Total Organic Carbon Prediction with Artificial Intelligence Techniques

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dc.authoridYuceer, Mehmet/0000-0002-2648-3931
dc.authoridGoz, Eda/0000-0002-3111-9042
dc.authorwosidYuceer, Mehmet/E-5110-2012
dc.authorwosidGoz, Eda/AAH-3388-2020
dc.contributor.authorGoz, Eda
dc.contributor.authorYuceer, Mehmet
dc.contributor.authorKaradurmus, Erdal
dc.date.accessioned2024-08-04T20:46:05Z
dc.date.available2024-08-04T20:46:05Z
dc.date.issued2019
dc.departmentİnönü Üniversitesien_US
dc.description29th European Symposium on Computer-Aided Process Engineering (ESCAPE) -- JUN 16-19, 2019 -- Eindhoven, NETHERLANDSen_US
dc.description.abstractThis study used the Extreme Learning Machine (ELM), Kernel Extreme Learning Machine (KELM) and Artificial Neural Network (ANN) models with a feed-forward neural network structure and partial least squares (PLSR) methods to estimate total organic carbon. In order to develop models, on-line data measured at five-minute time intervals were collected through one year (2007-2008) from the online-monitoring stations which were built near the River Yesil1rmak in Amasya in North-Eastern Turkey. These stations were the first practice in Turkey. Twelve parameters as luminescent dissolved oxygen (LDO), pH, conductivity, nitrate nitrogen (NO3-N), ammonium nitrogen (NH4-N), total organic carbon (TOC), chloride, orthophosphate, temperature, turbidity, suspended solid and flow rate were measured at the on-line monitoring stations. To predict the total organic carbon, four input variables, pH, conductivity, dissolved oxygen and temperature were selected. Moreover, the data were also collected at the central office in Ankara via a General Packet Radio Service (GPRS) channel. The validity of models was tested by using statistical methods in MATLAB including correlation coefficients (R), mean absolute percentage error (MAPE%) and root mean square error (RMSE). The best result was obtained in the presence of KELM with a radial basis function (RBF) kernel. R-test=0.984, MAPE(test)=3.01, RMSEtest=0.9676. Additionally, R-train=0.995, MAPE(train)=1.58 and RMSEtrain=0.532. Among the other two algorithms ANN provided better results than ELM and PLSR.en_US
dc.description.sponsorshipEuropean Federat Chem Engn, CAPE Working Party,Nederland Procestechnologie,Proc Syst Engn NL,Eindhoven Univ Technol,Delft Univ Technol,Univ Twente,Upfield,Univ Bremen,Univ Manchester,Hong Kong Univ Sci & Technolen_US
dc.description.sponsorshipTUBITAK [105G002]en_US
dc.description.sponsorshipThis study was supported by TUBITAK (Project number: 105G002).en_US
dc.identifier.doi10.1016/B978-0-12-818634-3.50149-1
dc.identifier.endpage894en_US
dc.identifier.isbn978-0-12-819939-8
dc.identifier.isbn978-0-12-819939-8
dc.identifier.issn1570-7946
dc.identifier.scopus2-s2.0-85069737889en_US
dc.identifier.scopusqualityQ4en_US
dc.identifier.startpage889en_US
dc.identifier.urihttps://doi.org/10.1016/B978-0-12-818634-3.50149-1
dc.identifier.urihttps://hdl.handle.net/11616/98883
dc.identifier.volume46en_US
dc.identifier.wosWOS:000495447200149en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Science Bven_US
dc.relation.ispartof29th European Symposium on Computer Aided Process Engineering, Pt Aen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtificial Neural Networken_US
dc.subjectExtreme Learning Machineen_US
dc.subjectOn-line monitoringen_US
dc.subjectRiver water qualityen_US
dc.titleTotal Organic Carbon Prediction with Artificial Intelligence Techniquesen_US
dc.typeConference Objecten_US

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