Neural Networks and Wavelet Conjunction Model for Intermittent Streamflow Forecasting
Publication: Journal of Hydrologic Engineering
Volume 14, Issue 8
Abstract
Intermittent streamflow estimates are important for water quality management, planning water supplies, hydropower, and irrigation systems. This paper proposes the application of a conjunction model (neurowavelet) for forecasting daily intermittent streamflow. The neurowavelet conjunction model is improved by combining two methods, discrete wavelet transform and artificial neural networks (ANN), for ahead streamflow forecasting and results are compared with those of the single ANN model. Intermittent streamflow data from two stations in the Thrace Region, the European part of Turkey, in the northwest part of the country are used in the study. The comparison results revealed that the suggested model could significantly increase the forecast accuracy of single ANN in forecasting daily intermittent streamflows. The neurowavelet conjunction model reduced the prediction root mean square errors and mean absolute errors with respect to the single ANN model by 74–65% and 43–12%, and increased the determination coefficient by 47–11%, respectively.
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Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 14 • Issue 8 • August 2009
Pages: 773 - 782
Copyright
© 2009 ASCE.
History
Received: Jul 8, 2008
Accepted: Nov 17, 2008
Published online: Feb 19, 2009
Published in print: Aug 2009
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