Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/3149
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dc.contributor.advisorDoğan, Sevgi Zeynepen
dc.contributor.authorKontbay, Setenay-
dc.date.accessioned2014-07-22T13:50:58Z-
dc.date.available2014-07-22T13:50:58Z-
dc.date.issued2011en
dc.identifier.urihttp://hdl.handle.net/11147/3149-
dc.descriptionThesis (Master)--İzmir Institute of Technology, Architecture, İzmir, 2011en
dc.descriptionIncludes bibliographical references (leaves: 76-81)en
dc.descriptionText in English;Abstract: Turkish and Englishen
dc.descriptionxii, 85 leavesen
dc.description.abstractThis study aims to predict the issuance durations of occupancy permit applications using the delay causes defined in the permit process and reveal the most significant causes affecting the performance of the prediction. Artificial Neural Networks (ANN) is used for predicting the issuance durations of occupancy permit applications. The model is constructed to predict the issuance durations of least once rejected applications made to Izmir Konak Municipality during year 2008. Then, sensitivity analysis is carried out to detect the most significant delay causes affecting the issuance duration. Permit data are examined to reveal the delay causes of occupancy permit process. Six inputs are generated from the delay causes and used in ANN model: 1) Number of missing approval letters, 2) Number of missing payment documents, 3) Number of non-conformances of project to codes and regulations, 4) Number of all missing documents, 5) First permit application season, 6) First permit rejection season. Total issuance durations of the occupancy permit applications are used as the output parameters of the model. The results of the analysis indicate that the prediction accuracy of the model is 86% and the number of missing approval letters, the number of missing payment documents, and the first application season are respectively the three most significant inputs affecting the prediction performance of the model. This study proves that the total issuance durations are so bound to the delay causes in the permit process that it can be learned and predicted by the ANN model and the occupancy permit process is required to be reengineered.en
dc.language.isoenen_US
dc.publisherIzmir Institute of Technologyen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subject.lcshBuilding permits--Turkeyen
dc.subject.lcshNeural networks (Computer science)en
dc.titleUsing artificial neural networks to predict issuance durations of occupancy permit applicationsen_US
dc.typeMaster Thesisen_US
dc.institutionauthorKontbay, Setenay-
dc.departmentThesis (Master)--İzmir Institute of Technology, Architectureen_US
dc.relation.publicationcategoryTezen_US
item.fulltextWith Fulltext-
item.openairetypeMaster Thesis-
item.cerifentitytypePublications-
item.grantfulltextopen-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
Appears in Collections:Master Degree / Yüksek Lisans Tezleri
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