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Depremden Zarar Görebilirlik Boyutunu Etkileyen Faktörlerin Derecelendirilmesi

Year 2023, Issue: 49, 61 - 67, 31.03.2023
https://doi.org/10.31590/ejosat.1259757

Abstract

Afetler geniş kapsamlı sosyal, fiziksel ve ekonomik zararlara neden olan insan veya doğa kaynaklı olaylardır. Deprem büyük yıkımlara neden olan maddi ve manevi ciddi zararlar veren en önemli afetlerden biridir. Depremin neden olacağı hasarların belirlenmesi ve hasarları engellemeye yönelik gerekli önlemlerin alınması zarar boyutunun azaltılmasında önemli bir adımdır. Bu çalışmada depremden zarar görebilirliğe etki eden faktörler literatür taraması ve uzman görüşleri ile belirlenir. Ana ve alt başlıklarda belirlenen zarar görebilirliğe etki eden faktörler etki boyut ve ilişkilerine göre açıklanır. Deprem risk planlamasının yapılması ve önlemlerin alınması için faktörlerin öncelik sıralaması yapılır. Öncelik sıralaması depremden önce, deprem sırasında ve depremden sonra olası maddi ve manevi zarar boyutunu en aza indirmede yardımcı olur. Çok kriterli karar verme problemi olarak tanımlanan problemde depremden zarar görebilirliği etki eden faktörler analitik hiyerarşi prosesi (AHP) ile yapılandırılır ve ikili karşılaştırma matrisleri (İKM) ile değerlendirilir. Ana ve alt faktörler ikili karşılaştırılarak faktörlerin öncelik değerleri elde edilir. Yapısal, çevresel ve sosyal ana faktörlerden yapısal faktörlerin önemi ön plana çıkarken sosyal faktörlerden nüfus yoğunluğu dışındaki faktörler sıralamada son sıralarda yer alır. Çevresel faktörlerden faylara uzaklık ve jeolojik yapının önemi vurgulanır. Yapısal faktörler ile çevresel faktörlerin beraber dikkate alınması depremden zarar görme ölçüsünü önemli ölçüde azaltacağı görülür. Depremden zarar görebilirliği azalmak için tüm faktörlerin bütünsel olarak ele alınması gerekliliği dikkat çeker.

