KaydedilenlerDerginizi ekleyin

Dergiler / Coğrafya Dergisi / 2026 / Sayı: 42

Doğal Afetler için AHP Tabanlı ELECTRE I ve Matematik Model ile Risk Değerlendirmesine Ilişkin Fine Kinney Yöntemine Yeni Bir Yaklaşım

A New Approach to the Fine Kinney Method with AHP Based ELECTRE I and Math Model on Risk Assessment for Natural Disasters

Onur Derse(Tarsus University, Department of Management and Organization, Logistics, Mersin, Turkey)0000-0002-4528-1999
Sayfa
155–164
Yayın Tarihi
2021-07-07
DOI
—
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Böylelikle kriz merkezinde en uygun lokasyonu seçme sorunu hedef programlama yaklaşımıyla ele alınmaktadır.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Doğal afetler insan yaşamı ve çevre üzerinde çok büyük riskler oluşturur.
📊
Temel Bulgular & Veri:Araştırmacılar bu riskleri afetler bağlamında değerlendirmeye daha fazla önem verdiler.
💡
Sonuç & Katkı:Böylelikle kriz merkezinde en uygun lokasyonu seçme sorunu hedef programlama yaklaşımıyla ele alınmaktadır.
🔬Tür:Araştırma Makalesi
📍Kapsam:2020 Verileri
⚙️Değişkenler:Risk Değerlendirme · Matematik Modelleme · Doğal Afetler

Özet

Doğal afetler insan yaşamı ve çevre üzerinde çok büyük riskler oluşturur. Araştırmacılar bu riskleri afetler bağlamında değerlendirmeye daha fazla önem verdiler. Çalışmalar, bölgelerdeki doğal afetlerden yalnızca birinin risk değerlendirmesine odaklanmaktadır. Ancak bu bölgeler için tamamen doğal afetleri içeren bir risk değerlendirmesi yapılmalıdır. Bu çalışmada, risk değerlendirmesi olarak Türkiye'nin Ege bölgesindeki 8 farklı ilde ele alınmıştır. Kapsanan şehirler için deprem, yangın, heyelan, sel, fırtına / tayfun gibi doğal afetler hakkında bir vaka verisi (1990-2020) dikkate alınmıştır. Doğal afetler için Fine Kinney risk değerlendirme yöntemi revize edilerek, her il için bir risk puanı elde edilmektedir. Daha sonra bu illere AHP tabanlı ELECTRE I yöntemi uygulanır. Bu yöntem sonucunda en riskli bölge elde edilir. Elde edilen en riskli bölgede bir kriz merkezinin konumu, afet sonuçlarından etkilenen bölgelere etkin çözümler getirmelidir. Böylelikle kriz merkezinde en uygun lokasyonu seçme sorunu hedef programlama yaklaşımıyla ele alınmaktadır.

Abstract

Natural disasters impose enormous risks on human living and the environment. Researchers have given more attention to evaluating these risks in the context of disasters. Studies focus on the risk assessment of only one of the natural disasters for the regions. However, a risk assessment should be conducted that includes all-natural disasters for these regions. This risk assessment is dealt with by 8 different provinces in the Aegean region of Turkey in the work. A case data (1990-2020) on natural disasters such as earthquake, fire, landslide, flood, storm/typhoon has been considered for the cities covered. By revising the Fine Kinney risk assessment method for natural disasters, a risk score is obtained for each province. Then, the AHP based ELECTRE I method is applied to these provinces. As a result of this method, the riskiest region is obtained. The location of a crisis center in the riskiest region obtained should ensure effective solutions to the regions which are affected by the results of the disasters. Thus, the problem of choosing the most suitable location in the crisis center is handled with a goal programming approach.

🔖

Bu Makaleye Atıf Yap

Derse, O. (2021). Doğal Afetler için AHP Tabanlı ELECTRE I ve Matematik Model ile Risk Değerlendirmesine Ilişkin Fine Kinney Yöntemine Yeni Bir Yaklaşım. Coğrafya Dergisi, Sayı: 42, 155–164. https://ajindex.com/articles/246496

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (31)

