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
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 d...
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Istanbul University Press
2024-12-01
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| Series: | Coğrafya Dergisi |
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| Online Access: | https://cdn.istanbul.edu.tr/file/JTA6CLJ8T5/EC03BAEB94F641CCA8AADAC72750D3D5 |
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| author | Emre Özşahin Mikayil Öztürk |
| author_facet | Emre Özşahin Mikayil Öztürk |
| author_sort | Emre Özşahin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-7560a9d9669d488fbc4f143db5e20d05 |
| institution | DOAJ |
| issn | 1305-2128 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Istanbul University Press |
| record_format | Article |
| series | Coğrafya Dergisi |
| spelling | doaj-art-7560a9d9669d488fbc4f143db5e20d052025-08-20T03:11:11ZengIstanbul University PressCoğrafya Dergisi1305-21282024-12-0149436310.26650/JGEOG2024-1432062123456Evaluation of Parameters Affecting Earthquake Damage Using a GIS-based Random Forests Machine Learning Model: The Case of the 6 February 2023 Kahramanmaras Earthquakes in TürkiyeEmre Özşahin0https://orcid.org/0000-0001-8169-6908Mikayil Öztürk1https://orcid.org/0009-0009-6482-108XNamık Kemal Üniversitesi, Tekirdag, TurkiyeNamık Kemal Üniversitesi, Tekirdag, TurkiyeTürkiye 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.https://cdn.istanbul.edu.tr/file/JTA6CLJ8T5/EC03BAEB94F641CCA8AADAC72750D3D5earthquakeearthquake damagegis |
| spellingShingle | Emre Özşahin Mikayil Öztürk 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 Coğrafya Dergisi earthquake earthquake damage gis |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 turkiye |
| topic | earthquake earthquake damage gis |
| url | https://cdn.istanbul.edu.tr/file/JTA6CLJ8T5/EC03BAEB94F641CCA8AADAC72750D3D5 |
| work_keys_str_mv | AT emreozsahin evaluationofparametersaffectingearthquakedamageusingagisbasedrandomforestsmachinelearningmodelthecaseofthe6february2023kahramanmarasearthquakesinturkiye AT mikayilozturk evaluationofparametersaffectingearthquakedamageusingagisbasedrandomforestsmachinelearningmodelthecaseofthe6february2023kahramanmarasearthquakesinturkiye |