Articles | Volume 23, issue 6
Research article
15 Jun 2023
Research article |  | 15 Jun 2023

Using machine learning algorithms to identify predictors of social vulnerability in the event of a hazard: Istanbul case study

Oya Kalaycıoğlu, Serhat Emre Akhanlı, Emin Yahya Menteşe, Mehmet Kalaycıoğlu, and Sibel Kalaycıoğlu

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Cited articles

Abarca-Alvarez, F. J., Reinoso-Bellido, R., and Campos-Sánchez, F. S.: Decision Model for Predicting Social Vulnerability Using Artificial Intelligence, ISPRS Int. J. Geo-Inf., 8, 575,, 2019. a, b, c, d, e
Acar, s., Karagoz, T., Meydan, M. C., Sahin Cinoglu, D., Kaygisiz, G., and Isik, M.: Ilcelerin sosyo-ekonomik gelismislik siralamasi arastirmasi – SEGE 2022 (Research on the socio-econimic development ranking of districts), Tech. Rep. 35, Republic Of Turkey Ministry of Industry and Technology, General Directorate of Development Agencies, (last access: 20 March 2023), 2022. a, b
Adaman, F., Aslan, D., Erus, B., and Sayan, S.: ESPN Thematic Report on in-work poverty in Turkey, Tech. rep., European Commission, Brussels,​​​​​​​ (last access: 20 March 2023), 2015. a
AFAD: Disaster and Management Presidency of Turkey – 2019 Overview of Disaster Management and Natural Disaster Statistics, Tech. rep., AFAD,​​​​​​​ (last access: 26 March 2023), 2019. a, b
Akhanli, S. E. and Hennig, C.: Comparing clusterings and numbers of clusters by aggregation of calibrated clustering validity indexes, Stat. Comput., 30, 1523–1544,, 2020. a
Short summary
The associations between household characteristics and hazard-related social vulnerability in Istanbul, Türkiye, were assessed using machine learning techniques. The results indicated that less educated households with no social security and job insecurity that live in squatter houses are at a higher risk of social vulnerability. We present the findings in an open-access R Shiny web application, which can serve as a guidance for identifying the target groups in the interest of risk mitigation.
Final-revised paper