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 |
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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