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Development of an Artificial Neural Network Model for Predicting Surface Water Level: Case of Modder River Catchment Area

Conference paper
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Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 260)

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?

Keywords

Artificial Neural Networks Modder River Free State Surface water Predication and monitoring 

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2019

Authors and Affiliations

  1. 1.Unit for Research in Informatics for Droughts in AfricaCentral University of TechnologyBloemfonteinSouth Africa

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