Air Quality Modeling Using the PSO-SVM-Based Approach, MLP Neural Network, and M5 Model Tree in the Metropolitan Area of Oviedo (Northern Spain)

García Nieto, P. J. (2001). Parametric study of selective removal of atmospheric aerosol by coagulation, condensation and gravitational settling. International Journal of Environmental Health Research, 11, 151–162. García Nieto, P. J. (2006). Study of the evolution of aerosol emissions from coal-fired power plants due to coagulation, condensation, and gravitational settling and health impact. Journal of Environmental Management, 79(4), 372–382. Lutgens, F. K., & Tarbuck, E. J. (2001). The atmosphere: an introduction to meteorology. New York: Prentice Hall. Wark, K., Warner, C. F., & Davis, W. T. (1997). Air pollution: its origin and control. New Jersey: Prentice Hall. Wang, L. K., Pereira, N. C., & Hung, Y. T. (2004). Air pollution control engineering. New York: Humana. Karaca, F., Alagha, O., & Ertürk, F. (2005). Statistical characterization of atmospheric PM10 and PM2.5 concentrations at a non-impacted suburban site of Istanbul, Turkey. Chemosphere, 59(8), 1183–1190. Comrie, A. C., & Diem, J. E. (1999). Climatology and forecast modeling of ambient carbon monoxide in Phoenix. Atmospheric Environment, 33, 5023–5036. Elbir, T., & Muezzinoglu, A. (2000). Evaluation of some air pollution indicators in Turkey. Environment International, 26(1–2), 5–10. Godish, T., Davis, W. T., & Fu, J. S. (2014). Air quality. Boca Raton: CRC. Akkoyunku, A., & Ertürk, F. A. (2003). Evaluation of air pollution trends in Istanbul. International Journal of Environment and Pollution, 18, 388–398. Suárez Sánchez, A., García Nieto, P. J., Riesgo Fernández, P., del Coz Díaz, J. J., & Iglesias-Rodríguez, F. J. (2011). Application of a SVM-based regression model to the air quality study at local scale in the Avilés urban area (Spain). Mathematical and Computer Modelling, 54(5–6), 1453–1466. Cooper, C. D., & Alley, F. C. (2002). Air pollution control. New York: Waveland Press. Vapnik, V. (1998). Statistical learning theory. New York: Wiley-Interscience. Hastie, T., Tibshirani, R., & Friedman, J. (2003). The elements of statistical learning. New York: Springer. Bishop, C. M. (2006). Pattern recognition and machine learning. New York: Springer. Schölkopf, B., Smola, A. J., Williamson, R., & Bartlett, P. (2000). New support vector algorithms. Neural Computing and Applications, 12(5), 1207–1245. Hansen, T., & Wang, C. J. (2005). Support vector based battery state of charge estimator. Journal of Power Sources, 141, 351–358. Li, X., Lord, D., Zhang, Y., & Xie, Y. (2008). Predicting motor vehicle crashes using support vector machine models. Accident Analysis & Prevention, 40, 1611–1618. Cristianini, N., & Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods. New York: Cambridge University Press. Steinwart, I., & Christmann, A. (2008). Support vector machines. New York: Springer. Kennedy, J., & Eberhart, R. C. (1995). Particle swarm optimization. In Proceedings of the fourth IEEE international conference on neural networks (Vol. 4, pp. 1942–1948). Perth: IEEE Service Center. Eberhart, R. C., Shi, Y., & Kennedy, J. (2001). Swarm intelligence. San Francisco: Morgan Kaufmann. Clerc, M. (2006). Particle swarm optimization. London: Wiley-ISTE. Olsson, A. E. (2011). Particle swarm optimization: theory, techniques and applications. New York: Nova Science Publishers. Boznar, M., Lesjack, M., & Mlakar, P. (1993). A neural network based method for short-term predictions of ambient SO2 concentrations in highly polluted industrial areas of complex terrain. Atmospheric Environment, 270, 221–230. Haykin, S. (1999). Neural networks: comprehensive foundation. New Jersey: Prentice Hall. Hooyberghs, J., Mensink, C., Dumont, D., Fierens, F., & Brasseur, O. (2005). A neural network forecast for daily average PM10 concentrations in Belgium. Atmospheric Environment, 39(18), 3279–3289. Kukkonen, J., Partanen, L., Karpinen, A., Ruuskanen, J., Junninen, H., Kolehmainen, M., Niska, H., Dorling, S., Chatterton, T., Foxall, R., & Cawley, G. (2003). Extensive evaluation of neural networks models for the prediction of NO2 and PM10 concentrations, compared with a deterministic modelling system and measurements in central Helsinki. Atmospheric Environment, 37, 