ASSENSING DISASTER RESILINCE: A CASE STUDY OF THE TR71 REGION

Year 2025, Volume: 13 Issue: 3, 683 - 711, 01.09.2025

Hakan Gündüz , Tamer Eren

https://doi.org/10.36306/konjes.1550099

Abstract

The sustainability of any social unit today largely depends on its resilience to disasters. Disaster resilience is the ability of a social unit to cope with an extraordinary impact (earthquake, fire, flood, etc.). Disaster resilience is also defined as the ability and endurance of a society exposed to a disaster to return to its pre-disaster state. Therefore, determining disaster resilience can guide studies such as planning, prioritization and resource allocation in disaster management studies.
In this article, an analiysis was conducted to determine the disaster resilience of Kırıkkale, Kırşehir, Nevşehir, Aksaray and Niğde provinces, which are grouped in plans and programs as TR71 provinces. These provinces are located on the central part of Türkiye, with respect to their economic and statistical properties. The disaster resiliences of the provinces were analyzed within the framework of four main criteria by using multi-criteria decision-making methods of Analytical Network Process (ANP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and PROMETHEE. The resiliences of provinces were determined with 11 sub-criteria and 39 indicators under the main criteria of economic, social, infrastructure and natural environment. This study is anticipated to provide substantial insights for decision-makers involved in disaster planning by highlighting the strengths and weaknesses of each province, thereby informing future strategies related to planning, budgeting, and investment. In this paper, it was determined that the provinces with the highest disaster resistance were Nevşehir and Aksaray, and the provinces with the lowest disaster resistance were Kırıkkale and Niğde. Kırşehir ranks in the middle in all methods.

Keywords

Disaster Management , Disaster Resilience , Resilience Indicators , Multi-Criteria Decision Making , Multi-Attribute Decision Making

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