Abstract:
Water is vital for life; however, water is a scarce natural resource that
is under serious threat of depletion. South Africa and indeed the Free State is a
water-scarce region, and facing growing challenges of delivering fresh and
adequate water to the people. In order to effectively manage surface water,
monitoring and predictions tools are required to inform decision makers on a
real-time basis. Artificial Neural Networks (ANNs) have proven that they can be
used to develop such prediction models and tools. This research makes use of
experimentation, prototyping and case study to develop, identify and evaluate
the ANN with best surface water level prediction capabilities. What ANN’s
techniques and algorithms are the most suitable for predicting surface water
levels given parameters such as water levels, precipitation, air temperature, wind
speed, wind direction? How accurately will the ANNs developed predict surface
water levels of the Modder River catchment area?
