Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/5529
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dc.contributor.authorBayram, Adem-
dc.contributor.authorKankal, Murat-
dc.contributor.authorTayfur, Gökmen-
dc.contributor.authorÖnsoy, Hızır-
dc.date.accessioned2017-05-17T06:40:47Z-
dc.date.available2017-05-17T06:40:47Z-
dc.date.issued2014-04-
dc.identifier.citationBayram, A., Kankal, M., Tayfur, G., and Önsoy, H. (2014). Prediction of suspended sediment concentration from water quality variables. Neural Computing and Applications, 24(5), 1079-1087. doi:10.1007/s00521-012-1333-3en_US
dc.identifier.issn0941-0643-
dc.identifier.issn1433-3058-
dc.identifier.urihttps://doi.org/10.1007/s00521-012-1333-3-
dc.identifier.urihttp://hdl.handle.net/11147/5529-
dc.description.abstractThis study investigates use of water quality (WQ) variables, namely total chromium concentration, total iron concentration, and turbidity for predicting suspended sediment concentration (SSC). For this purpose, the artificial neural networks (ANNs) and regression analysis (RA) models are employed. Seven different RA models are constructed, considering the functional relation between measured WQ variables and SSC. The WQ and SSC data are fortnightly obtained from six monitoring stations, located on the stream Harsit, Eastern Black Sea Basin, Turkey. A total of 132 water samples are collected from April 2009 to February 2010. Model prediction results reveal that ANN is able to predict SSC from WQ data, with mean absolute error (MAE) of 10.30 mg/L and root mean square error (RMSE) of 13.06 mg/L. Among seven RA models, the best one, which has the form including all independent parameters, produces results comparable to those of ANN, with MAE = 14.28 mg/L and RMSE = 15.35 mg/L. The sensitivity analysis results reveal that the most effective parameter on the SSC is total chromium concentration. These results have time- and cost-saving implications.en_US
dc.description.sponsorshipKaradeniz (Black Sea) Technical University (2007.118.01.2)en_US
dc.language.isoenen_US
dc.publisherSpringer Verlagen_US
dc.relation.ispartofNeural Computing and Applicationsen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial neural networksen_US
dc.subjectRegression analysisen_US
dc.subjectStream Harsiten_US
dc.subjectSuspended sediment concentrationen_US
dc.subjectTotal chromiumen_US
dc.subjectTotal ironen_US
dc.titlePrediction of suspended sediment concentration from water quality variablesen_US
dc.typeArticleen_US
dc.authoridTR2054en_US
dc.institutionauthorTayfur, Gökmen-
dc.departmentİzmir Institute of Technology. Civil Engineeringen_US
dc.identifier.volume24en_US
dc.identifier.issue5en_US
dc.identifier.startpage1079en_US
dc.identifier.endpage1087en_US
dc.identifier.wosWOS:000332955900009en_US
dc.identifier.scopus2-s2.0-84900639299en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1007/s00521-012-1333-3-
dc.relation.doi10.1007/s00521-012-1333-3en_US
dc.coverage.doi10.1007/s00521-012-1333-3en_US
dc.identifier.wosqualityQ2-
dc.identifier.scopusqualityQ2-
item.openairetypeArticle-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextopen-
item.fulltextWith Fulltext-
item.languageiso639-1en-
item.cerifentitytypePublications-
crisitem.author.dept03.03. Department of Civil Engineering-
Appears in Collections:Civil Engineering / İnşaat Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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