Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/5538
Full metadata record
DC FieldValueLanguage
dc.contributor.authorTayfur, Gökmen-
dc.contributor.authorNadiri, Ata A.-
dc.contributor.authorMoghaddam, Asghar A.-
dc.date.accessioned2017-05-17T10:48:07Z-
dc.date.available2017-05-17T10:48:07Z-
dc.date.issued2014-03-
dc.identifier.citationTayfur, G., Nadiri, A. A., and Moghaddam, A. A. (2014). Supervised intelligent committee machine method for hydraulic conductivity estimation. Water Resources Management, 28(4), 1173-1184. doi:10.1007/s11269-014-0553-yen_US
dc.identifier.issn0920-4741-
dc.identifier.issn1573-1650-
dc.identifier.urihttps://doi.org/10.1007/s11269-014-0553-y-
dc.identifier.urihttp://hdl.handle.net/11147/5538-
dc.description.abstractHydraulic conductivity is the essential parameter for groundwater modeling and management. Yet estimation of hydraulic conductivity in a heterogeneous aquifer is expensive and time consuming. In this study; artificial intelligence (AI) models of Sugeno Fuzzy Logic (SFL), Mamdani Fuzzy Logic (MFL), Multilayer Perceptron Neural Network associated with Levenberg-Marquardt (ANN), and Neuro-Fuzzy (NF) were applied to estimate hydraulic conductivity using hydrogeological and geoelectrical survey data obtained from Tasuj Plain Aquifer, Northwest of Iran. The results revealed that SFL and NF produced acceptable performance while ANN and MFL had poor prediciton. A supervised intelligent committee machine (SICM), which combines the results of individual AI models using a supervised artificial neural network, was developed for better prediction of the hydraulic conductivity in Tasuj plain. The performance of SICM was also compared to those of the simple averaging and weighted averaging intelligent committee machine (ICM) methods. The SICM model produced reliable estimates of hydraulic conductivity in heterogeneous aquifers.en_US
dc.language.isoenen_US
dc.publisherSpringer Verlagen_US
dc.relation.ispartofWater Resources Managementen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectArtificial intelligence methodsen_US
dc.subjectHeteregenous aquiferen_US
dc.subjectHydraulic conductivityen_US
dc.subjectSupervised intelligence committee machineen_US
dc.subjectTasuj plainen_US
dc.titleSupervised intelligent committee machine method for hydraulic conductivity estimationen_US
dc.typeArticleen_US
dc.authoridTR2054en_US
dc.institutionauthorTayfur, Gökmen-
dc.departmentİzmir Institute of Technology. Civil Engineeringen_US
dc.identifier.volume28en_US
dc.identifier.issue4en_US
dc.identifier.startpage1173en_US
dc.identifier.endpage1184en_US
dc.identifier.wosWOS:000332505400018en_US
dc.identifier.scopus2-s2.0-84896738245en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1007/s11269-014-0553-y-
dc.relation.doi10.1007/s11269-014-0553-yen_US
dc.coverage.doi10.1007/s11269-014-0553-yen_US
dc.identifier.wosqualityQ1-
dc.identifier.scopusqualityQ1-
item.openairetypeArticle-
item.cerifentitytypePublications-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.fulltextWith Fulltext-
item.grantfulltextopen-
crisitem.author.dept03.03. Department of Civil Engineering-
Appears in Collections:Civil Engineering / İnşaat Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Files in This Item:
File Description SizeFormat 
5538.pdfMakale459.21 kBAdobe PDFThumbnail
View/Open
Show simple item record



CORE Recommender

SCOPUSTM   
Citations

41
checked on Feb 16, 2024

WEB OF SCIENCETM
Citations

38
checked on Feb 26, 2024

Page view(s)

128
checked on Feb 26, 2024

Download(s)

238
checked on Feb 26, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.