Articles | Volume 22, issue 6
https://doi.org/10.5194/nhess-22-2131-2022
https://doi.org/10.5194/nhess-22-2131-2022
Research article
 | 
24 Jun 2022
Research article |  | 24 Jun 2022

Strategic framework for natural disaster risk mitigation using deep learning and cost-benefit analysis

Ji-Myong Kim, Sang-Guk Yum, Hyunsoung Park, and Junseo Bae

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Cited articles

Ajayi, A., Oyedele, L., Owolabi, H., Akinade, O., Bilal, M., Delgado, J. M. D., and Akanbi, L.: Deep learning models for health and safety risk prediction in power infrastructure projects, Risk Anal., 40, 2019–2039, 2019. 
Al Najar, M., Thoumyre, G., Bergsma, E. W., Almar, R., Benshila, R., and Wilson, D. G.: Satellite derived bathymetry using deep learning, Mach. Learn., 1–24, https://doi.org/10.1007/s10994-021-05977-w, 2021. 
Bae, S. W. and Yoo, J. S.: Apartment price estimation using machine learning: Gangnam-gu, Seoul as an example, Real Estate Stud., 24, 69–85, 2018. 
Bae, J., Yum, S. G., and Kim, J. M.: Harnessing machine learning for classifying economic damage trends in transportation infrastructure projects, Sustainability, 13, 1–12, https://doi.org/10.3390/su13116376, 2021. 
Blake, E. S., Landsea, C., and Gibney, E. J.: The deadliest, costliest, and most intense United States tropical cyclones from 1851 to 2010 (and other frequently requested hurricane facts), https://repository.library.noaa.gov/view/noaa/6929 (last access: 20 June 2022), 2011. 
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Insurance data has been utilized with deep learning techniques to predict natural disaster damage losses in South Korea.
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