Paper No. 05098 of the Journal of the American Water Resources Association (JAWRA) (Copyright © 2006). Discussions are open until June 1, 2007.
MULTITEMPORAL SCALE HYDROGRAPH PREDICTION USING ARTIFICIAL NEURAL NETWORKS1
Article first published online: 10 AUG 2007
JAWRA Journal of the American Water Resources Association
Volume 42, Issue 6, pages 1647–1657, December 2006
How to Cite
Melesse, A. M. and Wang, X. (2006), MULTITEMPORAL SCALE HYDROGRAPH PREDICTION USING ARTIFICIAL NEURAL NETWORKS1. JAWRA Journal of the American Water Resources Association, 42: 1647–1657. doi: 10.1111/j.1752-1688.2006.tb06026.x
- Issue published online: 10 AUG 2007
- Article first published online: 10 AUG 2007
- artificial neural network;
- Devils Lake;
- Red River;
- flood forecasting;
- lake stage prediction;
- temporal scale
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.