  1. Almeida, A. T. (2005). Multicriteria modelling of repair contract based on utility and ELECTRE I method with dependability and service quality criteria. Annals of Operations Research, 138(1), 113-126. google scholar
  2. AFAD (Ministry of Interior Disaster and Emergency Management Pres-idency), https://tabb-analiz.afad.gov.tr/Default.aspx, Access Date: 25.04.2020. google scholar
  3. Chen, N., Chen, L., Tang, C., Wu, Z., Chen, A. (2019). Disaster risk evaluation using factor analysis: a case study of Chinese re-gions. Natural Hazards, 99(1), 321-335. google scholar
  4. Dagsuyu, C., Derse, O., Oturakci, M. (2021). Integrated risk prioritiza-tion and action selection for cold chain. Environmental Science and Pollution Research, 28(13), 15646-15658. google scholar
  5. Dilley, M., Chen, R. S., Deichmann, U., Lerner-Lam, A. L., Arnold, M. (2005). Natural disaster hotspots: a global risk analysis. The World Bank. google scholar
  6. Emblemsvâg, J. (2008). On probability in risk analysis of natural disas-ters. Disaster Prevention and Management: An International Jour-nal. google scholar
  7. Eyre, R., De Luca, F., Simini, F. (2020). Social media usage reveals re-covery of small businesses after natural hazard events. Nature com-munications, 11(1), 1-10. google scholar
  8. Ersoy, M., Celik, M. Y., Yeşilkaya, L., & Colak, O. (2019). Combination of Fine-Kinney and GRA methods to solve occupational health and safety problems. Journal of the Faculty of Engineering and Archi-tecture of Gazi University, 34(2), 751-770. google scholar
  9. Ganguly, K. K., Guin K. K. (2013). A fuzzy AHP approach for inbound supply risk assessment. Benchmarking: Int. J. 20(1), 129-146. google scholar
  10. Gul, M., Guven, B., Guneri, A. F. (2018). A new Fine-Kinney-based risk assessment framework using FAHP-FVIKOR incorporation. Jour-nal ofLoss Prevention in the Process Industries, 53, 3-16. google scholar
  11. Guidry, V. T., Margolis, L. H. (2005). Unequal respiratory health risk: using GIS to explore hurricane-related flooding of schools in East-ern North Carolina. Environmental research, 98(3), 383-389. google scholar
  12. Guo, J., Zhao, S., Huang, C. (2017). Valid historical data for probabilis-tic risk analysis in natural disasters. Human and Ecological Risk Assessment: An International Journal, 23(3), 474-493. google scholar
  13. Hatami-Marbini, A., Tavana, M. (2011). An extension of the Electre I method for group decision-making under a fuzzy environ-ment. Omega, 39(4), 373-386. google scholar
  14. Hong, J. D., Jeong, K. Y. (2019). Humanitarian supply chain network design using data envelopment analysis and multi-objective pro-gramming models. European Journal of Industrial Engineer-ing, 13(5), 651-680. google scholar
  15. Ivcevic, A., Mazurek, H., Siame, L., Moussa, A. B., & Bellier, O. (2019). Indicators in risk management: Are they a user-friendly in-terface between natural hazards and societal responses? Challenges and opportunities after UN Sendai conference in 2015. International Journal of Disaster Risk Reduction, 101301. google scholar
  16. Kinney, G. F., Wiruth, A. D. (1976). Practical risk analysis for safety management (No. NWC-TP-5865). Naval Weapons Center China Lake Ca. google scholar
  17. Kokangül, A., Polat, U., Dağsuyu, C. (2017). A new approximation for risk assessment using the AHP and Fine Kinney methodolo-gies. Safety science, 91, 24-32. google scholar
  18. Lee, C. H., Lin, S. H., Kao, C. L., Hong, M. Y., Shih, C. L., Chuang, C. C. (2020). Impact of climate change on disaster events in metropol-itan cities-trend of disasters reported by Taiwan national medical response and preparedness system. Environmental research, 183, 109186. google scholar
  19. Li, N., Liu, X., Xie, W., Wu, J., Zhang, P. (2013). The return period analysis of natural disasters with statistical modeling of bivariate joint probability distribution. Risk Analysis: An International Jour-nal, 33(1), 134-145. google scholar
  20. Luchuan, R. E. N. (1999). Advance in risk analysis for regional natural disasters [J]. Advance in Earth Sciences, 3. google scholar
  21. Ma, Y., Xu, W., Qin, L., Zhao, X. (2019). Site selection models in natu-ral disaster shelters: a review. Sustainability, 11(2), 399. google scholar
  22. Mabrouki, C., Bentaleb, F., Mousrij, A. (2014). A decision support methodology for risk management within a port terminal. Safety Science, 63, 124-132. google scholar
  23. Pang, J., Zhang, G., Chen, G. (2011). ELECTRE I Decision Model of Reliability Design Scheme for Computer Numerical Control Ma-chine. JSW, 6(5), 894-900. google scholar
  24. Saaty, T. L. (1980). The analytic hierarchy process, new york: Mcgrew hill. International, Translated to Russian, Portuguesses and Chi-nese, Revised edition, Paperback (1996, 2000), Pittsburgh: RWS Publications, 9, 19-22. google scholar
  25. Sukcharoen, T., Weng, J., Teetat, C. (2016). GIS-based flood risk model evaluated by Fuzzy Analytic Hierarchy Process (FAHP). In Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII (Vol. 9998, p. 999809). International Society for Optics and Photonics. google scholar
  26. Supriyadi, B., Windarto, A. P., Soemartono, T. (2018). Mungad,“Classi-fication of natural disaster prone areas in Indonesia using K-means,”. Int. J. Grid Distrib. Comput, 11(8), 87-98. google scholar
  27. Osipov, V. I., Rumyantseva, N. A., Eremina, O. N. (2019). Living with risk of natural disasters. Russian Journal ofEarth Sciences, 19(6). google scholar
  28. URL 1, https://www.nufusu.com/ilceleri/izmir-ilceleri-nufusu, Access Date: 01.05.2020. google scholar
  29. URL 2, https://www.google.com/maps, Access Date: 01.05.2020. google scholar
  30. Yilmaz, F., Ozcan, M. S. (2019). A Risk Analysis and Ranking Applica-tion for Lifting Vehicles Used in Construction Sites with Integrated AHP and Fine-Kinney Approach. Advances in Science and Technol-ogy Research Journal, 13(3). google scholar
  31. Xu, X., Liang, D., Chen, X., Zhou, Y. (2015). A risk elimination coordi-nation method for large group decision-making in natural disaster emergencies. Human and Ecological Risk Assessment: An Interna-tional Journal, 21(5), 1314-1325. google scholar