4539–4550. Gardner, M. W., & Dorling, S. R. (1999). Neural network modelling and prediction of hourly NOx and NO2 concentrations in urban air in London. Atmospheric Environment, 33(5), 709–719. Chaloulakou, A., Saisana, M., & Spyrellis, N. (2003). Comparative assessment of neural networks and regression models for forecasting summertime ozone in Athens. Science of the Total Environment, 313, 1–13. Karaca, F., Nikov, A., & Alagha, O. (2006). NN-AirPol: a neural-network-based method for air pollution evaluation and control. International Journal of Environment and Pollution, 28(3–4), 310–325. Quinlan, J. R. (1992). Learning with continuous classes. In Proceedings of Australian joint conference on artificial intelligence (pp. 343–348). Singapore: World Scientific Press. Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and regression trees. Monterrey: Wadsworth and Brooks. Kisi, O. (2015). Pan evaporation modeling using least square support vector machine, multivariate adaptive regression splines and M5 model tree. Journal of Hydrology, 528, 312–320. Colbeck, I. (2008). Environmental chemistry of aerosol. New York: Wiley-Blackwell. Hewitt, C. N., & Jackson, A. V. (2009). Atmospheric science for environmental scientists. New York: Wiley-Blackwell. Schnelle, K. B., Dunn, R. F., & Ternes, M. E. (2015). Air pollution control technology handbook. Boca Raton: CRC. Simon, D. (2013). Evolutionary optimization algorithms. New York: Wiley. Yang, X.-S., Cui, Z., Xiao, R., Gandomi, A. H., & Karamanoglu, M. (2013). Swarm intelligence and bio-inspired computation: theory and applications. London: Elsevier. Monteiro, A., Lopes, M., Miranda, A. I., Borrego, C., & Vautard, R. (2005). Air pollution forecast in Portugal: a demand from the new air quality framework directive. International Journal of Environment and Pollution, 5, 1–9. Friedlander, S. K. (2000). Smoke, dust and haze: fundamentals of aerosol dynamics. New York: Oxford University Press. Vincent, J. H. (2007). Aerosol sampling: science, standards, instrumentation and applications. Chichester: Wiley. de Cos Juez, F. J., García Nieto, P. J., Martínez Torres, J., & Taboada Castro, J. (2010). Analysis of lead times of metallic components in the aerospace industry through a supported vector machine model. Mathematical and Computer Modelling, 52, 1177–1184. Shawe-Taylor, J., & Cristianini, N. (2004). Kernel methods for pattern analysis. New York: Cambridge University Press. Clerc, M. (2012). Standard particle swarm optimisation: from 2006 to 2011. Technical report. http://clerc.maurice.free.fr/pso/SPSO_descriptions.pdf. Accessed 23 Sept 2012. Solomatine, D. P., & Xue, Y. P. (2004). M5 model trees and neural networks: application to flood forecasting in the upper reach of the Hual River in China. Journal of Hydrologic Engineering, 9(6), 491–501. Rahimikhoob, A., Asadi, M., & Mashal, M. (2013). A comparison between conventional and M5 model tree methods for converting pan evaporation to reference evapotranspiration for semi-arid region. Water Resources Management, 27(14), 4815–4826. Pal, M., & Deswal, S. (2009). M5 model tree based modelling of reference evapotranspiration. Hydrological Processes, 23(10), 1437–1443. Pal, M. (2006). M5 model tree for land cover classification. International Journal of Remote Sensing, 27(4), 825–831. Wasserman, L. (2003). All of statistics: a concise course in statistical inference. New York: Springer. Freedman, D., Pisani, R., & Purves, R. (2007). Statistics. New York: W.W. Norton & Company. Picard, R., & Cook, D. (1984). Cross-validation of regression models. Journal of the American Statistical Association, 79(387), 575–583. Efron, B., & Tibshirani, R. (1997). Improvements on cross-validation: the .632 + bootstrap method. Journal of the American Statistical Association, 92(438), 548–560. Chang, C.-C., & Lin, C.-J. (2011). LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2, 1–27. Zambrano-Bigiarini, M., & Rojas, R. (2013). A model-independent particle swarm optimisation software for model calibration. Environmental Modelling & Software, 43, 5–25. Zambrano-Bigiarini, M., & Rojas, R. (2014). HydroPSO: a flexible and model-independent particle swarm optimisation (PSO) package for calibration/optimisation of environmental models. In R package, version 0.3–4. Vienna: R Foundation for Statistical Computing.