title: The improvement of total organic carbon forecasting using neural networks discharge model.
abstract: It is advantageous to take simultaneous measurements of discharge and water quality at the same station for real-time management of water quality. However, some of the continuous water quality monitoring stations can be located some distance from the water level stations; and the case of the Pyeongchang River is one such in South Korea. The major monitoring parameter in water quality is total organic carbon. In this study, an artificial neural network model was constructed for discharge prediction at the continuous water quality monitoring station. This model was connected to another model that forecasts total organic carbon. The connected system showed better results than the single model in the forecasting of total organic carbon.
authors.affiliation: Chungbuk National University, Cheongju, Korea.
authors.forename: I S
authors.initials: IS
authors.lastname: Yeon
authors.forename: K W
authors.initials: KW
authors.lastname: Jun
authors.forename: H J
authors.initials: HJ
authors.lastname: Lee
mesh_terms: D002244:Carbon / Q000032:analysis*; D016571:Neural Networks, Computer; D009930:Organic Chemicals / Q000032:analysis*; D045483:Rivers; D012621:Seasons; D014881:Water Supply / Q000032:analysis*
chemical_list: D009930:Organic Chemicals; D002244:Carbon