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Modeling destructive earthquake casualties based on a comparative study for Turkey

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  • S. Turkan
  • G. Özel

Abstract

The statistical modeling of destructive earthquakes is an indispensable tool for extracting information for prevention and risk reduction casualties after destructive earthquakes in a seismic region. The linear regression (LR) model can reveal the relation between casualty rate and related covariates based on earthquake catalog. However, if some covariates affect the casualty rate parametrically and some of them nonparametrically, the LR model may entail serious bias and loss of power when estimating or making inference about the effect of parameters. We suggest that semi-parametric beta regression (SBR), semi-parametric additive regression (SAR), and beta regression (BR) models could provide a more suitable description than the LR model to analyze the observed casualties after destructive earthquakes. We support this argument using destructive earthquakes occurred in Turkey between 1900 and 2012 having surface wave magnitudes five or more. The LR, SAR, BR, and SBR models are compared within the context of this data. The data strongly support that the SBR and SAR models can lead to more precise results than the BR and LR models. Furthermore, the SBR is the best model for the earthquake data since the beta distribution provides a flexible model that can be used to analyze the data involving proportions or rates. The results from this model suggest that the casualty rate depends on energy, damaged buildings, and the number of aftershocks of a destructive earthquake. Copyright Springer Science+Business Media Dordrecht 2014

Suggested Citation

  • S. Turkan & G. Özel, 2014. "Modeling destructive earthquake casualties based on a comparative study for Turkey," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 72(2), pages 1093-1110, June.
    • Handle: RePEc:spr:nathaz:v:72:y:2014:i:2:p:1093-1110
    DOI: 10.1007/s11069-014-1059-x
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    Cited by:

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    2. Muhammet Gul & Ali Fuat Guneri, 2016. "An artificial neural network-based earthquake casualty estimation model for Istanbul city," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 84(3), pages 2163-2178, December.
    3. Xuejun Jiang & Yunxian Li & Aijun Yang & Ruowei Zhou, 2020. "Bayesian semiparametric quantile regression modeling for estimating earthquake fatality risk," Empirical Economics, Springer, vol. 58(5), pages 2085-2103, May.
    4. Tongyan Zheng & Lei Li & Chong Xu & Yuandong Huang, 2023. "Spatiotemporal Analysis of Earthquake Distribution and Associated Losses in Chinese Mainland from 1949 to 2021," Sustainability, MDPI, vol. 15(11), pages 1-15, May.
    5. Chaoxu Xia & Gaozhong Nie & Huayue Li & Xiwei Fan & Wenhua Qi, 2023. "A composite database of casualty-inducing earthquakes in mainland China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(3), pages 3321-3351, April.
    6. Manhao Luo & Shuangyun Peng & Yanbo Cao & Jing Liu & Bangmei Huang, 2023. "Earthquake fatality prediction based on hybrid feature importance assessment: a case study in Yunnan Province, China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(3), pages 3353-3376, April.
    7. Xia Chaoxu & Nie Gaozhong & Fan Xiwei & Li Huayue & Zhou Junxue & Zeng Xun, 2022. "A new model for the quantitative assessment of earthquake casualties based on the correction of anti-lethal level," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 110(2), pages 1199-1226, January.

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