Browsing by Author "Steyn, Melise"

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    Short-term stream flow forecasting and downstream gap infilling using machine learning techniques
    (Stellenbosch : Stellenbosch University, 2018-03) Steyn, Melise; Smit, G. J. F.; Brink, Willie; Wilms, Josefine M.; Stellenbosch University. Faculty of Science. Dept. of Mathematical Sciences (Applied Mathematics)
    ENGLISH ABSTRACT : Stream flow is an important component in the hydrological cycle and plays a vital role in many hydrological applications. Accurate stream flow forecasts may be used for the study of various hydro-environmental aspects and may assist in reducing the consequences of floods. The utility of time series records for stream flow analyses is often dependent on continuous, uninterrupted observations. However, interruptions are often unavoidable and may negatively impact the sustainable management of water resources. This study proposes the application of machine learning techniques to address these hydrological challenges. The first part of this study focuses on single station short-term stream flow forecasting for river basins where historical time series data are available. Two machine learning techniques were investigated, namely support vector regression and multilayer perceptrons. Each model was trained on historical stream flow and precipitation data to forecast stream flow with a lead time of up to seven days. The Shoalhaven, Herbert and Adelaide rivers in Australia were considered for experimentation. The predictive performance of each model was determined by the Pearson correlation coefficient, the root mean squared error and the Nash-Sutcliffe efficiency, and the predictive capabilities of the models were compared to that of a physically based stream flow forecasting model currently supplied by the Australian Bureau of Meteorology. Based on the results, it was concluded that the machine learning models have the ability to overcome certain challenges faced by physically based models and the potential to be useful stream flow forecasting tools in river basin modelling. The second part of this study investigates the ability of support vector regression and multilayer perceptron models to infill incomplete stream flow records. The infilling techniques relied upon data from donor stations and rain gauges within close proximity to the station considered for infilling. A case study was conducted on a channel in the Goulburn basin in Australia. The results showed the promising role of machine learning applications for the infilling of gaps in stream flow records and indicated that data from donor stations contribute more to the success of these models compared to precipitation data.