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

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Volume: 12 | Issue: 6 | Pages: 1217-1236
DOI: 10.4209/aaqr.2012.07.0163
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Kaan Yetilmezsoy 1, Sabah Ahmed Abdul-Wahab2

  • 1 Department of Environmental Engineering, Faculty of Civil Engineering, Yildiz Technical University, 34220, Davutpasa, Esenler, Istanbul, Turkey
  • 2 Department of Mechanical and Industrial Engineering, College of Engineering, Sultan Qaboos University, P.O. Box 33, Al-Khod, Postal Code 123, Muscat, Sultanate of Oman

Abstract

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.

Keywords

Particulate matter PM10 Fuzzy-logic model Multiple regression Kuwait


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