A Prognostic Approach Based on Fuzzy-Logic Methodology to Forecast PM10 Levels in Khaldiya Residential Area, Kuwait

A prognostic approach is proposed based on a fuzzy-logic model to estimate suspended dust concentrations, related to PM10, in a specific residential area in Kuwait with high traffic and industrial influences. Seven input variables, including four important meteorological parameters (wind speed, wind direction, relative humidity and solar radiation) and the ambient concentrations of three gaseous pollutants (methane, carbon monoxide and ozone) were fuzzified using a sytem with a graphical user interface (GUI) and an artificial intelligence-based approach. Trapezoidal membership functions with ten and fifteen levels were employed for the fuzzy subsets of each model variable. A Mamdani-type fuzzy inference system (FIS) was developed to introduce a total of 146 rules in the IF-THEN format. The product (prod) and the centre of gravity (centroid) methods were performed as the inference operator and defuzzification methods, respectively, for the proposed FIS. The results obtained using uzzy-logic were compared with the outputs of an exponential regression model. The predictive performances of the models were compared based on various descriptive statistical indicators, and the proposed method was tested against additional observed data. The prognostic model presented in this work produced very small deviations from the actual results, and showed better predictive performance than the other model with regard to forecasting PM10 levels, with a very high determination coefficient of over 0.99.

[1]  Kaan Yetilmezsoy,et al.  Stochastic modeling applications for the prediction of COD removal efficiency of UASB reactors treating diluted real cotton textile wastewater , 2009 .

[2]  Hisham Ettouney,et al.  Analysis of ozone pollution in the Shuaiba industrial area in Kuwait , 2000 .

[3]  Gokmen Tayfur,et al.  FUZZY LOGIC MODEL FOR THE PREDICTION OF CEMENT COMPRESSIVE STRENGTH , 2004 .

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

[5]  Kaan Yetilmezsoy,et al.  A fuzzy-logic-based model to predict biogas and methane production rates in a pilot-scale mesophilic UASB reactor treating molasses wastewater. , 2010, Journal of hazardous materials.

[6]  Tahir Husain,et al.  Performance evaluation of slow sand filters using fuzzy rule-based modelling , 2004, Environ. Model. Softw..

[7]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[8]  G. Wolff,et al.  Measurements of SOx, NOx and aerosol species on Bermuda , 1986 .

[9]  Kaan Yetilmezsoy,et al.  Stochastic modeling approaches based on neural network and linear–nonlinear regression techniques for the determination of single droplet collection efficiency of countercurrent spray towers , 2007 .

[10]  Kaan Yetilmezsoy,et al.  Response surface modeling of Pb(II) removal from aqueous solution by Pistacia vera L.: Box-Behnken experimental design. , 2009, Journal of hazardous materials.

[11]  Archontoula Chaloulakou,et al.  Neural Network and Multiple Regression Models for PM10 Prediction in Athens: A Comparative Assessment , 2003, Journal of the Air & Waste Management Association.

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

[13]  Frank Klawonn,et al.  Design of Fuzzy Controllers , 2006 .

[14]  J. Legrand,et al.  Fuzzy logic modelling tracer response in milli torus reactor under aerated and non-aerated conditions , 2009 .

[15]  P. R. Adhikary,et al.  THE INFLUENCE OF METEOROLOGICAL CONDITIONS ON PM10 CONCENTRATIONS IN KATHMANDU VALLEY , 2008 .

[16]  I. Nolt,et al.  Chemical analysis of cloud water collected over Hawaii , 1982 .

[17]  Neil Rubens.  The Application of Fuzzy Logic to the Construction of the Ranking Function of Information Retrieval Systems , 2006, ArXiv.

[18]  Mehmet Cakmakci,et al.  Fuzzy logic modeling of the dissolved oxygen fluctuations in Golden Horn , 2005 .

[19]  P. Scheff,et al.  Defining the Photochemical Contribution to Particulate Matter in Urban Areas Using Time-Series Analysis , 2002, Journal of the Air & Waste Management Association.

[20]  Ali Elkamel,et al.  Measurement and prediction of ozone levels around a heavily industrialized area: a neural network approach , 2001 .

[21]  Michael Lipsett,et al.  Air Pollution and Exacerbation of Asthma in African-American Children in Los Angeles , 2001, Epidemiology.

[22]  Mikko T. Kolehmainen,et al.  Data exploration with self-organizing maps in environmental informatics and bioinformatics , 2004 .

[23]  K. Yetilmezsoy.  A new empirical model for the determination of the required retention time in hindered settling , 2007 .

[24]  Sameer Sharma,et al.  Neural Network Models for Air Quality Prediction: A Comparative Study , 2007 .

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

[26]  André Nel,et al.  ATMOSPHERE: Enhanced: Air Pollution-Related Illness: Effects of Particles , 2005 .

