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Title: Predicting and forecasting flow discharge at sites receiving significant lateral inflow
Authors: Tayfur, Gökmen
Moramarco, Tommaso
Singh, Vijay P.
Keywords: Artificial neural networks
Feed-forward back propagation
Flood hydrograph
Modified Muskingum method
Publisher: John Wiley and Sons Inc.
Source: Tayfur, G., Moramarco, T., and Singh, V. P. (2007). Predicting and forecasting flow discharge at sites receiving significant lateral inflow. Hydrological Processes, 21(14), 1848-1859. doi:10.1002/hyp.6320
Abstract: Two models, one linear and one non-linear, were employed for the prediction of flow discharge hydrographs at sites receiving significant lateral inflow. The linear model is based on a rating curve and permits a quick estimation of flow at a downstream site. The non-linear model is based on a multilayer feed-forward back propagation (FFBP) artificial neural network (ANN) and uses flow-stage data measured at the upstream and downstream stations. ANN predicted the real-time storm hydrographs satisfactorily and better than did the linear model. The results of sensitivity analysis indicated that when the lateral inflow contribution to the channel reach was insignificant, ANN, using only the flow-stage data at the upstream station, satisfactorily predicted the hydrograph at the downstream station. The prediction error of ANN increases exponentially with the difference between the peak discharge used in training and that used in testing. ANN was also employed for flood forecasting and was compared with the modified Muskingum model (MMM). For a 4-h lead time, MMM forecasts the floods reliably but could not be applied to reaches for lead times greater than the wave travel time. Although ANN and MMM had comparable performances for an 8-h lead time, ANN is capable of forecasting floods with lead times longer than the wave travel time.
ISSN: 0885-6087
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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