Evaluating the use of neural networks to predict river flow gauge values

Without improved water management the global population could be facing serious water shortages. River flow discharge rates are one factor that could contribute to improving water management, being able to predict a forecasted river flow value would provide support in the management of water resources. This research investigates the use of an artificial neural network (ANN) to create a model that predicts river flow gauge values. The Driel Barrage monitoring station on the Thukela river in South Africa was used as a case study. The research makes use of data from the Department of Water and Sanitation (DWS) and weather forecast data from the European Center For Medium- Range Forecasts (ECMWF) to train the predictive model. An evaluation of the ANN model identified that the model is highly sensitive to selected weather parameters and is sensitive to the initial weights used in the ANN. These were overcome using an ANN ensemble and selective scenarios to identify the best weather parameters to use as input into the ANN model. Five weather parameters and a correlation coefficient cut-off value produced the most accurate prediction by the ANN. The research found that ANNs can be used for predicting river flow gauge values but to improve the results a greater ensemble, additional data and different ANN structures may create a better performing model. For the ANN model to be used in practice the research needs to be extended to evaluate the whole catchment area and a range of rivers in South Africa.

Grantor dc:publisher

University of Pretoria

Year dc:date.issued

2017

Advisor dc:contributor.advisor

• Coetzee, Serena Martha

• River Flow gauge value
• Artificial Neural Network
• Thukela
• UCTD

Statement dc:rights

• © 2017 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria.

Language dc:language.iso

en

Dc Identifier Other

S2017

OAI identifier oai:identifier

oai:repository.up.ac.za:2263/63361