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Modeling soil temperatures at different depths by using three different neural computing techniques

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dc.contributor.authorTombul, Mustafa
dc.contributor.authorMustafa Tombul
dc.contributor.authorMohammad Zounemat‐Kermani
dc.contributor.orcid0000-0001-7847-5872
dc.contributor.orcid0000-0002-1875-8042
dc.contributor.orcid0000-0002-1421-8671
dc.date.accessioned2025-11-13T09:04:11Z
dc.date.issued2014-08-06
dc.identifier.doihttps://doi.org/10.1007/s00704-014-1232-x
dc.identifier.endpage387
dc.identifier.issn0177-798X
dc.identifier.issue1-2
dc.identifier.openalexW1983945090
dc.identifier.startpage377
dc.identifier.urihttps://hdl.handle.net/11421/367
dc.identifier.urihttps://doi.org/10.1007/s00704-014-1232-x
dc.identifier.volume121
dc.language.isoen
dc.relation.ispartofTheoretical and Applied Climatology
dc.rightsrestrictedAccess
dc.subjectArtificial neural network
dc.subjectLinear regression
dc.subjectMultilayer perceptron
dc.subjectWind speed
dc.subjectMean squared error
dc.subjectPerceptron
dc.subjectCoefficient of determination
dc.subjectAir temperature
dc.subjectEnvironmental science
dc.subjectRegression
dc.subjectRelative humidity
dc.subjectRegression analysis
dc.subjectMathematics
dc.subjectStatistics
dc.subjectMeteorology
dc.subjectComputer science
dc.subjectAtmospheric sciences
dc.subjectMachine learning
dc.subjectGeology
dc.subject.sdg13
dc.titleModeling soil temperatures at different depths by using three different neural computing techniques
dc.typeArticle
dspace.entity.typePublication
local.authorid.openalexA5081589463

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