A prognostic approach based on fuzzy-logic methodology to forecast PM10 levels in Khaldiya residential area, Kuwait

Kaan Yetilmezsoy, Sabah Ahmed Abdul-Wahab

Research output: Contribution to journalArticle

19 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)1217-1236
Number of pages20
JournalAerosol and Air Quality Research
Volume12
Issue number6
DOIs
Publication statusPublished - 2012

Fingerprint

fuzzy mathematics
Fuzzy logic
methodology
Fuzzy inference
artificial intelligence
Ozone
Methane
Carbon Monoxide
Membership functions
Graphical user interfaces
Solar radiation
carbon monoxide
Carbon monoxide
wind direction
Artificial intelligence
Dust
forecast
residential area
relative humidity
solar radiation

Keywords

  • Fuzzy-logic model
  • Kuwait
  • Multiple regression
  • Particulate matter
  • PM

ASJC Scopus subject areas

  • Environmental Chemistry
  • Pollution

Cite this

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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.",
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AB - 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.

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