Evaluation of Risk Management Performance of Disaster Management Centers Using Multi-Criteria Decision Making Methods: Example of Southeastern Anatolia Region

Öz

Disasters are the results of natural, technological or human-induced events that cause physical, economic and social losses that interrupt the flow of people's daily lives. Preventing the formation of disaster risks at the national level and reducing the risks depend on the studies carried out at the local level. Disaster management centers are the critical components of the local in preventing the loss of life and property that may occur after disasters. The preparedness level of disaster management centers in the affected regions for disasters and emergencies prevents emergencies from turning into disasters. The Southeastern Anatolia Region of Turkey is a region exposed to many disasters such as earthquakes, landslides, floods, terrorism and avalanches. Within the scope of the study to increase the disaster preparedness of the local, the disaster risk management performances of AFAD (Disaster and Emergency Directorate) centers of 4 cities in the Southeastern Anatolia Region, Gaziantep, Mardin, Diyarbakır and Kilis, were evaluated with the Multi-Criteria Decision Making Method (MCDM). The AHP (Analytical Hierarchy Process) method was used to determine the importance weights of risk management performance criteria, and the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method was used to compare the preparedness levels of the provinces. According to the study results, the most important criterion in the evaluation of the risk management performance of disaster management centers was determined as risk determination, and Mardin was determined as the province with the highest level of preparedness. The study aims to be a tool for determining the vulnerabilities with disaster potential that endanger the sustainable development of the region, increasing resilience against disasters and disaster preparedness plans.

Anahtar Kelimeler

• AFAD
• AHP and TOPSIS
• Disaster Risk Management
• Multi-Criteria Decision Making Method

Afet Yönetim Merkezlerinin Risk Yönetimi Performansının Çok Kriterli Karar Verme Yöntemleriyle Değerlendirilmesi: Güneydoğu Anadolu Bölgesi Örneği

Öz

Afetler, insanların günlük hayat akışını kesintiye uğratan fiziksel ekonomik ve sosyal kayıplara sebep olan doğal teknolojik veya insan kaynaklı olayların sonuçlarıdır. Ulusal düzeyde afet risklerinin oluşumunu önlemek ve riskleri azaltmak yerel düzeyde yapılan çalışmalara bağlıdır. Afet yönetim merkezleri, afetlerden sonra oluşabilecek can ve mal kaybının önüne geçilebilmesinde yerelin kritik bileşenidir. Etkilenen bölgelerdeki afet yönetim merkezlerinin, afet ve acil durumlara hazırlık düzeyi acil durumların afete dönüşmesini engeller. Türkiye’nin Güneydoğu Anadolu Bölgesi, deprem, heyelan, sel, terör ve çığ gibi bir çok afete maruziyet yaşayan bir bölgedir. Yerelin afet hazırlığının artırılması için çalışma kapsamında Güneydoğu Anadolu Bölgesinde 4 şehir olan Gaziantep, Mardin, Diyarbakır ve Kilis illerinin AFAD (Afet ve Acil Durum Müdürlüğü) merkezlerinin afet risk yönetimi performansları Çok Kriterli Karar Verme Yöntemi (ÇKKV) ile değerlendirildi. Risk yönetimi performansı kriterlerinin önem ağırlıklarını belirlemede AHP (Analytical Hierarchy Process) yöntemi ve illerin hazırlık düzeyinin karşılaştırılması için TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) yöntemiyle sıralama yapılmıştır. Çalışma sonuçlarında afet yönetim merkezlerinin risk yönetimi performansının değerlendirilmesinde en önemli kriter riski belirleme, hazırlık düzeyi en yüksek il olarak Mardin ili belirlenmiştir. Çalışmanın bölgenin sürdürülebilir kalkınmasını tehlikeye atan afet potansiyeli olan kırılganlıkların belirlenmesinde, afetlere karşı dirençliliğin artırılması ve afet hazırlık planları için bir araç olması hedeflenmektedir.

