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dc.contributor.authorKişi, Özgür
dc.contributor.authorTombul, Mustafa
dc.contributor.authorKermani, Mohammad Zounemat
dc.date.accessioned2019-10-21T21:11:32Z
dc.date.available2019-10-21T21:11:32Z
dc.date.issued2015
dc.identifier.issn0177-798X
dc.identifier.issn1434-4483
dc.identifier.urihttps://dx.doi.org/10.1007/s00704-014-1232-x
dc.identifier.urihttps://hdl.handle.net/11421/21026
dc.descriptionWOS: 000356539300031en_US
dc.description.abstractThis study compares the accuracy of three different neural computing techniques, multi-layer perceptron (MLP), radial basis neural networks (RBNN), and generalized regression neural networks (GRNN), in modeling soil temperatures (ST) at different depths. Climatic data of air temperature, wind speed, solar radiation, and relative humidity from Mersin Station, Turkey, were used as inputs to the models to estimate monthly ST values. In the first part of the study, the effect of each climatic variable on ST was investigated by using GRNN models. Air temperature was found to be the most effective variable in modeling monthly ST. In the second part of the study, the accuracy of GRNN models was compared with MLP, RBNN, and multiple linear regression (MLR) models. RBNN models were found to be better than the GRNN, MLP, and MLR models in estimating monthly ST at the depths of 5 and 10 cm while the MLR and GRNN models gave the best accuracy in the case of 50- and 100-cm depths, respectively. In the third part of the study, the effect of periodicity on the training, validation, and test accuracy of the applied models was investigated. The results indicated that the adding periodicity component significantly increase models' accuracies in estimating monthly ST at different depths. Root mean square errors of the GRNN, MLP, RBNN, and MLR models were decreased by 19, 15, 19, and 15 % using periodicity in estimating monthly ST at 5-cm depth.en_US
dc.description.sponsorshipTurkish Academy of Sciences (TUBA)en_US
dc.description.sponsorshipThis study was partly supported by The Turkish Academy of Sciences (TUBA). The first author would like to thank TUBA for their support of this study.en_US
dc.language.isoengen_US
dc.publisherSpringer Wienen_US
dc.relation.isversionof10.1007/s00704-014-1232-xen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.titleModeling soil temperatures at different depths by using three different neural computing techniquesen_US
dc.typearticleen_US
dc.relation.journalTheoretical and Applied Climatologyen_US
dc.contributor.departmentAnadolu Üniversitesi, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.identifier.volume121en_US
dc.identifier.issue1.Şuben_US
dc.identifier.startpage377en_US
dc.identifier.endpage387en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US


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