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Research Publications (Engineering and Built Environment)

Permanent URI for this collectionhttp://ir-dev.dut.ac.za/handle/10321/215

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Author: search.filters.author.Oyebode, Oluwaseun Kunle

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    Review of three data- driven modelling techniques for hydrological modelling and forecasting
    (PSP, 2014) Oyebode, Oluwaseun Kunle; Otieno, Fredrick Alfred O.; Adeyemo, Josiah
    Various modelling techniques have been proposed and applied for modelling and forecasting of hydrological sys-tems in different studies. These modelling techniques are majorly categorized into two namely, process-based and data-driven modelling techniques. While the process-based techniques provides detailed description of hydro-logical processes, data-driven techniques however de-scribe the behaviour of hydrological processes by taking into account only limited assumptions about the underly-ing physics of the system being modelled. Although, process-based techniques have been widely applied in numerous hydrological modelling studies, the application of data-driven modelling techniques on the other hand has not been fully embraced in the hydrological domain. This paper provides a comprehensive review of several stud-ies relating to three data-driven modelling techniques namely, K-Nearest Neighbours (K-NN), Model Trees (MTs) and Fuzzy Rule-Based Systems (FRBS). Modern trends with respect to their applications in hydrological model-ling and forecasting studies are also discussed. The struc-ture of this review encapsulates an introduction to each of the modelling techniques, their applications in hydrological modelling and forecasting, identification of areas of con-cern in their use, performance improvement methods, as well as summary of their advantages and disadvantages. The review aims to make a case for the application of data-driven modelling techniques by discussing the benefits em-bedded in its integration into water resources applications.
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    Comparison of two data-driven modelling techniques for long-term streamflow prediction using limited datasets
    (SCIELO, 2015-09) Oyebode, Oluwaseun Kunle; Adeyemo, Josiah; Otieno, Fredrick Alfred O.
    This paper presents an investigation into the efficacy of two data-driven modelling techniques in predicting streamflow response to local meteorological variables on a long-term basis and under limited availability of datasets. Genetic programming (GP), an evolutionary algorithm approach and differential evolution (DE)-trained artificial neural networks (ANNs) were applied for flow prediction in the upper uMkhomazi River, South Africa. Historical records of streamflow, rainfall and temperature for a 19-year period (1994-2012) were used for model design, and also in the selection of predictor variables into the input vector space of the model. In both approaches, individual monthly predictive models were developed for each month of the year using a one-year lead time. The performances of the predictive models were evaluated using three standard model evaluation criteria, namely mean absolute percentage error (MAPE), root mean-square error (RMSE) and coefficient of determination (R2). Results showed better predictive performance by the GP models (MAPE: 3.64%; RMSE: 0.52: R2: 0.99) during the validation phase when compared to the ANNs (MAPE: 93.99%; RMSE: 11.17; R2: 0.35). Generally, the GP models were found to be superior to the ANNs, as they showed better performance based on the three evaluation measures, and were found capable of giving a good representation of non-linear hydro-meteorological variations despite the use of minimal datasets.
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    Monthly stream flow prediction with limited hydro-climatic variables in the upper Mkomazi River, South Africa using genetic programming
    (Parlar Scientific Publication, 2014) Oyebode, Oluwaseun Kunle; Adeyemo, Josiah; Otieno, Fredrick Alfred O.
    Streamflow prediction remains crucial to decision-making especially when it concerns planning and management of water resources systems. The prediction of streamflow however, comes with various complexities arising from non-linear and dynamic nature of the climatological and hydrological factors. Several modelling studies relating to streamflow prediction have been carried out using different approaches. However, considering the non-linear and dynamic behaviour of hydro-climatological processes, a significant amount of historical data is required in all these approaches in order to achieve accurate and reliable results. Genetic Programming (GP), a computational intelligence approach based on evolutionary algorithm was employed in this study to predict the response of streamflow to hydro-climatic variables in the upper Mkomazi River in South Africa using limited amount of datasets. Historical records for a period of nineteen years (1994-2012) were used for the construction and selection of input variables into the GP vector space. Individual monthly models were employed for streamflow prediction for each month of the year. The performances of the models were evaluated using three statistical measures of accuracy. Results obtained indicate a close agreement and highly positive correlation between observed and predicted values of streamflow during the training and validation phases for all the twelve models developed. These results further confirm the efficacy of the GP approach in monthly streamflow prediction despite the use of limited amount of datasets.