MULTITEMPORAL SCALE HYDROGRAPH PREDICTION USING ARTIFICIAL NEURAL NETWORKS1 |
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Authors: | Assefa M Melesse Xixi Wang |
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Institution: | Respectively, Assistant Professor, Department of Environmental Studies, ECS 339, Florida International University, 11200 SW 8th Street, Miami, Florida 33199 (formerly at Upper Midwest Aerospace Consortium, University of North Dakota, Grand Forks, North Dakota);and Research Scientist, Environmental and Energy Research Center, University of North Dakota 15 North 23rd Street, Grand Forks, North Dakota 58202-9018 (E-Mail/Melesse: ). |
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Abstract: | Abstract: An artificial neural network (ANN) provides a mathematically flexible structure to identify complex nonlinear relationship between inputs and outputs. A multilayer perceptron ANN technique with an error back propagation algorithm was applied to a multitime-scale prediction of the stage of a hydro-logically closed lake, Devils Lake (DL), and discharge of the Red River of the North at Grand Forks station (RR-GF) in North Dakota. The modeling exercise used 1 year (2002), 5 years (1998–2002), and 27 years (1975–2002) of data for the daily, weekly, and monthly predictions, respectively. The hydrometeorological data (precipitations P(t), P(t-1), P(t-2), P(t-3), antecedent runoff/lake stage R(t-1) and air temperature T(t) were partitioned for training and for testing to predict the current hydro-graph at the selected DL and RR-GF stations. Performance of ANN was evaluated using three combinations of daily datasets (Input I = P(t)), P(t-l), P(t-2), P(t-3), T(t) and R(t-l); Input II = Input-l less P(t) P(t-l), P(t-2), P(t-3); and Input III = Input-II less T(t)). Comparison of the model output using Input I data with the observed values showed average testing prediction efficiency (E) of 86 percent for DL basin and 46 percent for RR-GF basin, and higher efficiency for the daily than monthly simulations. |
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Keywords: | artificial neural network Devils Lake Red River flood forecasting lake stage prediction temporal scale |
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