River Discharges Forecasting In Northern Iraq Using Different ANN Techniques

Taymoor Awchi ()

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2014, vol. 28, issue 3, 814 pages

Abstract: The Upper and Lower Zab Rivers are two of main and most important tributaries of Tigris River in Northern Iraq region. They supply Tigris River with more than 40 % of its yield. The forecasting of flows for these rivers is very important in operation of the existing Dokan Dam on the Lower Zab River and the proposed Bakhma Dam on the Upper Zab River for flood mitigation and also in drought periods. Three types of Artificial Neural Networks (ANNs) are investigated and evaluated for flow forecasting of both rivers. The ANN techniques are the feedforward neural networks (FFNN), generalized regression neural networks (GRNN), and the radial basis function neural networks (RBF). The networks’ performance varied with different cases involved in the study; however, the FFNN was almost better than other networks. The effect of including a time index within the inputs of the networks is investigated. In addition, the ANNs’ performance is investigated in forecasting the high and low peaks and in forecasting river flows using the data of the other river. Copyright Springer Science+Business Media Dordrecht 2014

Keywords: Forecasting; ANNs; River flow; Peak flow; Zab River (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (17)

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Persistent link: https://EconPapers.repec.org/RePEc:spr:waterr:v:28:y:2014:i:3:p:801-814

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DOI: 10.1007/s11269-014-0516-3

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