Goz, EdaYuceer, MehmetKaradurmus, Erdal2024-08-042024-08-042019978-0-12-819939-8978-0-12-819939-81570-7946https://doi.org/10.1016/B978-0-12-818634-3.50149-1https://hdl.handle.net/11616/9888329th European Symposium on Computer-Aided Process Engineering (ESCAPE) -- JUN 16-19, 2019 -- Eindhoven, NETHERLANDSThis 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.eninfo:eu-repo/semantics/closedAccessArtificial Neural NetworkExtreme Learning MachineOn-line monitoringRiver water qualityConference Object4688989410.1016/B978-0-12-818634-3.50149-12-s2.0-85069737889Q4WOS:000495447200149N/A