Browsing by Author "Elmahdi, Reem"

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    Predicting water quality variables
    (Stellenbosch : Stellenbosch University., 2020-03) Elmahdi, Reem; Brink, Willie; Wilms, Josefine M.; Stellenbosch University. Faculty of Science. Department of Mathematical Sciences (Applied Mathematics).
    ENGLISH ABSTRACT: Water is an important substance for all of life, and can be used in domestic, agricultural and industrial activities. Water quality determines the usefulness of water for particular purposes, and can be defined in terms of time-varying water quality variables such as dissolved oxygen, turbidity, temperature, pH, specific conductance, chlorophylls, nitrate and salinity. Different mathematical and statistical models have been used for the prediction of time-series data. Machine learning can also be used when enough data is available. In particular, artificial neural networks (ANNs) have demonstrated success in solving such problems. They are conceptually simple and easily implemented. In this thesis,an overview of two ANN s tructures is presented for solving the problem of predicting water quality variables. Specifically, multilayer perceptrons (MLPs) and long short-term memory (LSTM) networks are presented. Experiments are conducted on Hog Island water quality variables and the results of the models are compared using various accuracy metrics like root mean squared error. It is found that LSTM performs better than MLP across most of the accuracy metrics.