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MULTITEMPORAL SCALE HYDROGRAPH PREDICTION USING ARTIFICIAL NEURAL NETWORKS 1
Author(s) -
Melesse Assefa M.,
Wang Xixi
Publication year - 2006
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/j.1752-1688.2006.tb06026.x
Subject(s) - hydrograph , artificial neural network , hydrology (agriculture) , hydrometeorology , mathematics , graph , perceptron , statistics , surface runoff , algorithm , meteorology , artificial intelligence , computer science , geology , physics , combinatorics , ecology , geotechnical engineering , biology , precipitation
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.