[27]  D. Dockery,et al.  Particulate air pollution as a predictor of mortality in a prospective study of U.S. adults. , 1995, American journal of respiratory and critical care medicine.

[28]  E. Patterson,et al.  Characterization of the Atlanta area aerosol, elemental composition and possible sources , 1986 .

[29]  Kaan Yetilmezsoy,et al.  Fuzzy-logic modeling of Fenton's oxidation of anaerobically pretreated poultry manure wastewater , 2012, Environmental Science and Pollution Research.

[30]  A. da Silva,et al.  Quantification of dust-forced heating of the lower troposphere , 1998, Nature.

[31]  L. Janssen,et al.  Analysis of spatial and temporal variations of PM 10 concentrations in the Netherlands using Kalman filtering , 2000 .

[32]  Hisham Ettouney,et al.  Predicting ozone levels , 1996, Environmental science and pollution research international.

[33]  K. Yetilmezsoy.  Determination of Optimum Body Diameter of Air Cyclones Using a New Empirical Model and a Neural Network Approach , 2006 .

[34]  Gary W. Heinke,et al.  Environmental Science and Engineering , 1989 .

[35]  A. B. Chelani,et al.  Predicting chaotic time series of PM10 concentration using artificial neural network , 2005 .

[36]  Gabriel Ibarra-Berastegi,et al.  Regression and multilayer perceptron-based models to forecast hourly O3 and NO2 levels in the Bilbao area , 2006, Environ. Model. Softw..

[37]  Jan Jantzen,et al.  Design Of Fuzzy Controllers , 1998 .

[38]  Christian Monn,et al.  Exposure assessment of air pollutants: a review on spatial heterogeneity and indoor/outdoor/personal exposure to suspended particulate matter, nitrogen dioxide and ozone , 2001 .

[39]  H. Hansson,et al.  Urban air pollution source apportionment using a combination of aerosol and gas monitoring techniques , 1996 .

[40]  J. Coakley,et al.  Climate Forcing by Anthropogenic Aerosols , 1992, Science.

[41]  S. M. Al-Salem,et al.  AN OVERVIEW OF THE PM10 POLLUTION PROBLEM IN FAHAHEEL URBAN AREA, KUWAIT , 2008 .

[42]  José David Martín-Guerrero,et al.  Neural networks for analysing the relevance of input variables in the prediction of tropospheric ozone concentration , 2006 .

[43]  Gabriel Ibarra-Berastegi,et al.  From diagnosis to prognosis for forecasting air pollution using neural networks: Air pollution monitoring in Bilbao , 2008, Environ. Model. Softw..

[44]  Serge Guillaume,et al.  Designing fuzzy inference systems from data: An interpretability-oriented review , 2001, IEEE Trans. Fuzzy Syst..

[45]  Ferhat Karaca,et al.  Traffic related PM predictor for Besiktas, Turkey , 2009, ITEE.

[46]  Kaan Yetilmezsoy,et al.  Modeling Water-in-Oil Emulsion Formation Using Fuzzy Logic , 2012, J. Multiple Valued Log. Soft Comput..

[47]  Osman Aytekin,et al.  The use of fuzzy logic in predicting house selling price , 2010, Expert Syst. Appl..

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

[49]  K. Wyat Appel,et al.  Evaluation of the Community Multiscale Air Quality (CMAQ) model version 4.5 : Sensitivities impacting model performance Part I-Ozone , 2007 .

[50]  H. D. Scott,et al.  Applications of fuzzy logic to the prediction of soil erosion in a large watershed , 1998 .

[51]  K. Yetilmezsoy,et al.  Development of empirical models for performance evaluation of UASB reactors treating poultry manure wastewater under different operational conditions. , 2008, Journal of hazardous materials.

[52]  M. Yassin,et al.  Assessment of airborne bacteria and fungi in an indoor and outdoor environment , 2010 .

[53]  K. Yetilmezsoy.  Composite desirability function‐based empirical modeling for packed tower design in physical ammonia absorption , 2012 .

[54]  V. Özdemir,et al.  Temperature prediction in a coal fired boiler with a fixed bed by fuzzy logic based on numerical solution , 2005 .

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

[56]  Ercan Oztemel,et al.  A neural network-based approach for the prediction of urban SO2 concentrations in the Istanbul metropolitan area , 2010 .

[57]  D. Oderbolz,et al.  One decade of parallel fine (PM 2.5 ) and coarse (PM 10 ???PM 2.5 ) particulate matter measurements in Europe: trends and variability , 2012 .

[58]  Claudio Carnevale,et al.  Neuro-fuzzy and neural network systems for air quality control , 2009 .

[59]  K. Bhaskaran,et al.  Cardiovascular consequences of air pollution: what are the mechanisms? , 2010, Heart.