Evaluation of Parameters Affecting Earthquake Damage Using a GIS-based Random Forests Machine Learning Model: The Case of the 6 February 2023 Kahramanmaras Earthquakes in Türkiye

Basit Öğe Kaydı
dc.contributor.authorOzsahin, Emre
dc.contributor.authorOzturk, Mikayil
dc.date.accessioned2025-04-06T12:23:41Z
dc.date.available2025-04-06T12:23:41Z
dc.date.issued2024
dc.departmentTekirdağ Namık Kemal Üniversitesi
dc.description.abstractT & uuml;rkiye is a geographical feature with intense seismic activity due to its tectonic features. Despite such a high earthquake risk, the evaluation of parameters affecting earthquake damage is still very inadequate in T & uuml;rkiye. The aim of this study was to evaluate the parameters affecting earthquake damage in the 6 February 2023 Kahramanmaras earthquake, which caused the highest number of casualties in the history of the Republic of T & uuml;rkiye. Therefore, data were produced to understand the differences in the behavior of structures in the case of an earthquake hazard in different parts of T & uuml;rkiye. The study used sample data from 198,634 buildings with varying types of structural damage in residential areas where the earthquake had been felt. The relationship between these data and key factors causing structural damage was analyzed using a Geographic Information Systems (GIS)-based Random Forests (RF) Machine Learning (ML) model. As a result of this study, it was understood that the 6 February 2023 Kahramanmaras earthquakes caused structural damage as a result of different combinations of building age, local soil conditions, distance to fault lines, distance to the epicenter, ground slip velocity, maximum ground velocity, and soil liquefaction effect factors
dc.identifier.doi10.26650/JGEOG2024-1432062
dc.identifier.issn1302-7212
dc.identifier.issn1305-2128
dc.identifier.issue49
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://doi.org/10.26650/JGEOG2024-1432062
dc.identifier.urihttps://hdl.handle.net/20.500.11776/17138
dc.identifier.wosWOS:001419155600001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherIstanbul Univ, Fac Letters, Dept Geography
dc.relation.ispartofJournal of Geography-Cografya Dergisi
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WOS_20250406
dc.subjectEarthquake
dc.subjectEarthquake damage
dc.subjectGIS
dc.titleEvaluation of Parameters Affecting Earthquake Damage Using a GIS-based Random Forests Machine Learning Model: The Case of the 6 February 2023 Kahramanmaras Earthquakes in Türkiye
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu