An ANFIS-Based Air Quality Model for Prediction of SO2 Concentration in Urban Area

This paper presents the results of attempt to perform modeling of SO2 concentration in urban area in vicinity of copper smelter in Bor (Serbia), using ANFIS methodological approach. The aim of obtained model was to develop a prediction tool that will be used to calculate potential SO2 concentration, above prescribed limitation, based on input parameters. As predictors, both technogenic and meteorological input parameters were considered. Accordingly, the dependence of SO2 concentration was modeled as the function of wind speed, wind direction, air temperature, humidity and amount sulfur emitted from the pyrometallurgical process of sulfidic copper concentration treatment.

[1]  Saleh M. Al-Alawi,et al.  Combining principal component regression and artificial neural networks for more accurate predictions of ground-level ozone , 2008, Environ. Model. Softw..

[2]  Zsolt Csaba Johanyák,et al.  Fuzzy Model based Prediction of Ground-Level Ozone Concentration , 2011 .

[3]  B. Brunekreef,et al.  Air pollution and health , 2002, The Lancet.

[4]  Babak Nadjar Araabi,et al.  Uncertainty analysis of developed ANN and ANFIS models in prediction of carbon monoxide daily concentration , 2010 .

[5]  Gavin C. Cawley,et al.  Modelling SO2 concentration at a point with statistical approaches , 2004, Environ. Model. Softw..

[6]  J. Pezzuto,et al.  Cancer Chemopreventive Activity of Resveratrol , 2002, Drugs under experimental and clinical research.

[8]  G. Wong,et al.  Temporal relationship between air pollution and hospital admissions for asthmatic children in Hong Kong , 2001, Clinical and experimental allergy : journal of the British Society for Allergy and Clinical Immunology.

[9]  Saleh M. Al-Alawi,et al.  Assessment and prediction of tropospheric ozone concentration levels using artificial neural networks , 2002, Environ. Model. Softw..

[10]  Goksel Demir,et al.  Prediction of Tropospheric Ozone Concentration by Employing Artificial Neural Networks , 2008 .

[11]  H. Kan,et al.  Air pollution and daily mortality in Shanghai: a time-series study. , 2003, Archives of environmental health.

[12]  L. Chin,et al.  Telomere dysfunction induces metabolic and mitochondrial compromise , 2011, Nature.

[13]  G. Hoek,et al.  Is SO2 A Causative Factor for the PM-Associated Mortality Risks in the Netherlands? , 2000 .

[14]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[15]  H. Koren.  Associations between criteria air pollutants and asthma. , 1995, Environmental health perspectives.

[16]  O. Herbarth,et al.  Effect of sulfur dioxide and particulate pollutants on bronchitis in children—A risk analysis , 2001, Environmental toxicology.

[17]  Norman R. Farnsworth,et al.  Cancer Chemopreventive Activity of Resveratrol, a Natural Product Derived from Grapes , 1997, Science.

[18]  Feng Wan,et al.  Applying Ensemble Learning Techniques to ANFIS for Air Pollution Index Prediction in Macau , 2012, ISNN.

[19]  A. Biggeri,et al.  [Meta-analysis of the Italian studies on short-term effects of air pollution--MISA 1996-2002]. , 2004, Epidemiologia e prevenzione.

[20]  Živan Živković,et al.  Multi-criteria Analysis of Air Pollution with SO2 and PM10 in Urban Area Around the Copper Smelter in Bor, Serbia , 2009, Water, air, and soil pollution.

[21]  C. Chan,et al.  Air pollution in mega cities in China , 2008 .

[22]  R. Burnett,et al.  Spatial Analysis of Air Pollution and Mortality in Los Angeles , 2005, Epidemiology.

[23]  Atakan Yücel,et al.  An approach based on ANFIS input selection and modeling for supplier selection problem , 2011, Expert Syst. Appl..

[24]  Ivan Mihajlović,et al.  COMPARISON OF LINEAR AND NONLINEAR STATISTICS METHODS APPLIED IN INDUSTRIAL PROCESS MODELING PROCEDURE , 2010 .

[25]  Mehmet Cakmakci,et al.  ARTIFICIAL INTELLIGENCE-BASED PREDICTION MODELS FOR ENVIRONMENTAL ENGINEERING , 2011 .

[26]  S. M. Nair,et al.  Prediction of daily air pollution using wavelet decomposition and Adaptive Network-based fuzzy inference system , 2011 .

[27]  Mahmut Bayramoglu,et al.  Adaptive neuro-fuzzy based modelling for prediction of air pollution daily levels in city of Zonguldak. , 2006, Chemosphere.

[28]  L. Friberg.  [Air pollution]. , 1984, Svenska lakartidningen.

[29]  Oguz Kaynar,et al.  Multiple regression, ANN (RBF, MLP) and ANFIS models for prediction of swell potential of clayey soils , 2010, Expert Syst. Appl..

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

[31]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..

[32]  Joel Schwartz,et al.  Air pollution and child respiratory health: a case-crossover study in Australia and New Zealand. , 2005, American journal of respiratory and critical care medicine.

[33]  Vinod Kumar Jain,et al.  A new scheme to predict chaotic time series of air pollutant concentrations using artificial neural network and nearest neighbor searching , 2008 .

[34]  J. Bell,et al.  A risk assessment of potential agricultural losses due to ambient SO2 in the central regions of Chile , 2001 .

[35]  Francesco Carlo Morabito,et al.  Fuzzy neural identification and forecasting techniques to process experimental urban air pollution data , 2003, Neural Networks.

[36]  Rodolfo E. Pérez-Roa,et al.  Air-pollution modelling in an urban area : Correlating turbulent diffusion coefficients by means of an artificial neural network approach , 2006 .

[37]  WHO Air quality guidelines for particulate matter, ozone, nitrogen dioxide and sulfur dioxide , 2006 .

[38]  E. Mantilla,et al.  Influence of local and regional Mediterranean meteorology on SO₂ ground-level concentrations in SE Spain. , 2011, Journal of environmental monitoring : JEM.

[39]  F. Zeng,et al.  Frequency changes in a continuous tone: Auditory cortical potentials , 2008, Clinical Neurophysiology.

[40]  K. Andolsek,et al.  Risk assessment , 2003, Nature.