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

Du Toit, MeliseWilms, Josefine MSmit, GJFBrink, W2017-05-162017-05-162016-10Du 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 Africa978-0-620-72974-1http://www.csag.uct.ac.za/wp-content/uploads/2016/04/SASAS_2016_Conference_Proceedings_Final_18Nov_16.pdfhttp://hdl.handle.net/10204/9031Copyright: The authors 2016. Contact SASAS for permission pertaining to the overall collection.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.enCC0 1.0 UniversalSupport vector machineSupport vector regressionAmazon basinStream flow predictionApplication of support vector regression (SVR) for stream flow prediction on the Amazon basinConference PresentationDu 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/10204/9031Du 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/10204/9031Du 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 .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 -