Total Organic Carbon Prediction with Artificial Intelligence Techniques

Basit Öğe Kaydı
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.accessioned2021-11-01T15:02:56Z
dc.date.available2021-11-01T15:02:56Z
dc.date.issued2019
dc.department[Belirlenecek]
dc.description29th European Symposium on Computer-Aided Process Engineering (ESCAPE) -- JUN 16-19, 2019 -- Eindhoven, NETHERLANDS
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.
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.sponsorshipTUBITAKTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [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; 978-0-12-819939-8
dc.identifier.issn1570-7946
dc.identifier.scopus2-s2.0-85069737889
dc.identifier.scopusqualityQ4
dc.identifier.startpage889en_US
dc.identifier.urihttps://doi.org/10.1016/B978-0-12-818634-3.50149-1
dc.identifier.urihttps://hdl.handle.net/11491/6862
dc.identifier.volume46en_US
dc.identifier.wosWOS:000495447200149
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthor[Belirlenecek]
dc.language.isoen
dc.publisherElsevier Science Bv
dc.relation.ispartof29Th European Symposium On Computer Aided Process Engineering, Pt A
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
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 Techniques
dc.typeConference Object

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu