Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/9441
DC FieldValueLanguage
dc.contributor.authorKalogirou, Soteris A.-
dc.contributor.authorFlorides, Georgios A.-
dc.contributor.authorPouloupatis, Panayiotis-
dc.contributor.authorChristodoulides, Paul-
dc.contributor.authorJoseph-Stylianou, Josephina-
dc.date.accessioned2017-02-03T11:28:05Z-
dc.date.available2017-02-03T11:28:05Z-
dc.date.issued2015-
dc.identifier.citationRenewable Energy, 2015, vol. 77, pp. 400-407.en_US
dc.identifier.issn09601481-
dc.identifier.urihttps://hdl.handle.net/20.500.14279/9441-
dc.description.abstractIn this paper a neural network is used for the generation of a contour map of the ground conductivity in Cyprus. Archived data of thermal conductivity of ground recorded at 41 boreholes are used for training a multiple hidden layer neural network with feedforward architecture. The correlation coefficient obtained between the predicted and training data set is 0.9657, indicating an accurate mapping of the data. The validation of the network was performed using an unknown dataset. The correlation coefficient for the unknown cases was 0.9553. In order to broaden the database, the patterns used for the validation of the technique were embedded into the training data set and a new training of the network was performed. The correlation coefficient value for this case was equal to 0.9718. A 10×10km grid is then drawn over a detailed topographic map of Cyprus and the various input parameters were recorded for each grid point. This information was then supplied to the trained network and by doing so ground conductivity was predicted at each grid-point. This map will be a helpful tool for engineers in designing geothermal heat pump systems in Cyprus.en_US
dc.formatpdfen_US
dc.language.isoenen_US
dc.relation.ispartofRenewable Energyen_US
dc.rights© Elsevieren_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/us/*
dc.subjectArtificial neural networksen_US
dc.subjectBoreholesen_US
dc.subjectGeothermal mapsen_US
dc.subjectGround conductivityen_US
dc.titleArtificial neural networks for the generation of a conductivity map of the grounden_US
dc.typeArticleen_US
dc.collaborationCyprus University of Technologyen_US
dc.collaborationMinistry of Agriculture, Rural Development and Environment, Cyprusen_US
dc.subject.categoryEnvironmental Engineeringen_US
dc.journalsOpen Accessen_US
dc.countryCyprusen_US
dc.subject.fieldEngineering and Technologyen_US
dc.publicationPeer Revieweden_US
dc.identifier.doi10.1016/j.renene.2014.12.033en_US
dc.relation.volume77en_US
cut.common.academicyear2014-2015en_US
dc.identifier.spage400en_US
dc.identifier.epage407en_US
item.openairetypearticle-
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.openairecristypehttp://purl.org/coar/resource_type/c_6501-
item.languageiso639-1en-
crisitem.author.deptDepartment of Mechanical Engineering and Materials Science and Engineering-
crisitem.author.deptDepartment of Mechanical Engineering and Materials Science and Engineering-
crisitem.author.deptDepartment of Mechanical Engineering and Materials Science and Engineering-
crisitem.author.deptDepartment of Electrical Engineering, Computer Engineering and Informatics-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.facultyFaculty of Engineering and Technology-
crisitem.author.orcid0000-0002-4497-0602-
crisitem.author.orcid0000-0001-9079-1907-
crisitem.author.orcid0000-0002-2229-8798-
crisitem.author.parentorgFaculty of Engineering and Technology-
crisitem.author.parentorgFaculty of Engineering and Technology-
crisitem.author.parentorgFaculty of Engineering and Technology-
crisitem.author.parentorgFaculty of Engineering and Technology-
crisitem.journal.journalissn0960-1481-
crisitem.journal.publisherElsevier-
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