Monthly evaporation forecasting using artificial neural networks and support vector machines

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dc.contributor.authorTezel, Gulay
dc.contributor.authorBuyukyildiz, Meral
dc.date.accessioned2020-03-26T19:25:11Z
dc.date.available2020-03-26T19:25:11Z
dc.date.issued2016
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractEvaporation is one of the most important components of the hydrological cycle, but is relatively difficult to estimate, due to its complexity, as it can be influenced by numerous factors. Estimation of evaporation is important for the design of reservoirs, especially in arid and semi-arid areas. Artificial neural network methods and support vector machines (SVM) are frequently utilized to estimate evaporation and other hydrological variables. In this study, usability of artificial neural networks (ANNs) (multilayer perceptron (MLP) and radial basis function network (RBFN)) and epsilon-support vector regression (SVR) artificial intelligence methods was investigated to estimate monthly pan evaporation. For this aim, temperature, relative humidity, wind speed, and precipitation data for the period 1972 to 2005 from Beysehir meteorology station were used as input variables while pan evaporation values were used as output. The Romanenko and Meyer method was also considered for the comparison. The results were compared with observed class A pan evaporation data. In MLP method, four different training algorithms, gradient descent with momentum and adaptive learning rule backpropagation (GDX), Levenberg-Marquardt (LVM), scaled conjugate gradient (SCG), and resilient backpropagation (RBP), were used. Also, epsilon-SVR model was used as SVR model. The models were designed via 10-fold cross-validation (CV); algorithm performance was assessed via mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R (2)). According to the performance criteria, the ANN algorithms and epsilon-SVR had similar results. The ANNs and epsilon-SVR methods were found to perform better than the Romanenko and Meyer methods. Consequently, the best performance using the test data was obtained using SCG(4,2,2,1) with R (2) = 0.905.en_US
dc.identifier.doi10.1007/s00704-015-1392-3en_US
dc.identifier.endpage80en_US
dc.identifier.issn0177-798Xen_US
dc.identifier.issn1434-4483en_US
dc.identifier.issue01.02.2020en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage69en_US
dc.identifier.urihttps://dx.doi.org/10.1007/s00704-015-1392-3
dc.identifier.urihttps://hdl.handle.net/20.500.12395/33799
dc.identifier.volume124en_US
dc.identifier.wosWOS:000373143600006en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSPRINGER WIENen_US
dc.relation.ispartofTHEORETICAL AND APPLIED CLIMATOLOGYen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.titleMonthly evaporation forecasting using artificial neural networks and support vector machinesen_US
dc.typeArticleen_US

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