References

  • AFAD. (2021). 1990-2020 Türkiye Deprem İstatistikleri. https://istatistik.com.tr/1990-2020-turkiye-deprem-istatistikleri/
  • AFAD. (2023). Açıklamalı Afet Yönetimi Terimleri Sözlüğü. https://www.afad.gov.tr/aciklamali-afet-yonetimi-terimleri-sozlugu
  • Aguarón, J., Escobar, M. T., Moreno-Jiménez, J. M., & Turón, A. (2020). The Triads Geometric Consistency Index in AHP-Pairwise Comparison Matrices. Mathematics, 8(6), 926.
  • Alizadeh, M., Hashim, M., Alizadeh, E., Shahabi, H., Karami, M. R., Beiranvand Pour, A., Pradhan, B., & Zabihi, H. (2018). Multi-criteria decision making (MCDM) model for seismic vulnerability assessment (SVA) of urban residential buildings. ISPRS International Journal of Geo-Information, 7(11), 444.
  • Alizadeh, M., Ngah, I., Hashim, M., Pradhan, B., & Pour, A. B. (2018). A hybrid analytic network process and artificial neural network (ANP-ANN) model for urban earthquake vulnerability assessment. Remote Sensing, 10(6), 975.
  • Alonso, J. A., & Lamata, M. T. (2006). Consistency in the analytic hierarchy process: A new approach. International journal of uncertainty, fuzziness and knowledge-based systems, 14(04), 445-459.
  • Armaş, I. (2012). Multi-criteria vulnerability analysis to earthquake hazard of Bucharest, Romania. Natural hazards, 63, 1129-1156.
  • Bahadori, H., Hasheminezhad, A., & Karimi, A. (2017). Development of an integrated model for seismic vulnerability assessment of residential buildings: Application to Mahabad City, Iran. Journal of Building Engineering, 12, 118-131.
  • BDTİM. (2000). Genel Bilgiler. Boğaziçi Üniversitesi Kandilli Rasathanesi ve Deprem Araştırma Enstitüsü. http://www.koeri.boun.edu.tr/sismo/2/deprem-bilgileri/genel-bilgiler/
  • BDTİM. (2023a). Büyük Depremler. Boğaziçi Üniversitesi Kandilli Rasathanesi ve Deprem Araştırma Enstitüsü Bölgesel Deprem-Tsunami İzleme ve Değerlendirme Merkezi. http://www.koeri.boun.edu.tr/sismo/2/deprem-bilgileri/buyuk-depremler/#
  • BDTİM. (2023b). Yıllık Deprem Harita, Grafik ve Tabloları. Boğaziçi Üniversitesi Kandilli Rasathanesi ve Deprem Araştırma Enstitüsü Bölgesel Deprem-Tsunami İzleme ve Değerlendirme Merkezi. http://www.koeri.boun.edu.tr/sismo/2/deprem-verileri/yillik-deprem-haritalari/
  • Brunelli, M. (2018). A survey of inconsistency indices for pairwise comparisons. International Journal of General Systems, 47(8), 751-771.
  • Bulut, E., Duru, O., Keçeci, T., & Yoshida, S. (2012). Use of consistency index, expert prioritization and direct numerical inputs for generic fuzzy-AHP modeling: A process model for shipping asset management. Expert Systems with Applications, 39(2), 1911-1923.
  • Csató, L. (2018). Characterization of an inconsistency ranking for pairwise comparison matrices. Annals of Operations Research, 261(1), 155-165.
  • Çoban, V. (2020). Solar energy plant project selection with AHP decision-making method based on hesitant fuzzy linguistic evaluation. Complex & Intelligent Systems, 6(3), 507-529.
  • Duzgun, H., Yucemen, M., Kalaycioglu, H., Çelik, K., Kemec, S., Ertugay, K., & Deniz, A. (2011). An integrated earthquake vulnerability assessment framework for urban areas. Natural hazards, 59, 917-947.
  • Etemadfard, H., & Moradi, M. (2021). Estimating the Damage of Earthquake Using RADIUS Model (Case Study: Tehran). 49-62.
  • Franek, J., & Kresta, A. (2014). Judgment scales and consistency measure in AHP. Procedia economics and finance, 12, 164-173.
  • Gass, S. I., & Rapcsák, T. (2004). Singular value decomposition in AHP. European Journal of Operational Research, 154(3), 573-584.
  • Harker, P. T. (1987). Derivatives of the Perron root of a positive reciprocal matrix: With application to the analytic hierarchy process. Applied Mathematics and Computation, 22(2-3), 217-232.
  • Jena, R., Pradhan, B., & Beydoun, G. (2020). Earthquake vulnerability assessment in Northern Sumatra province by using a multi-criteria decision-making model. International journal of disaster risk reduction, 46, 101518.
  • Nazmfar, H. (2019). An integrated approach of the analytic network process and fuzzy model mapping of evaluation of urban vulnerability against earthquake. Geomatics, Natural Hazards and Risk.
  • Rahman, N., Ansary, M. A., & Islam, I. (2015). GIS based mapping of vulnerability to earthquake and fire hazard in Dhaka city, Bangladesh. International journal of disaster risk reduction, 13, 291-300.
  • Rashed, T., & Weeks, J. (2003). Assessing vulnerability to earthquake hazards through spatial multicriteria analysis of urban areas. International Journal of Geographical Information Science, 17(6), 547-576.
  • Saaty, R. W. (1987). The analytic hierarchy process—What it is and how it is used. Mathematical Modelling, 9(3), 161-176. https://doi.org/10.1016/0270-0255(87)90473-8
  • Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International journal of services sciences, 1(1), 83-98.
  • Shadmaan, M. S., & Popy, S. (2022). An assessment of earthquake vulnerability by multi-criteria decision-making method. Geohazard Mechanics.
  • Shadmaan, S., & Islam, A. I. (2021). Estimation of earthquake vulnerability by using analytical hierarchy process. Natural Hazards Research, 1(4), 153-160.
  • Shafapourtehrany, M., Yariyan, P., Özener, H., Pradhan, B., & Shabani, F. (2022). Evaluating the application of K-mean clustering in Earthquake vulnerability mapping of Istanbul, Turkey. International Journal of Disaster Risk Reduction, 79, 103154.
  • Xu, Y., Wang, Q., Cabrerizo, F. J., & Herrera-Viedma, E. (2018). Methods to improve the ordinal and multiplicative consistency for reciprocal preference relations. Applied Soft Computing, 67, 479-493.
  • Zhou, H., Wang, X., & Yuan, Y. (2015). Risk assessment of disaster chain: Experience from Wenchuan earthquake-induced landslides in China. Journal of mountain science, 12, 1169-1180.

