Articles | Volume 23, issue 6
https://doi.org/10.5194/nhess-23-2133-2023
https://doi.org/10.5194/nhess-23-2133-2023
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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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.
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