Modelling sulphur dioxide levels of Konya city using artificial intelligent related to ozone, nitrogen dioxide and meteorological factors

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dc.contributor.authorDursun, S.
dc.contributor.authorKunt, F.
dc.contributor.authorTaylan, O.
dc.date.accessioned2020-03-26T19:06:21Z
dc.date.available2020-03-26T19:06:21Z
dc.date.issued2015
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractIncreasing 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.en_US
dc.description.sponsorshipSelcuk University (BAP)Selcuk University [BAP-10401028]en_US
dc.description.sponsorshipAuthors would like to thank Selcuk University (BAP) for the financial support of this study with Project Number BAP-10401028.en_US
dc.identifier.doi10.1007/s13762-015-0821-2en_US
dc.identifier.endpage3928en_US
dc.identifier.issn1735-1472en_US
dc.identifier.issn1735-2630en_US
dc.identifier.issue12en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage3915en_US
dc.identifier.urihttps://dx.doi.org/10.1007/s13762-015-0821-2
dc.identifier.urihttps://hdl.handle.net/20.500.12395/32330
dc.identifier.volume12en_US
dc.identifier.wosWOS:000365756300020en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSPRINGERen_US
dc.relation.ispartofINTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGYen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectAir pollutionen_US
dc.subjectAir qualityen_US
dc.subjectArtificial neural networken_US
dc.subjectEnvironmenten_US
dc.subjectFuzzy logicen_US
dc.titleModelling sulphur dioxide levels of Konya city using artificial intelligent related to ozone, nitrogen dioxide and meteorological factorsen_US
dc.typeArticleen_US

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