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International Journal of Environment Science and Technology
Center for Environment and Energy Research and Studies (CEERS)
ISSN: 1735-1472 EISSN: 1735-1472
Vol. 12, No. 12, 2015, pp. 3915-3928
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Bioline Code: st15369
Full paper language: English
Document type: Research Article
Document available free of charge
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International Journal of Environment Science and Technology, Vol. 12, No. 12, 2015, pp. 3915-3928
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Modelling sulphur dioxide levels of Konya city using artificial intelligent related to ozone, nitrogen dioxide and meteorological factors
Dursun, S.; Kunt, F. & Taylan, O.
Abstract
Increasing industrial developments increased
the environmental pollution problems in many cities of the
world. Air quality modelling and indexes are used to introduce
the information on local air quality indicators in
polluted regions. Estimation and monitoring of air quality
in the city centres are important due to environmental
health and comfort of human-related topics. Air quality
approximation is a complicate subject that artificial intelligent
techniques are successfully used for modelling the
complicated and nonlinear approximation problems. In
present study, artificial neural network and an adaptive
neuro-fuzzy logic method developed to approximate the
impact of certain environmental conditions on air quality
and sulphur dioxide pollution level and used with this study
in Konya city centre. Data of sulphur dioxide concentrations
were collected from 15 selected points of Konya city
for prediction of air quality. Using air quality standards, air
quality was discussed by considering the sulphur dioxide
concentration as independent variables with meteorological
parameters. Different meteorological parameters were used
for investigation of pollution relation. One of the important
modelling tools, adaptive network-based fuzzy inference
system model, was used to assess performance by a number
of checking data collected from different sampling stations
in Konya. The outcomes of adaptive network-based fuzzy
inference system model was evaluated by fuzzy quality
charts and compared to the results obtained from Turkey
and Environmental Protection Agency air quality standards.
From the present results, fuzzy rule-based adaptive
network-based fuzzy inference system model is a valuable
tool prediction and assessment of air quality and tends to
propagate accurate results.
Keywords
Air pollution; Air quality; Artificial neural network; Environment; Fuzzy logic
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