Anahtar Kelimeler

• AFAD
• Afet Risk Yönetimi
• AHP ve TOPSIS
• Çok Kriterli Karar Verme Yöntemi

Kaynakça

1. AFAD. (2022). AFAD Tarihçesi.T.C. İçişleri Bakanlığı Afet ve Acil Durum Yönetimi Başkanlığı. https://www.afad.gov.tr/afad-hakkinda
2. Aman, D. D., & Aytac, G. (2022). Multi-criteria decision making for city-scale infrastructure of post-earthquake assembly areas: Case study of Istanbul. International Journal of Disaster Risk Reduction, 67, 102668. https://doi.org/10.1016/j.ijdrr.2021.102668 (Son Erişim: 13.11.2021)
3. Baohua, J., Lihui, Z., Jinxu, L., Zhenyuan, H., & Liang, Z. (2011, May). AHP-based assessment of emergency response agencies. In 2011 IEEE 3rd International Conference on Communication Software and Networks (pp. 82-85). IEEE. DOI: 10.1109/ICCSN.2011.6014680
4. Cardona O.D. (2010) Indicators of Disaster Risk and Risk Management- Program for Latin America and the Caribbean: Summary Report – Updated 2009, Inter-American Development Bank, Infrastructure and Environment Sector, Washington. http://dx.doi.org/10.18235/0010537
5. Cardona, O. D. (2006). A system of indicators for disaster risk management in the Americas. Measuring vulnerability to natural hazards—Towards disaster resilient societies
6. Cardona, O. D., & Carreño, M. L. (2011). Updating the indicators of disaster risk and risk management for the Americas. IDRiM Journal, 1(1), 27-47. DOI 10.5595/idrim.2011.0014
7. Cardona, O.D. (2005). Indicators of disaster risk and risk management: Summary report. Inter-American Development Bank. http://dx.doi.org/10.18235/0010537 (Son Erişim: Temmuz 2005)
8. Carreño, M. L., Cardona, O. D., & Barbat, A. H. (2007). A disaster risk management performance index. Natural hazards, 41(1), 1-20. DOI 10.1007/s11069-006-9008-y

