Predicting and forecasting flow discharge at sites receiving significant lateral inflow |
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| Authors: | Gokmen Tayfur Tommaso Moramarco Vijay P Singh |
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| Institution: | 1. Department Civil Engineering, Izmir Institute of Technology, Urla, Izmir, 35340, Turkey;2. Research Institute for Hydrogeological Protection, National Research Council, Via Madonna Alta, 126, 06128 Perugia, Italy;3. Department of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA 70803, USA |
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| 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. Copyright © 2007 John Wiley & Sons, Ltd. |
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| Keywords: | rating curve ANN feed‐forward back propagation modified Muskingum method prediction forecasting flood hydrograph flow stage lead time |
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