How to cite: Tașar, B., Unes, F., Varcin, H. (2019) Prediction of the Rainfall – Runoff Relationship Using Neuro-Fuzzy and Support Vector Machines. 2019 ”Air and Water – Components of the Environment” Conference Proceedings, Cluj-Napoca, Romania, p. 237-246, DOI: 10.24193/AWC2019_24.
PREDICTION OF THE RAINFALL – RUNOFF RELATIONSHIP USING NEURO-FUZZY AND SUPPORT VECTOR MACHINES
Bestami TAȘAR , Fatih UNES, Hakan VARCİN
ABSTRACT. – Rainfall- Runoff relationship analyzes are essential for the protection of flood rooting, management of water resources and design of water structures. In this study, Neuro-Fuzzy (NF) and Support Vector Machines (SVM) methods are applied for Rainfall- Runoff prediction. Daily hydrological and seasonal data taken from Muskegon basin in USA were used for present study. 1397 daily data of rainfall, temperature and runoff from the study area were analyzed by NF and SVM methods. The results show that the SVM method lead to low errors and high determinations in the Rainfall-Runoff modeling. Models results are compared with daily observed data. SVM method can be used as an alternative to classical methods in Rainfall- Runoff prediction.
Keywords: Prediction, Rainfall, Runoff, Support vector machines, Neuro-fuzzy.
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