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A comparative study of artificial neural network (MLP, RBF) and support vector machine models for river flow prediction

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dc.creator Terzi, Ozlem
dc.creator GHORBANİ, Mohammad Ali
dc.creator ZADEH, Hojat Ahmad
dc.creator ISAZADEH, Mohammad
dc.date 2016-02-29T22:00:00Z
dc.date.accessioned 2020-10-06T10:48:51Z
dc.date.available 2020-10-06T10:48:51Z
dc.identifier 9240bed3-6ccb-4f89-a56a-5fecde061a9d
dc.identifier 10.1007/s12665-015-5096-x
dc.identifier https://avesis.sdu.edu.tr/publication/details/9240bed3-6ccb-4f89-a56a-5fecde061a9d/oai
dc.identifier.uri http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/66489
dc.description This study investigates the applicability of multilayer perceptron (MLP), radial basis function (RBF) and support vector machine (SVM) models for prediction of river flow time series. Monthly river flow time series for period of 1989-2011 of Safakhaneh, Santeh and Polanian hydrometric stations from Zarrinehrud River located in north-western Iran were used. To obtain the best input-output mapping, different input combinations of antecedent monthly river flow and a time index were evaluated. The models results were compared using root mean square errors and the correlation coefficient. A comparison of models indicates that MLP and RBF models predicted better than SVM model for monthly river flow time series. Also the results showed that including a time index within the inputs of the models increases their performance significantly. In addition, the reliability of the models prediction was calculated by an uncertainty estimation. The results indicate that the uncertainty in the SVM model was less than those in the RBF and MLP models for predicting monthly river flow.
dc.language eng
dc.rights info:eu-repo/semantics/closedAccess
dc.title A comparative study of artificial neural network (MLP, RBF) and support vector machine models for river flow prediction
dc.type info:eu-repo/semantics/article


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