Rating of Factors Affecting the Dimension of Vulnerability from Earthquake

Year 2023, Issue: 49, 61 - 67, 31.03.2023
https://doi.org/10.31590/ejosat.1259757

Abstract

Disasters are human or natural events that cause massive social, physical, and economic damage. Earthquake is the most important disaster that causes great destruction and causes serious material and moral damage. Identifying the sources of damage to be caused by the earthquake and taking the necessary precautions is an important step in reducing the size of the damage. In this study, the factors affecting vulnerability from earthquakes are determined by a literature review and expert opinions. To make earthquake risk planning and take precautions, the factors are prioritized. In the problem, which is defined as a multi-criteria decision-making problem, the factors affecting vulnerability from earthquakes are structured using the analytical hierarchy process (AHP) and evaluated with pairwise comparison matrices. While the importance of structural factors, which is one of the main structural, environmental, and social factors, comes to the fore, factors other than population density, which is one of the social factors, are at the bottom of the list. The distance from the environmental factors to the faults and the importance of the geological structure are emphasized. It is seen that considering structural and environmental factors together will significantly reduce the extent of damage from earthquakes. It has been pointed out that all factors should be considered holistically to reduce vulnerability to earthquakes.

References

  • AFAD. (2021). 1990-2020 Türkiye Deprem İstatistikleri. https://istatistik.com.tr/1990-2020-turkiye-deprem-istatistikleri/
  • AFAD. (2023). Açıklamalı Afet Yönetimi Terimleri Sözlüğü. https://www.afad.gov.tr/aciklamali-afet-yonetimi-terimleri-sozlugu
  • Aguarón, J., Escobar, M. T., Moreno-Jiménez, J. M., & Turón, A. (2020). The Triads Geometric Consistency Index in AHP-Pairwise Comparison Matrices. Mathematics, 8(6), 926.
  • Alizadeh, M., Hashim, M., Alizadeh, E., Shahabi, H., Karami, M. R., Beiranvand Pour, A., Pradhan, B., & Zabihi, H. (2018). Multi-criteria decision making (MCDM) model for seismic vulnerability assessment (SVA) of urban residential buildings. ISPRS International Journal of Geo-Information, 7(11), 444.
  • Alizadeh, M., Ngah, I., Hashim, M., Pradhan, B., & Pour, A. B. (2018). A hybrid analytic network process and artificial neural network (ANP-ANN) model for urban earthquake vulnerability assessment. Remote Sensing, 10(6), 975.
  • Alonso, J. A., & Lamata, M. T. (2006). Consistency in the analytic hierarchy process: A new approach. International journal of uncertainty, fuzziness and knowledge-based systems, 14(04), 445-459.
  • Armaş, I. (2012). Multi-criteria vulnerability analysis to earthquake hazard of Bucharest, Romania. Natural hazards, 63, 1129-1156.
  • Bahadori, H., Hasheminezhad, A., & Karimi, A. (2017). Development of an integrated model for seismic vulnerability assessment of residential buildings: Application to Mahabad City, Iran. Journal of Building Engineering, 12, 118-131.
  • BDTİM. (2000). Genel Bilgiler. Boğaziçi Üniversitesi Kandilli Rasathanesi ve Deprem Araştırma Enstitüsü. http://www.koeri.boun.edu.tr/sismo/2/deprem-bilgileri/genel-bilgiler/
  • BDTİM. (2023a). Büyük Depremler. Boğaziçi Üniversitesi Kandilli Rasathanesi ve Deprem Araştırma Enstitüsü Bölgesel Deprem-Tsunami İzleme ve Değerlendirme Merkezi. http://www.koeri.boun.edu.tr/sismo/2/deprem-bilgileri/buyuk-depremler/#
  • BDTİM. (2023b). Yıllık Deprem Harita, Grafik ve Tabloları. Boğaziçi Üniversitesi Kandilli Rasathanesi ve Deprem Araştırma Enstitüsü Bölgesel Deprem-Tsunami İzleme ve Değerlendirme Merkezi. http://www.koeri.boun.edu.tr/sismo/2/deprem-verileri/yillik-deprem-haritalari/
  • Brunelli, M. (2018). A survey of inconsistency indices for pairwise comparisons. International Journal of General Systems, 47(8), 751-771.
