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dc.creatorDjurović, Nevenka
dc.creatorDomazet, Milka
dc.creatorStričević, Ružica
dc.creatorPočuča, Vesna
dc.creatorSpalević, V.
dc.creatorPivić, Radmila
dc.creatorGregorić, Eniko
dc.creatorDomazet, Uroš
dc.date.accessioned2020-12-17T21:22:30Z
dc.date.available2020-12-17T21:22:30Z
dc.date.issued2015
dc.identifier.issn2356-6140
dc.identifier.urihttp://aspace.agrif.bg.ac.rs/handle/123456789/3838
dc.description.abstractWater table forecasting plays an important role in the management of groundwater resources in agricultural regions where there are drainage systems in river valleys. The results presented in this paper pertain to an area along the left bank of the Danube River, in the Province of Vojvodina, which is the northern part of Serbia. Two soft computing techniques were used in this research: an adaptive neurofuzzy inference system (ANFIS) and an artificial neural network (ANN) model for one-month water table forecasts at several wells located at different distances from the river. The results suggest that both these techniques represent useful tools for modeling hydrological processes in agriculture, with similar computing and memory capabilities, such that they constitute an exceptionally good numerical framework for generating high-quality models.en
dc.publisherHindawi Publishing Corporation
dc.rightsopenAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.sourceScientific World Journal
dc.titleComparison of Groundwater Level Models Based on Artificial Neural Networks and ANFISen
dc.typearticle
dc.rights.licenseBY
dc.citation.other2015: -
dc.citation.volume2015
dc.identifier.doi10.1155/2015/742138
dc.identifier.fulltexthttp://aspace.agrif.bg.ac.rs/bitstream/id/2395/3835.pdf
dc.identifier.scopus2-s2.0-84949255926
dc.type.versionpublishedVersion


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