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Development of streamflow forecasting model using artificial neural network in the Awash River Basin, Ethiopia

Published Article / Early indication of possible drought can help in developing suitable drought mitigation strategies and measures in advance. Therefore, drought forecasting plays an important role in the planning and management of water resource in such circumstances. In this study, a non-linear streamflow forecasting model was developed using Artificial Neural Network (ANN) modeling technique at the Melka Sedi stream gauging station, Ethiopia, with adequate lead times. The available data was divided into two independent sets using a split sampling tool of the neural network software. The first data set was used for training and the second data set, which is normally about one fourth of the total available data, was used for testing the model. A one year data was set aside for validating the ANN model. The streamflow predicted using the model on weekly time step compared favorably with the measured streamflow data (R2 = 75%) during the validation period. Application of the model in assessing appropriate agricultural water management strategies for a large-scale irrigation scheme in the Awash River Basin, Ethiopia, has already been considered for publication in a referred journal.

Identiferoai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:cut/oai:ir.cut.ac.za:11462/332
Date January 2011
CreatorsEdossa, D.C., Babel, M.S.
ContributorsCentral University of Technology Free State Bloemfontein
PublisherInterim : Interdisciplinary Journal, Vol 10 , Issue 1: Central University of Technology Free State Bloemfontein
Source SetsSouth African National ETD Portal
Languageen_US
Detected LanguageEnglish
TypeArticle
Format457 053 bytes, 1 file, Application/PDF
RightsCentral University of Technology Free State Bloemfontein

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