GIS-based seismic vulnerability assessment for the Istanbul Historical Peninsula

Abstract

According to the Index of Risk Management-INFORM 2020 Report, Turkey was included in the group of “high-risk” countries in terms of humanitarian crises and disasters with an index score of 5.0 in 2019. In statistics related to the damage caused by disasters, it is known that natural disasters cause a 3% loss in Turkey's gross national product every year, and this rate approaches 4-5% with indirect losses. Since disasters cause socioeconomic, physical, and institutional losses, attention has been given to the importance of disaster management and risk reduction studies. This paper focuses on vulnerability assessments and presents a multi-criteria decision-making and earthquake-related vulnerability assessment method by using physical and socioeconomic parameters in the Historic Peninsula. A Multi-Criteria Decision Making (MCDM) method was applied in this study because vulnerability assessments are complex and depend on many different criteria. Due to its flexible structure, the Analytical Hierarchy Process (AHP), which is one of the MCDM methods widely used in urban vulnerability assessment studies, was preferred and integrated with Geographic Information Systems. As a result of the study, it is found that approximately 49% of the district is at a moderate vulnerability level in terms of socioeconomic characteristics. For the structural characteristics, this rate is found to be at a high vulnerability level of 93%. The remaining 7% is moderately vulnerable. In this context, emphasis should be placed on identifying risky structures and strengthening and renovating them in the Historic Peninsula. The results of the method proposed in this study can be used as a basis for risk reduction studies. In addition, it can be a guide in pre-disaster risk reduction studies and can be integrated into city planning processes to keep disaster damage at minimum levels and predict the damage that may occur in settlements. The proposed method is a low-cost and short-term analysis that can be used, especially in public institutions that lack a technologically qualified workforce.