Articles | Volume 22, issue 6
https://doi.org/10.5194/nhess-22-2131-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-22-2131-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Jun 2022
Research article |
| 24 Jun 2022
| 24 Jun 2022
Strategic framework for natural disaster risk mitigation using deep learning and cost-benefit analysis
Ji-Myong Kim
Department of Architectural Engineering, Mokpo National University,
Mokpo 58554, South Korea
Sang-Guk Yum
Department of Civil Engineering, Gangneung-Wonju National University, Gangneung 25457, South Korea
Hyunsoung Park
Department of Mechanical and Civil Engineering, University of
Evansville, Evansville, Indiana 47722, United States
Junseo Bae
CORRESPONDING AUTHOR
School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley PA1 2BE, United Kingdom
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Cited
17 citations as recorded by crossref.
- Strengthening Climate Resilience: Urban Water Technologies for Heat-Resilient Physical Infrastructure in Southeast Asia Cities Amidst Extreme Temperature Events and El Niño Challenges T. Kurniawan et al. https://doi.org/10.1021/acsestwater.4c00585
- Economic Evidence for Proactive Management of Extreme Weather Disaster Risks: A Global Systematic Review Across Strategies, Hazards, and Settings J. Ogbodo et al. https://doi.org/10.3390/cli14100201
- A study of deep learning algorithm usage in predicting building loss ratio due to typhoons: the case of southern part of the Korean Peninsula J. Kim et al. https://doi.org/10.3389/feart.2023.1136346
- Crisis and disaster management: international scientific and technological trends in maintenance projects I. Munhoz et al. https://doi.org/10.1016/j.procs.2026.02.235
- Nature-Based Solutions for Large-Scale Landslide Mitigation: A Review of Sustainable Approaches, Modeling Integration, and Future Perspectives Y. Zhou et al. https://doi.org/10.3390/su18010308
- Machine learning and the economic losses in disasters: Progress and future trends M. Pedra et al. https://doi.org/10.1016/j.jnlssr.2025.100276
- Parametric Insurance for Sustainable Disaster Risk Finance: Legal, Data, and Governance Pathways in Slovenia and Croatia N. Pleterski https://doi.org/10.3390/su17219643
- Evaluating the international economic evidence of proactive disaster management strategies for extreme weather events: A global systematic review protocol. J. Ogbodo et al. https://doi.org/10.12688/hrbopenres.14350.1
- Automatic damaged vehicle estimator using enhanced deep learning algorithm J. Qaddour & S. Siddiqa https://doi.org/10.1016/j.iswa.2023.200192
- Identifying the potential participation in natural disaster insurance: first attempt based on a national socio-economic survey in Indonesia S. Oktora et al. https://doi.org/10.1108/IJDRBE-04-2022-0034
- Building loss assessment using deep learning algorithm from typhoon Rusa J. Kim et al. https://doi.org/10.1016/j.heliyon.2023.e23324
- Appraising investments in Disaster Risk Reduction (DRR): A systematic literature review T. Weerasinghe et al. https://doi.org/10.1016/j.pdisas.2025.100438
- Prioritizing stakeholder interactions in disaster management: A TOPSIS-based decision support tool for enhancing community resilience S. Elkady et al. https://doi.org/10.1016/j.pdisas.2024.100320
- Mapping Post-Earthquake Landslide Susceptibility Using U-Net, VGG-16, VGG-19, and Metaheuristic Algorithms M. Shafapourtehrany et al. https://doi.org/10.3390/rs15184501
- Robust analysis and best practices in flood economic loss assessment using HEC-FIA and GO-Consequence software for climate-ready risk management T. Poulose & S. Kumar https://doi.org/10.1016/j.ijdrr.2024.104772
- A Conceptual Approach to Predicting Seismic Events and Flood Risks Using Convolutional Neural Networks M. Rehan et al. https://doi.org/10.33093/jiwe.2025.4.2.9
- Municipal resilience bonds for earthquake risk reduction: a financing model for Istanbul’s infrastructure Y. Song & F. Medda https://doi.org/10.20935/AcadEng7947
17 citations as recorded by crossref.
- Strengthening Climate Resilience: Urban Water Technologies for Heat-Resilient Physical Infrastructure in Southeast Asia Cities Amidst Extreme Temperature Events and El Niño Challenges T. Kurniawan et al. https://doi.org/10.1021/acsestwater.4c00585
- Economic Evidence for Proactive Management of Extreme Weather Disaster Risks: A Global Systematic Review Across Strategies, Hazards, and Settings J. Ogbodo et al. https://doi.org/10.3390/cli14100201
- A study of deep learning algorithm usage in predicting building loss ratio due to typhoons: the case of southern part of the Korean Peninsula J. Kim et al. https://doi.org/10.3389/feart.2023.1136346
- Crisis and disaster management: international scientific and technological trends in maintenance projects I. Munhoz et al. https://doi.org/10.1016/j.procs.2026.02.235
- Nature-Based Solutions for Large-Scale Landslide Mitigation: A Review of Sustainable Approaches, Modeling Integration, and Future Perspectives Y. Zhou et al. https://doi.org/10.3390/su18010308
- Machine learning and the economic losses in disasters: Progress and future trends M. Pedra et al. https://doi.org/10.1016/j.jnlssr.2025.100276
- Parametric Insurance for Sustainable Disaster Risk Finance: Legal, Data, and Governance Pathways in Slovenia and Croatia N. Pleterski https://doi.org/10.3390/su17219643
- Evaluating the international economic evidence of proactive disaster management strategies for extreme weather events: A global systematic review protocol. J. Ogbodo et al. https://doi.org/10.12688/hrbopenres.14350.1
- Automatic damaged vehicle estimator using enhanced deep learning algorithm J. Qaddour & S. Siddiqa https://doi.org/10.1016/j.iswa.2023.200192
- Identifying the potential participation in natural disaster insurance: first attempt based on a national socio-economic survey in Indonesia S. Oktora et al. https://doi.org/10.1108/IJDRBE-04-2022-0034
- Building loss assessment using deep learning algorithm from typhoon Rusa J. Kim et al. https://doi.org/10.1016/j.heliyon.2023.e23324
- Appraising investments in Disaster Risk Reduction (DRR): A systematic literature review T. Weerasinghe et al. https://doi.org/10.1016/j.pdisas.2025.100438
- Prioritizing stakeholder interactions in disaster management: A TOPSIS-based decision support tool for enhancing community resilience S. Elkady et al. https://doi.org/10.1016/j.pdisas.2024.100320
- Mapping Post-Earthquake Landslide Susceptibility Using U-Net, VGG-16, VGG-19, and Metaheuristic Algorithms M. Shafapourtehrany et al. https://doi.org/10.3390/rs15184501
- Robust analysis and best practices in flood economic loss assessment using HEC-FIA and GO-Consequence software for climate-ready risk management T. Poulose & S. Kumar https://doi.org/10.1016/j.ijdrr.2024.104772
- A Conceptual Approach to Predicting Seismic Events and Flood Risks Using Convolutional Neural Networks M. Rehan et al. https://doi.org/10.33093/jiwe.2025.4.2.9
- Municipal resilience bonds for earthquake risk reduction: a financing model for Istanbul’s infrastructure Y. Song & F. Medda https://doi.org/10.20935/AcadEng7947
Saved (final revised paper)
Latest update: 25 Oct 2026
Short summary
Insurance data has been utilized with deep learning techniques to predict natural disaster damage losses in South Korea.
Insurance data has been utilized with deep learning techniques to predict natural disaster...
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