas Heurísticas para Problemas de Diseno en Telecomunicaciones. [17] Dorigo, M. (1992) Optimization, Learning and Natural Algorithms. Ph.D. Thesis, Politecnico di Milano, Italian. [18] Calixto, C.A.C. (2005) Implementacion En Hidroinformática De Un Método De Optimización Matemática Basado En La Colonia De Hormigas. Tesis no publicada, Pontificia Universidad Javeriana, Bogota, Colombia. [19] Kole, A., Chakrabarti, P. and Bhattacharyya, S. (2013) An Ant Colony Optimization Algorithm for Uncapacitated Facility Location Problem.
[1] http://www.calidadaire.df.gob.mx/calidadaire/index.php?opcion=4&opcionrecursostecnicos=3 [2] Cortina. M. (2012) Aplicación de inteligencia artificial a la predicción de contaminantes atmosféricos. Tesis Doctoral, E.T.S.I. Telecomunicación (UPM). [3] Aceves-Fernández, M.A., Sotomayor-Olmedo, A., Gorrostieta-Hurtado, E., Pedraza-Ortega, J.C., Tovar-Arriaga, S. and Ramos-Arreguin, J.M. (2011) Performance Assessment of Fuzzy Clustering Models Applied to Urban Airborne Pollution. 2011 21st International Conference on Electrical Communications and Computers (CONIELECOMP), San Andres Cholula, 28 February-2 March 2011, 212-216. [4] Sotomayor-Olmedo, A., Aceves-Fernández, M.A., Gorrostieta-Hurtado, E., Pedraza-Ortega, C., Ramos-Arreguín, J.M. and Vargas-Soto, J.E. (2013) Forecast Urban Air Pollution in Mexico City by Using Support Vector Machines: A Kernel Performance Approach. International Journal of Intelligence Science, 3, 126-135.
http://dx.doi.org/10.4236/ijis.2013.33014 [5] (2005) Adaptive Neuro-Fuzzy Inference Systems (ANFIS). In: Nedjah, N. and de Macedo Mourelle, L., Eds., Fuzzy Systems Engineering: Theory and Practice. Vol. 181, Springer, Rio de Janeiro, 53-83. [6] Adaptive Neuro-Fuzzy Inference Systems (ANFIS) Library for Simulink Ilias S. Konsoulas M.Sc., CEng, MIET. [7] 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 [8] Sugeno, M. and Kang, G.T. (1988) Structure Identification of Fuzzy Model. Fuzzy Sets and Systems, 28, 15-33.
http://dx.doi.org/10.1016/0165-0114(88)90113-3 [9] Takagi, T. and Sugeno, M. (1985) Fuzzy Identification of Systems and Its Application to Modeling and Control. IEEE Transactions on Systems, Man and Cybernetics, SMC-15, 116-132. [10] Bezdek, J.C. (1981) Pattern Recognition with Fuzzy Objective Function Algoritms. Plenum Press, New York.
http://dx.doi.org/10.1007/978-1-4757-0450-1 [11] Dunn, J.C. (1973) A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters. Journal of Cybernetics, 3, 32-57. http://dx.doi.org/10.1080/01969727308546046 [12] Jang, J.S.R. (1993) ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE Transactions on Systems, Man and Cybernetics, 23, 665-685. [13] Ghanbari, A., Abbasian-Naghneh, S. and Hadavandi, E. (2011) An Intelligent Load Forecasting Expert System by Integration of Ant Colony Optimization, Genetic Algorithms and Fuzzy Logic. 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM), Paris, 11-15 April 2011, 246-251. [14] Adaptive Neuro-Fuzzy Inference Systems (ANFIS) Library for Simulink [15] Munoz, M., López, J. and Caicedos, E. (2008) Inteligencia de enjambres: Sociedades para la solución de problemas (una revisión). Ingeniería e Investigación, 28, 119-130. [16] Carretero, J.C.O., Cánovas, D.G. and Pereira, F.D.Q. (2008) Técnicas Heurísticas para Problemas de Diseno en Telecomunicaciones. [17] Dorigo, M. (1992) Optimization, Learning and Natural Algorithms. Ph.D. Thesis, Politecnico di Milano, Italian. [18] Calixto, C.A.C. (2005) Implementacion En Hidroinformática De Un Método De Optimización Matemática Basado En La Colonia De Hormigas. Tesis no publicada, Pontificia Universidad Javeriana, Bogota, Colombia. [19] Kole, A., Chakrabarti, P. and Bhattacharyya, S. (2013) An Ant Colony Optimization Algorithm for Uncapacitated Facility Location Problem.