Prediction of Peak Concentrations of PM10 in the Area of Campo de Gibraltar (Spain) Using Classification Models

A comparative study of different classification methods that would en able predicting the peaks of pollutant concentrations in critical meteorological sit uations is carried out because of particulates emissions that cause the widespread existing industry in the area of Campo de Gibraltar. The classification methods used in this study are k-nearest-neighbour, Bayesian classifier, Backpropagation Multilayer Neural Network, and Support Vector Machine to predict daily mean concentrations peaks. The prediction of particulate matter (PM10) concentrations was performed on the basis of their concentration lagged and using other exogenous information as: temperature, humidity, wind speed and wind direction data. In order to avoid the curse of dimensionality, Principal Component Analysis (PCA) and Fisher Linear Discriminant Analysis (Fisher LDA) were applied as feature se lection methods. The study results indicate that the support vector machine models are able to give better predictions with fewer fractions of false peaks detected than the rest of classification models.

[1]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[2]  Patricio Perez.  Prediction of sulfur dioxide concentrations at a site near downtown Santiago, Chile , 2001 .

[3]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[4]  Patricio Perez,et al.  Prediction of NO and NO2 concentrations near a street with heavy traffic in Santiago, Chile , 2001 .

[5]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[6]  Nello Cristianini,et al.  An Introduction to Support Vector Machines and Other Kernel-based Learning Methods , 2000 .

[7]  Asha B. Chelani,et al.  Prediction of sulphur dioxide concentration using artificial neural networks , 2002, Environ. Model. Softw..

[8]  I. Jolliffe.  Principal Component Analysis , 2002 .

[9]  P. Viotti,et al.  Atmospheric urban pollution: applications of an artificial neural network (ANN) to the city of Perugia , 2002 .

[10]  James L. McClelland,et al.  James L. McClelland, David Rumelhart and the PDP Research Group, Parallel distributed processing: explorations in the microstructure of cognition . Vol. 1. Foundations . Vol. 2. Psychological and biological models . Cambridge MA: M.I.T. Press, 1987. , 1989, Journal of Child Language.

[11]  Giorgio Corani,et al.  Air quality prediction in Milan: feed-forward neural networks, pruned neural networks and lazy learning , 2005 .

[12]  Gavin C. Cawley,et al.  Extensive evaluation of neural network models for the prediction of NO2 and PM10 concentrations, compared with a deterministic modelling system and measurements in central Helsinki , 2003 .

[13]  J M Gorriz,et al.  Prediction of CO maximum ground level concentrations in the Bay of Algeciras, Spain using artificial neural networks. , 2008, Chemosphere.

[14]  Alexander J. Smola,et al.  Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.

[15]  Aldo Cipriano,et al.  Forecasting ozone daily maximum levels at santiago, chile , 1998 .

[16]  Jorge Reyes,et al.  Prediction of Particlulate Air Pollution using Neural Techniques , 2001, Neural Computing & Applications.

[17]  Philip Demokritou,et al.  Measurements of PM10 and PM2.5 particle concentrations in Athens, Greece , 2003 .

[18]  S. M. Lo,et al.  Application of evolutionary neural network method in predicting pollutant levels in downtown area of Hong Kong , 2003, Neurocomputing.

[19]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[20]  Emilio Corchado,et al.  Soft computing models to identify typical meteorological days , 2011, Log. J. IGPL.

[21]  Gavin C. Cawley,et al.  A rigorous inter-comparison of ground-level ozone predictions , 2003 .

[22]  Emilio Corchado,et al.  A soft computing method for detecting lifetime building thermal insulation failures , 2010, Integr. Comput. Aided Eng..

[23]  Elisa Guerrero Vázquez,et al.  Multiple comparison procedures applied to model selection , 2002, Neurocomputing.

[24]  Álvaro Herrero,et al.  Neural visualization of network traffic data for intrusion detection , 2011, Appl. Soft Comput..

[25]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[26]  Yves Candau,et al.  Hourly ozone prediction for a 24-h horizon using neural networks , 2008, Environ. Model. Softw..

[27]  Georgios Grivas,et al.  Artificial neural network models for prediction of PM10 hourly concentrations, in the Greater Area of Athens, Greece , 2006 .

[28]  I. Turias,et al.  Prediction models of CO, SPM and SO2 concentrations in the Campo de Gibraltar Region, Spain: a multiple comparison strategy , 2008, Environmental monitoring and assessment.

[29]  Jorge Reyes,et al.  Prediction of PM2.5 concentrations several hours in advance using neural networks in Santiago, Chile , 2000 .

[30]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[31]  David G. Stork,et al.  Pattern Classification , 1973 .