Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/5745
Title: Significance of rent attributes in prediction of earthquake damage in Adapazari, Turkey
Authors: Tayfur, Gökmen
Bektaş, Birkan
Duvarcı, Yavuz
Keywords: Artificial neural network
Earthquakes
Regression analysis
Urban development
Urban rent
Issue Date: Dec-2014
Publisher: Czech Technical University in Prague
Source: Tayfur, G., Bektaş, B., and Duvarcı, Y. (2014). Significance of rent attributes in prediction of earthquake damage in Adapazari, Turkey. Neural Network World, 24(6), 637-653. doi:10.14311/NNW.2014.24.036
Abstract: This paper analyses rent-based determinants of earthquake damage from an urban planning perspective with the data gathered from Adapazari, Turkey, after the disaster in 1999 Eastern Marmara Earthquake (EME). The study employs linear regression, log-linear regression, and artificial neural networks (ANN) methods for cross-verification of results and for finding out the significant urban rent attribute(s) responsible for the damage. All models used are equally capable of predicting the earthquake damage and converge to similar results even if the data are limited. Of the rent variables, the physical density is proved to be especially significant in predicting earthquake damage, while the land value contributes to building resistance. Thus, urban rent can be the primary tool for planners to help reduce the fatalities in preventive planning studies.
URI: https://doi.org/10.14311/NNW.2014.24.036
http://hdl.handle.net/11147/5745
ISSN: 1210-0552
2336-4335
1210-0552
Appears in Collections:City and Regional Planning / Şehir ve Bölge Planlama
Civil Engineering / İnşaat Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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