9. Carreno, M. L., Cardona, O. D., ve Barbat, A. H. (2005). Evaluation of the risk management performance. INTERSECTII/INTERSECTIONS, 2(2).
10. Cengiz, D. (2015). Zorunlu Göçün Mekânsal Etkileri ve Yerel Halkın Algısı: Kilis Örneği. Electronic Turkish Studies, 10(2). http://dx.doi.org/10.7827/TurkishStudies.7938
11. Chen, N., Chen, L., Tang, C., Wu, Z., & Chen, A. (2019). Disaster risk evaluation using factor analysis: a case study of Chinese regions. Natural Hazards, 99, 321-335. https://doi.org/10.1007/s11069-019-03742-w
12. Chitsaz, N., & Banihabib, M. E. (2015). Comparison of different multi criteria decision-making models in prioritizing flood management alternatives. Water Resources Management, 29, 2503-2525. DOI 10.1007/s11269-015-0954-6
13. Cutter, S. L., Barnes, L., Berry, M., Burton, C., Evans, E., Tate, E., & Webb, J. (2008). A place-based model for understanding community resilience to natural disasters. Global environmental change, 18(4), 598-606. https://doi.org/10.1016/j.gloenvcha.2008.07.013
14. Dilley, M., Chen, R. S., Deichmann, U., Lerner-Lam, A. L., & Arnold, M. (2005). Natural disaster hotspots: a global risk analysis. The World Bank.
15. EM-DAT. (2023). Disasters Year in Review 2022. Centre for Research on the Epidemiology of Disaster CRED. Issue No:70.
16. Erkal, T., & Değerliyurt, M. (2009). Türkiye’de afet yönetimi. Doğu Coğrafya Dergisi, 14(22), 147-164.
17. Frigerio, I., & De Amicis, M. (2016). Mapping social vulnerability to natural hazards in Italy: A suitable tool for risk mitigation strategies. Environmental Science & Policy, 63, 187-196. https://doi.org/10.1016/j.envsci.2016.06.001
18. GAP. (2021). Güneydoğu Anadolu Projesi Bölge Kalkınma Programı Eylem Planı. 2021-2023.
19. García-Cascales, M. S., & Lamata, M. T. (2012). On rank reversal and TOPSIS method. Mathematical and computer modelling, 56(5-6), 123-132. https://doi.org/10.1016/j.mcm.2011.12.022
20. Gumasing, M. J. J., Prasetyo, Y. T., Ong, A. K. S., & Nadlifatin, R. (2022). Determination of factors affecting the response efficacy of Filipinos under Typhoon Conson 2021 (Jolina): An extended protection motivation theory approach. International Journal of Disaster Risk Reduction, 70, 102759. https://doi.org/10.1016/j.ijdrr.2021.102759
21. Keating, A., Mechler, R., Mochizuki, J., Kunreuther, H., Bayer, J., Hanger, S., ... & Egan, C. (2014). Operationalizing resilience against natural disaster risk: opportunities, barriers, and a way forward. Kelly, M., Schwarz, I., Ziegelaar, M., Watkins, A. B., & Kuleshov, Y. (2023). Flood Risk Assessment and Mapping: A Case Study from Australia’s Hawkesbury-Nepean Catchment. Hydrology, 10(2), 26. https://doi.org/10.3390/hydrology10020026
22. Kontokosta, C. E., & Malik, A. (2018). The Resilience to Emergencies and Disasters Index: Applying big data to benchmark and validate neighborhood resilience capacity. Sustainable cities and society, 36, 272-285. https://doi.org/10.1016/j.scs.2017.10.025
23. Kun-Peng, J., & Zhi-Jie, S. (2009, June). Assessment of disaster emergency management ability based on the interval-valued fuzzy TOPSIS method. In International Conference on Multiple Criteria Decision Making (pp. 501-506). Berlin, Heidelberg: Springer Berlin Heidelberg.
24. Lezki, Ş., Sönmez, H., IŞIKLAR, E., Özdemir, A., & Alptekin, N. (2016). İşletmelerde karar verme teknikleri. Eskişehir: Anadolu Üniversitesi Yayınları.
25. Malakar, S., Rai, A. K., Kannaujiya, V. K., & Gupta, A. K. (2023). Revised Empirical Relations Between Earthquake Source and Rupture Parameters by Regression and Machine Learning Algorithms. Pure and Applied Geophysics, 180(10), 3477-3494. https://doi.org/10.1007/s00024-023-03340-9
26. Novelo-Casanova, D. A., & Suárez, G. (2015). Estimation of the Risk Management Index (RMI) using statistical analysis. Natural Hazards, 77(3), 1501-1514. DOI 10.1007/s11069-015-1663-4
27. Otay, I., & Jaller, M. (2020). Multi-expert disaster risk management & response capabilities assessment using interval-valued intuitionistic fuzzy sets. Journal of Intelligent & Fuzzy Systems, 38(1), 835-852. DOI: 10.3233/JIFS-179452
28. Otay, I., & Jaller, M. (2020). Multi-expert disaster risk management & response capabilities assessment using interval-valued intuitionistic fuzzy sets. Journal of Intelligent & Fuzzy Systems, 38(1), 835-852. DOI:10.3233/JIFS-179452
29. Parsons, M., Glavac, S., Hastings, P., Marshall, G., McGregor, J., McNeill, J., ... & Stayner, R. (2016). Top-down assessment of disaster resilience: A conceptual framework using coping and adaptive capacities. International Journal of Disaster Risk Reduction, 19, 1-11. https://doi.org/10.1016/j.ijdrr.2016.07.005
30. Peduzzi, P., Dao, H., Herold, C., & Mouton, F. (2009). Assessing global exposure and vulnerability towards natural hazards: the Disaster Risk Index. Natural hazards and earth system sciences, 9(4), 1149-1159. https://doi.org/10.5194/nhess-9-1149-2009
31. Pelling, M. (2004). Visions of Risk: A Review of International Indicators of Disaster Risk and Management. United Nations International Strategy for Disaster Reduction (UNISDR).
32. Roy, S., Bose, A., Singha, N., Basak, D., & Chowdhury, I. R. (2021). Urban waterlogging risk as an undervalued environmental challenge: An Integrated MCDA-GIS based modeling approach. Environmental Challenges, 4, 100194. https://doi.org/10.1016/j.envc.2021.100194
33. Saaty, T. (2013). The analytic network process. In Decision making with the analytic network process (pp. 1-40). Springer, Boston, MA.
34. Saaty, T. L. (1990). The analytic hierarchy process in conflict management. International Journal of Conflict Management.
35. Saaty, T. L. (1991). Some mathematical concepts of the analytic hierarchy process. Behaviormetrika, 18(29), 1-9. https://doi.org/10.2333/bhmk.18.29_1
36. Saaty, T. L., & Vargas, L. G. (2006). Decision making with the analytic network process (Vol. 282). Springer Science+ Business Media, LLC.
37. Shih, H. S., Shyur, H. J., & Lee, E. S. (2007). An extension of TOPSIS for group decision making. Mathematical and computer modelling, 45(7-8), 801-813. https://doi.org/10.1016/j.mcm.2006.03.023
38. Sönmez, M. E., & Kesici, Ö. (2012). İklim Değişikliği ve Plansız Şehirleşmenin Kilis Şehrinde Yol Açtığı Sel Felaketleri. Doğu Coğrafya Dergisi, 17(28), 57-77.
39. UNDRR, (2022). United Nations Office for Disaster Risk Reduction. https://www.undrr.org/terminology/disaster-risk
40. UNISDR (United Nations International Strategy for Disaster Reduction). 2015. Sendai framework for disaster risk reduction 2015–2030. Geneva: UNISDR. DOI 10.1007/s13753-015-0050-9
41. Yanilmaz, S., Baskak, D., Yucesan, M., & Gul, M. (2021). Extension of FEMA and SMUG models with Bayesian best-worst method for disaster risk reduction. International Journal of Disaster Risk Reduction, 66, 102631. https://doi.org/10.1016/j.ijdrr.2021.102631
42. Yanılmaz, S., Gürel, E., Kartal, O. V., Çam, A., & Turan, M. (2019). Toplum Tabanlı Afet Yönetimi Çerçevesinde Barınma Risk Analizi. The Journal, 12(67).
43. Yaprak, Ü. (2020). Afetler için yaygın savunmasızlık ve yerel afet endeksleri hesaplanması: Türkiye illeri örneği. Doktora Tezi.
44. Zahmatkesh, Z., & Karamouz, M. (2017). An uncertainty-based framework to quantifying climate change impacts on coastal flood vulnerability: Case study of New York City. Environmental monitoring and assessment, 189, 1-20. https://doi.org/10.1007/s10661-017-6282-y
45. Zavadskas, E. K., Turskis, Z., & Kildienė, S. (2014). State of art surveys of overviews on MCDM/MADM methods. Technological and economic development of economy, 20(1), 165-179. https://doi.org/10.3846/20294913.2014.892037