  • Bulut, E., Duru, O., Keçeci, T., & Yoshida, S. (2012). Use of consistency index, expert prioritization and direct numerical inputs for generic fuzzy-AHP modeling: A process model for shipping asset management. Expert Systems with Applications, 39(2), 1911-1923.
  • Csató, L. (2018). Characterization of an inconsistency ranking for pairwise comparison matrices. Annals of Operations Research, 261(1), 155-165.
  • Çoban, V. (2020). Solar energy plant project selection with AHP decision-making method based on hesitant fuzzy linguistic evaluation. Complex & Intelligent Systems, 6(3), 507-529.
  • Duzgun, H., Yucemen, M., Kalaycioglu, H., Çelik, K., Kemec, S., Ertugay, K., & Deniz, A. (2011). An integrated earthquake vulnerability assessment framework for urban areas. Natural hazards, 59, 917-947.
  • Etemadfard, H., & Moradi, M. (2021). Estimating the Damage of Earthquake Using RADIUS Model (Case Study: Tehran). 49-62.
  • Franek, J., & Kresta, A. (2014). Judgment scales and consistency measure in AHP. Procedia economics and finance, 12, 164-173.
  • Gass, S. I., & Rapcsák, T. (2004). Singular value decomposition in AHP. European Journal of Operational Research, 154(3), 573-584.
  • Harker, P. T. (1987). Derivatives of the Perron root of a positive reciprocal matrix: With application to the analytic hierarchy process. Applied Mathematics and Computation, 22(2-3), 217-232.
  • Jena, R., Pradhan, B., & Beydoun, G. (2020). Earthquake vulnerability assessment in Northern Sumatra province by using a multi-criteria decision-making model. International journal of disaster risk reduction, 46, 101518.
  • Nazmfar, H. (2019). An integrated approach of the analytic network process and fuzzy model mapping of evaluation of urban vulnerability against earthquake. Geomatics, Natural Hazards and Risk.
  • Rahman, N., Ansary, M. A., & Islam, I. (2015). GIS based mapping of vulnerability to earthquake and fire hazard in Dhaka city, Bangladesh. International journal of disaster risk reduction, 13, 291-300.
  • Rashed, T., & Weeks, J. (2003). Assessing vulnerability to earthquake hazards through spatial multicriteria analysis of urban areas. International Journal of Geographical Information Science, 17(6), 547-576.
  • Saaty, R. W. (1987). The analytic hierarchy process—What it is and how it is used. Mathematical Modelling, 9(3), 161-176. https://doi.org/10.1016/0270-0255(87)90473-8
  • Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International journal of services sciences, 1(1), 83-98.
  • Shadmaan, M. S., & Popy, S. (2022). An assessment of earthquake vulnerability by multi-criteria decision-making method. Geohazard Mechanics.
  • Shadmaan, S., & Islam, A. I. (2021). Estimation of earthquake vulnerability by using analytical hierarchy process. Natural Hazards Research, 1(4), 153-160.
  • Shafapourtehrany, M., Yariyan, P., Özener, H., Pradhan, B., & Shabani, F. (2022). Evaluating the application of K-mean clustering in Earthquake vulnerability mapping of Istanbul, Turkey. International Journal of Disaster Risk Reduction, 79, 103154.
  • Xu, Y., Wang, Q., Cabrerizo, F. J., & Herrera-Viedma, E. (2018). Methods to improve the ordinal and multiplicative consistency for reciprocal preference relations. Applied Soft Computing, 67, 479-493.
  • Zhou, H., Wang, X., & Yuan, Y. (2015). Risk assessment of disaster chain: Experience from Wenchuan earthquake-induced landslides in China. Journal of mountain science, 12, 1169-1180.
There are 31 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Veysel Çoban 0000-0002-7885-1935

Suheyla Yerel Kandemir 0000-0003-4056-5383

Early Pub Date March 25, 2023
Publication Date March 31, 2023
Published in Issue Year 2023 Issue: 49

Cite

APA Çoban, V., & Yerel Kandemir, S. (2023). Depremden Zarar Görebilirlik Boyutunu Etkileyen Faktörlerin Derecelendirilmesi. Avrupa Bilim Ve Teknoloji Dergisi(49), 61-67. https://doi.org/10.31590/ejosat.1259757