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

Related authors

Assessing Typhoon Soulik-induced morphodynamics over the Mokpo coastal region in South Korea based on a geospatial approach
Sang-Guk Yum, Moon-Soo Song, and Manik Das Adhikari
Nat. Hazards Earth Syst. Sci., 23, 2449–2474, https://doi.org/10.5194/nhess-23-2449-2023,https://doi.org/10.5194/nhess-23-2449-2023, 2023
Short summary
Prediction of landslide induced debris’ severity using machine learning algorithms: a case of South Korea
Tuganishuri Jérémie, Chan-Young Yune, Gihong Kim, Seung Woo Lee, Manik Adhikari, and Sang-Guk Yum
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2023-73,https://doi.org/10.5194/nhess-2023-73, 2023
Manuscript not accepted for further review
Short summary
Development of black ice prediction model using GIS-based multi-sensor model validation
Seok Bum Hong, Hong Sik Yun, Sang Guk Yum, Seung Yeop Ryu, In Seong Jeong, and Jisung Kim
Nat. Hazards Earth Syst. Sci., 22, 3435–3459, https://doi.org/10.5194/nhess-22-3435-2022,https://doi.org/10.5194/nhess-22-3435-2022, 2022
Short summary
A Comparative Analysis of Machine Learning Algorithms for Snowfall Prediction Models in South Korea
Moon-Soo Song, Hong-Sik Yun, Jae-Joon Lee, and Sang-Guk Yum
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2022-118,https://doi.org/10.5194/nhess-2022-118, 2022
Manuscript not accepted for further review
Short summary
Estimation of the non-exceedance probability of extreme storm surges in South Korea using tidal-gauge data
Sang-Guk Yum, Hsi-Hsien Wei, and Sung-Hwan Jang
Nat. Hazards Earth Syst. Sci., 21, 2611–2631, https://doi.org/10.5194/nhess-21-2611-2021,https://doi.org/10.5194/nhess-21-2611-2021, 2021
Short summary

Related subject area

Risk Assessment, Mitigation and Adaptation Strategies, Socioeconomic and Management Aspects
Mangrove ecosystem properties regulate high water levels in a river delta
Ignace Pelckmans, Jean-Philippe Belliard, Luis E. Dominguez-Granda, Cornelis Slobbe, Stijn Temmerman, and Olivier Gourgue
Nat. Hazards Earth Syst. Sci., 23, 3169–3183, https://doi.org/10.5194/nhess-23-3169-2023,https://doi.org/10.5194/nhess-23-3169-2023, 2023
Short summary
Analysis of flood warning and evacuation efficiency by comparing damage and life-loss estimates with real consequences related to the São Francisco tailings dam failure in Brazil
André Felipe Rocha Silva and Julian Cardoso Eleutério
Nat. Hazards Earth Syst. Sci., 23, 3095–3110, https://doi.org/10.5194/nhess-23-3095-2023,https://doi.org/10.5194/nhess-23-3095-2023, 2023
Short summary
Criteria-based visualization design for hazard maps
Max Schneider, Fabrice Cotton, and Pia-Johanna Schweizer
Nat. Hazards Earth Syst. Sci., 23, 2505–2521, https://doi.org/10.5194/nhess-23-2505-2023,https://doi.org/10.5194/nhess-23-2505-2023, 2023
Short summary
Low-regret climate change adaptation in coastal megacities – evaluating large-scale flood protection and small-scale rainwater detention measures for Ho Chi Minh City, Vietnam
Leon Scheiber, Christoph Gabriel David, Mazen Hoballah Jalloul, Jan Visscher, Hong Quan Nguyen, Roxana Leitold, Javier Revilla Diez, and Torsten Schlurmann
Nat. Hazards Earth Syst. Sci., 23, 2333–2347, https://doi.org/10.5194/nhess-23-2333-2023,https://doi.org/10.5194/nhess-23-2333-2023, 2023
Short summary
Modeling compound flood risk and risk reduction using a globally applicable framework: a pilot in the Sofala province of Mozambique
Dirk Eilander, Anaïs Couasnon, Frederiek C. Sperna Weiland, Willem Ligtvoet, Arno Bouwman, Hessel C. Winsemius, and Philip J. Ward
Nat. Hazards Earth Syst. Sci., 23, 2251–2272, https://doi.org/10.5194/nhess-23-2251-2023,https://doi.org/10.5194/nhess-23-2251-2023, 2023
Short summary

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. 
Download
Short summary
Insurance data has been utilized with deep learning techniques to predict natural disaster damage losses in South Korea.
Altmetrics
Final-revised paper
Preprint