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?
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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van Vuuren, J.J., Masinde, M., Luwes, N. (2019). Development of an Artificial Neural Network Model for Predicting Surface Water Level: Case of Modder River Catchment Area. In: Zitouni, R., Agueh, M. (eds) Emerging Technologies for Developing Countries. AFRICATEK 2018. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 260. Springer, Cham. https://doi.org/10.1007/978-3-030-05198-3_8
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DOI: https://doi.org/10.1007/978-3-030-05198-3_8
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