Preprints
https://doi.org/10.5194/nhess-2022-198
https://doi.org/10.5194/nhess-2022-198
 
20 Jul 2022
20 Jul 2022
Status: this preprint is currently under review for the journal NHESS.

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

Oya Kalaycioglu1,2, Serhat Emre Akhanli3, Emin Yahya Mentese4, Mehmet Kalaycioglu5, and Sibel Kalaycioglu6 Oya Kalaycioglu et al.
  • 1Department of Statistical Science, University College London, London, WC1E 6BT, United Kingdom
  • 2Depratment of Biostatistics and Medical Informatics, Bolu Abant Izzet Baysal University, Bolu, 14030, Turkey
  • 3Department of Statistics, Mugla Sitki Kocman University, Mugla, 48000, Turkey
  • 4Kandilli Observatory and Earthquake Research Institute, Bogazici University, Istanbul, 34684, Turkey
  • 5Tomorrow’s Cities Research Group, City and Regional Planning Div., Middle East Technical University, Ankara, 06800, Turkey
  • 6Department of Sociology, Middle East Technical University, Ankara, 06800, Turkey

Abstract. For an effective disaster risk mitigation plan and for building a society more resilient to natural disasters, it is essential to understand the factors that are related to social vulnerability as an important dimension to social risk. This study aims to identify the associations between socio-economic and socio-demographic household characteristics and earthquake related social vulnerability using survey data collected from 41,093 households in Istanbul. Machine learning models, namely: logistic regression, classification tree, random forest, support vector machine, naive bayes, artificial neural network, and K-nearest neighbours, were employed to classify households according to their social vulnerability status. Due to the disparity of class size for the outcome variable, subsampling strategies were applied for dealing with imbalanced data. Artificial Neural Network (ANN) was found to have the optimal predictive performance when random majority under sampling was applied (AUC: 0.813). The results from the ANN method indicated that not having social security, living in a squatter house and having high risk of job loss after an earthquake were among the most important predictors for increasing social vulnerability risk. Additionally, the level of education, the ratio of elderly persons in the household, owning a property, household size, ratio of income earners, and having savings were associated with vulnerability. An open access R-shiny web application was developed to visually display the performance of ML methods, important variables for the social vulnerability risk classification and the spatial distribution of the variables across Istanbul neighbourhoods. The machine learning methodology and the findings that we present in this paper can serve as a guidance for decision makers in identifying and prioritising action towards target groups to reduce their vulnerability risk prior to earthquakes.

Oya Kalaycioglu et al.

Status: open (until 31 Aug 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2022-198', Yi (Victor) Wang, 08 Aug 2022 reply

Oya Kalaycioglu et al.

Model code and software

R-shiny web application Oya Kalaycioglu, Serhat Emre Akhanli, Yahya Emin Mentese, Mehmet Kalaycioglu, and Sibel Kalaycioglu https://oyakalaycioglu.shinyapps.io/Social_Vulnerability/

Oya Kalaycioglu et al.

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Short summary
The relationship between household characteristics and earthquake related social vulnerability in Istanbul, Turkey was assessed using machine learning techniques. The results indicated that less educated households with no social security, risk of job loss, who live in squatter house are at higher risk of social vulnerability. We present 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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