Abdellatif, M, Osman, Y and Elkhidir, A (2015) Comparison of Artificial Neural Networks and Autoregressive Model to Forecast Inflows to Roseires Reservoir for better Prediction of Irrigation Water Supply in the Sudan. International Journal of River Basin Management, 13 (2). pp. 203-214. ISSN 1571-5124

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Abstract

The Blue Nile River is utilized in Sudan as the main source of irrigation water. However, the river has a long, dry, low-flow season (October–May), which necessitates the use of regulations and rules to manage its water use during this period. This depends on the use of accurate lead time forecasts of inflows to the reservoirs built along the river. Thus a reliable and tested forecasting tool is needed to provide inflow forecast, with sufficient lead time. In the present study, artificial neural network (ANN) is used to model the recession curve of the flow hydrograph at El-Deim gauging station, which subsequently is used as inflows to the Roseires Reservoir on the Blue Nile River. Different scenarios of ANN have been tested to forecast 23 10-day mean discharges during the recession period and their performances were assessed. Results from the optimal ANN model were compared to those simulated with an autoregressive (AR1) model to check their accuracy. Modelling results showed that the ANN model developed is capable of accurately forecasting the inflows to the Roseires Reservoir and outperforms the AR1 model. It has then proposed for use in operation of the reservoir for purposes of predicting irrigation water supply.

Item Type:	Article
Additional Information:	This is an Accepted Manuscript of an article published by Taylor & Francis Group in International Journal of River Basin Management on 09/02/2015, available online: http://dx.doi.org/10.1080/15715124.2014.1003381
Uncontrolled Keywords:	0799 Other Agricultural And Veterinary Sciences
Subjects:	G Geography. Anthropology. Recreation > GB Physical geography G Geography. Anthropology. Recreation > GE Environmental Sciences
Divisions:	Civil Engineering & Built Environment
Publisher:	Taylor and Francis
Date Deposited:	19 Oct 2015 14:04
Last Modified:	04 Sep 2021 13:52
URI:	https://researchonline.ljmu.ac.uk/id/eprint/2206

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