Show simple item record

dc.contributor.authorİnal, Fikret
dc.date.accessioned2016-12-22T07:46:07Z
dc.date.available2016-12-22T07:46:07Z
dc.date.issued2010-10
dc.identifier.citationİnal, F. (2010). Artificial neural network prediction of tropospheric ozone concentrations in Istanbul, Turkey. Clean - Soil, Air, Water, 38(10), 897-908. doi:10.1002/clen.201000138en_US
dc.identifier.issn1863-0650
dc.identifier.urihttp://doi.org/10.1002/clen.201000138
dc.identifier.urihttp://hdl.handle.net/11147/2648
dc.description.abstractTropospheric (ground-level) ozone has adverse effects on human health and environment. In this study, next day's maximum 1-h average ozone concentrations in Istanbul were predicted using multi-layer perceptron (MLP) type artificial neural networks (ANNs). Nine meteorological parameters and nine air pollutant concentrations were utilized as inputs. The total 578 datasets were divided into three groups: training, cross-validation, and testing. When all the 18 inputs were used, the best performance was obtained with a network containing one hidden layer with 24 neurons. The transfer function was hyperbolic tangent. The correlation coefficient (R), mean absolute error (MAE), root mean squared error (RMSE), and index of agreement or Willmott's Index (d2) for the testing data were 0.90, 8.78 μg/m3, 11.15μg/m3, and 0.95, respectively. Sensitivity analysis has indicated that the persistence information (current day's maximum and average ozone concentrations), NO concentration, average temperature, PM10, maximum temperature, sunshine time, wind direction, and solar radiation were the most important input parameters. The values of R, MAE, RMSE, and d2 did not change considerably for the MLP model using only these nine inputs. The performances of the MLP models were compared with those of regression models (i.e., multiple linear regression and multiple non-linear regression). It has been found that there was no significant difference between the ANN and regression modeling techniques for the forecasting of ozone concentrations in Istanbul. Tropospheric ozone has adverse effects on human health and environment. Here, the next-day's maximum 1-h average ozone concentrations in Istanbul were predicted using multi-layer perceptron type artificial neural networks (MLP-ANNs). The MLP-ANNs were compared to multiple linear and multiple non-linear regression models. Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.en_US
dc.description.sponsorshipIzmir Institute of Technologyen_US
dc.language.isoengen_US
dc.publisherWiley-VCH Verlagen_US
dc.relation.isversionof10.1002/clen.201000138en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial neural networksen_US
dc.subjectMulti-layer perceptronen_US
dc.subjectOzoneen_US
dc.subjectRegressionen_US
dc.subjectIstanbulen_US
dc.titleArtificial neural network prediction of tropospheric ozone concentrations in Istanbul, Turkeyen_US
dc.typearticleen_US
dc.contributor.authorIDTR30587en_US
dc.contributor.iztechauthorİnal, Fikret
dc.relation.journalClean - Soil, Air, Wateren_US
dc.contributor.departmentIzmir Institute of Technology. Chemical Engineeringen_US
dc.identifier.volume38en_US
dc.identifier.issue10en_US
dc.identifier.startpage897en_US
dc.identifier.endpage908en_US
dc.identifier.wosWOS:000284680200001
dc.identifier.scopusSCOPUS:2-s2.0-85017434067
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record