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dc.creatorKovacević, Miljan
dc.creatorIvanišević, Nenad
dc.creatorDašić, Tina
dc.creatorMarković, Ljubo
dc.date.accessioned2019-04-19T14:30:17Z
dc.date.available2019-04-19T14:30:17Z
dc.date.issued2018
dc.identifier.issn0350-2465
dc.identifier.urihttps://grafar.grf.bg.ac.rs/handle/123456789/968
dc.description.abstractThe possibility of short-term water flow forecasting in a karst region is presented in this paper. Four state-of-the-art machine learning algorithms are used for the one day ahead forecasting: multi-layer perceptron neural network, radial basis function neural network, support vector machines for regression (SVR), and adaptive neuro fuzzy inference system (ANFIS). The results show that the ANFIS model outperforms other algorithms when the root mean square error and mean absolute error are used as quality indicators.en
dc.publisherUnion of Croatian Civil Engineers and Technicians
dc.rightsopenAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceGrađevinar
dc.subjectartificial neural networken
dc.subjectSVRen
dc.subjectANFISen
dc.subjectrainfall-runoff ratio in karst areasen
dc.titleApplication of artificial neural networks for hydrological modelling in karsten
dc.typearticle
dc.rights.licenseBY
dc.citation.epage10
dc.citation.issue1
dc.citation.other70(1): 1-10
dc.citation.rankM23
dc.citation.spage1
dc.citation.volume70
dc.identifier.doi10.14256/JCE.1594.2016
dc.identifier.fulltexthttps://grafar.grf.bg.ac.rs//bitstream/id/4317/966.pdf
dc.identifier.rcubconv_1959
dc.identifier.scopus2-s2.0-85042587602
dc.identifier.wos000427672000001
dc.type.versionpublishedVersion


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