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Application of support vector regression (SVR) for stream flow prediction on the Amazon basin

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dc.contributor.author Du Toit, Melise
dc.contributor.author Wilms, Josefine M
dc.contributor.author Smit, GJF
dc.contributor.author Brink, W
dc.date.accessioned 2017-05-16T09:32:44Z
dc.date.available 2017-05-16T09:32:44Z
dc.date.issued 2016-10
dc.identifier.citation Du Toit, M., Wilms, J.M., Smit, G.J.F. et al. 2016. The application of support vector regression (SVR) for stream flow prediction on the Amazon basin. SASAS Conference 2016, 31 October - 1 November 2016, Cape Town, South Africa en_US
dc.identifier.isbn 978-0-620-72974-1
dc.identifier.uri http://www.csag.uct.ac.za/wp-content/uploads/2016/04/SASAS_2016_Conference_Proceedings_Final_18Nov_16.pdf
dc.identifier.uri http://hdl.handle.net/10204/9031
dc.description Copyright: The authors 2016. Contact SASAS for permission pertaining to the overall collection. en_US
dc.description.abstract Long-term forecasting of river runoff is important for climate scientists and hydrologists. By analysing the processes of a river basin characterized by measurable variables, an empirical data-driven model can be constructed. The support vector regression technique is used in this study to analyse historical stream flow occurrences and predict stream flow values for the Amazon basin. Up to twelve month predictions are made and the coefficient of determination and root-mean-square error are used for accuracy assessment. Compared to previous studies, satisfactory results are obtained. Inclusion of environmental aspects such as precipitation and evaporation are suggested for more accurate predictions. en_US
dc.language.iso en en_US
dc.publisher South African Society for Atmospheric Sciences en_US
dc.rights CC0 1.0 Universal *
dc.rights.uri http://creativecommons.org/publicdomain/zero/1.0/ *
dc.subject Support vector machine en_US
dc.subject Support vector regression en_US
dc.subject Amazon basin en_US
dc.subject Stream flow prediction en_US
dc.title Application of support vector regression (SVR) for stream flow prediction on the Amazon basin en_US
dc.type Conference Presentation en_US
dc.identifier.apacitation Du Toit, M., Wilms, J. M., Smit, G., & Brink, W. (2016). Application of support vector regression (SVR) for stream flow prediction on the Amazon basin. South African Society for Atmospheric Sciences. http://hdl.handle.net/10204/9031 en_ZA
dc.identifier.chicagocitation Du Toit, Melise, Josefine M Wilms, GJF Smit, and W Brink. "Application of support vector regression (SVR) for stream flow prediction on the Amazon basin." (2016): http://hdl.handle.net/10204/9031 en_ZA
dc.identifier.vancouvercitation Du Toit M, Wilms JM, Smit G, Brink W, Application of support vector regression (SVR) for stream flow prediction on the Amazon basin; South African Society for Atmospheric Sciences; 2016. http://hdl.handle.net/10204/9031 . en_ZA
dc.identifier.ris TY - Conference Presentation AU - Du Toit, Melise AU - Wilms, Josefine M AU - Smit, GJF AU - Brink, W AB - Long-term forecasting of river runoff is important for climate scientists and hydrologists. By analysing the processes of a river basin characterized by measurable variables, an empirical data-driven model can be constructed. The support vector regression technique is used in this study to analyse historical stream flow occurrences and predict stream flow values for the Amazon basin. Up to twelve month predictions are made and the coefficient of determination and root-mean-square error are used for accuracy assessment. Compared to previous studies, satisfactory results are obtained. Inclusion of environmental aspects such as precipitation and evaporation are suggested for more accurate predictions. DA - 2016-10 DB - ResearchSpace DP - CSIR KW - Support vector machine KW - Support vector regression KW - Amazon basin KW - Stream flow prediction LK - https://researchspace.csir.co.za PY - 2016 SM - 978-0-620-72974-1 T1 - Application of support vector regression (SVR) for stream flow prediction on the Amazon basin TI - Application of support vector regression (SVR) for stream flow prediction on the Amazon basin UR - http://hdl.handle.net/10204/9031 ER - en_ZA


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