Application of artificial neural networks and regression models in the prediction of daily maximum PM10 concentration in Düzce, Tukey

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dc.contributor.authorTaşpınar, Fatih
dc.contributor.authorBozkurt, Zehra
dc.date.accessioned2020-04-30T13:32:08Z
dc.date.available2020-04-30T13:32:08Z
dc.date.issued2014
dc.departmentDÜ, Mühendislik Fakültesi, Çevre Mühendisliği Bölümüen_US
dc.description.abstractIncreasing levels of atmospheric particulate matter are known to adversely affect human health. Therefore, air quality predictions may provide important information in order to take actions for the public before the pollution happens. In this study, we presented artificial neural network (ANN), stepwise regression (SR) and multiple linear regression (MLR) models to forecast maximum daily PM1o concentrations one day ahead in Düzce, Turkey. Particularly, a special emphasis was put on the prediction of particulate levels during winter episodes. Inputs to the models include lagged values of maximum, minimum and standard deviations of PM1o concentrations, and some meteorological factors, which are all on daily basis. The output is the expected maximum concentration of PM10 in (?g.-3 for the following day. The data sets used in training and testing stages covered the daily averaged values of these variables for the period of 2011-2013. The results showed that selected inputs based on stepwise regression approach and use of cascading-training in multi-layer perceptron ANN (ANN-MLP) appeared to be promising with R2 up to 0.69 and index-of-agreement up to 0.79. It is concluded that local monitoring systems associated with ANN model predictions may be a sound way to develop embedded online systems for public health. © by PSP.en_US
dc.identifier.endpage2459en_US
dc.identifier.issn1018-4619
dc.identifier.issue10en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage2450en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12684/96
dc.identifier.volume23en_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherParlar Scientific Publicationsen_US
dc.relation.ispartofFresenius Environmental Bulletinen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial neural networks; Forecasting; Multiple regression; Particulate matter; Stepwise regressionen_US
dc.titleApplication of artificial neural networks and regression models in the prediction of daily maximum PM10 concentration in Düzce, Tukeyen_US
dc.typeArticleen_US

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