Deprem Hasarını Etkileyen Parametrelerin CBS tabanlı Rastgele Ormanlar Makine Öğrenmesi Modeli Kullanılarak Değerlendirilmesi: Türkiye’de 6 Şubat 2023 Kahramanmaraş Depremleri Örneği

Öz

Türkiye, tektonik özellikleri nedeniyle sismik aktivitenin yoğun olduğu bir coğrafyada yer almaktadır. Bu kadar yüksek deprem riskine rağmen, deprem hasarını etkileyen parametrelerin değerlendirilmesi Türkiye'de hala çok yetersizdir. Bu çalışmanın amacı, Türkiye Cumhuriyeti tarihinde en fazla can kaybına neden olan 6 Şubat 2023 Kahramanmaraş depremlerinde deprem hasarını etkileyen parametreleri değerlendirmektir. Bu nedenle, Türkiye'nin farklı bölgelerinde deprem tehlikesi durumunda yapıların davranışlarındaki farklılıkları anlamak için veri üretilmiştir. Çalışmada, depremin hissedildiği yerleşim bölgelerinde farklı yapısal hasar türlerine sahip 198.634 binadan alınan örnek veriler kullanılmıştır. Bu veriler ile yapısal hasara neden olan temel faktörler arasındaki ilişki Coğrafi Bilgi Sistemleri (CBS) tabanlı Rastgele Ormanlar (RO) Makine Öğrenimi (MÖ) modeli kullanılarak analiz edilmiştir. Çalışma sonucunda, 6 Şubat 2023 Kahramanmaraş depremlerinin bina yaşı, yerel zemin koşulları, fay hatlarına uzaklık, merkez üssüne uzaklık, zemin kayma hızı, maksimum zemin hızı ve zemin sıvılaşma etkisi faktörlerinin farklı kombinasyonları sonucunda yapısal hasara neden olduğu anlaşılmıştır.

Anahtar Kelimeler

• Deprem
• Deprem hasarı
• CBS

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

Öz

Türkiye 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 Türkiye. 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 Türkiye. Therefore, data were produced to understand the differences in the behavior of structures in the case of an earthquake hazard in different parts of Türkiye. 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.

Anahtar Kelimeler

• Earthquake
• Earthquake damage
• GIS

Destekleyen Kurum

No

Etik Beyan

I confirm that all authors have made substantial contributions to the manuscript, have approved the final version of the manuscript, and have agreed to its submission to the Journal of Geography.

Teşekkür

We thank Said Turksever for his help in obtaining the sample data used in this study. We wish mercy from Allah for those who lost their lives in this earthquake and a speedy recovery to those who were injured.

Kaynakça

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