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. / Dissertation (MSc)--University of Pretoria, 2017. / Geography, Geoinformatics and Meteorology / MSc / Unrestricted
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:up/oai:repository.up.ac.za:2263/63361 |
| Date | January 2017 |
| Creators | Walford, Wesley Michael |
| Contributors | Coetzee, Serena Martha, wwalford@gmail.com, Van Zyl, Terence L. |
| Publisher | University of Pretoria |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| 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. |
Page generated in 0.0025 seconds