Natural Hazards and Earth System Sciences
Natural Hazards and Earth System Sciences
NHESS
Articles | Volume 23, issue 6
Articles | Volume 23, issue 6
https://doi.org/10.5194/nhess-23-2133-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-23-2133-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
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

Viewed

Total article views: 3,791 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,705 997 89 3,791 126 124 109
  • HTML: 2,705
  • PDF: 997
  • XML: 89
  • Total: 3,791
  • Supplement: 126
  • BibTeX: 124
  • EndNote: 109
Views and downloads (calculated since 20 Jul 2022)
Cumulative views and downloads (calculated since 20 Jul 2022)

Viewed (geographical distribution)

Total article views: 3,791 (including HTML, PDF, and XML) Thereof 3,636 with geography defined and 155 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 25 Nov 2025
Download
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.
Read more
Share
Special issue
Advances in machine learning for natural hazards risk...
Altmetrics
Final-revised paper
Preprint