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|Title:||Monthly stream flow prediction with limited hydro-climatic variables in the upper Mkomazi River, South Africa using genetic programming||Authors:||Oyebode, Oluwaseun Kunle
Otieno, Fredrick Alfred O.
|Issue Date:||2014||Publisher:||Parlar Scientific Publication||Source:||Oyebode, O. K.; Adeyemo, J. and Otieno, F. 2014. Monthly stream flow prediction with limited hydro-climatic variables in the upper Mkomazi River, South Africa using genetic programming. Fresenius environmental bulletin. 23(3): 708-719.||Abstract:||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.||URI:||http://hdl.handle.net/10321/2337||ISSN:||1018-4619|
|Appears in Collections:||Research Publications (Engineering and Built Environment)|
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