Artificial Neural Networks for Forecasting Watershed Runoff and Stream Flows
Publication: Journal of Hydrologic Engineering
Volume 10, Issue 3
Abstract
This research demonstrates an application of artificial neural networks (ANN) for watershed-runoff and stream-flow forecasts. A watershed runoff prediction model was developed to predict stormwater runoff at a gauged location near the watershed outlet. Another stream flow forecasting model was formulated to forecast river flows at downstream locations along the same channel. Input data for both models include the current and preceding records of rainfall and stream flow gathered at the watershed outlet and downstream locations. Computational algorithms for both models were based on a commercially available software. A case study was conducted on a small urban watershed in Greensboro, North Carolina. These two ANN-hydrologic forecasting models were successfully applied to provide near-real-time- and near-term-flow predictions with lead times starting from the present time and advancing to a few hours later on increments. An important aspect of this research has been the development of methodology for input data organization, model performance evaluation, and ANN processing techniques. Encouraging results obtained indicate that ANN-hydrologic forecasting models can be considered an alternate and practical tool for stream-flow forecast, which is particularly useful for assisting small urban watersheds to issue timely and early flood warnings.
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Acknowledgments
The writers thank the Division of Storm Water Services of the City of Greensboro for their technical and financial support, remarkable comments and revisions suggested by the manuscript reviewers, and advice from Professor Robert C. Borden. The U.S. Geological Survey assisted in rainfall and stream data acquisition. The project was funded in part through a grant provided by the N.C. Water Resources Research Institute.
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© 2005 ASCE.
History
Received: Aug 29, 2002
Accepted: Aug 12, 2004
Published online: May 1, 2005
Published in print: May 2005
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