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.
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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EGUsphere, https://doi.org/10.5194/egusphere-2025-1169, https://doi.org/10.5194/egusphere-2025-1169, 2025
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1. A total of 112 landslide locations were identified in the Jecheon-si region, South Korea, based on aerial photos, dronographs and Google Earth imagery. 2. GIS-based statistical models (i.e., FR, IV, CF and LR) were used for landslide susceptibility mapping. 3. The ROC curve, R-index, MAE, MSE, RMSE, and precision were used to assess the model's. 4. The LSI predicted using an integrated model exhibited good agreement with topographic and landslide characteristics.
Jérémie Tuganishuri, Chan-Young Yune, Gihong Kim, Seung Woo Lee, Manik Das Adhikari, and Sang-Guk Yum
Nat. Hazards Earth Syst. Sci., 25, 1481–1499, https://doi.org/10.5194/nhess-25-1481-2025, https://doi.org/10.5194/nhess-25-1481-2025, 2025
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To reduce the consequences of landslides due to rainfall, such as loss of life, economic losses, and disruption to daily living, this study describes the process of building a machine learning model which can help to estimate the volume of landslide material that can occur in a particular region, taking into account antecedent rainfall, soil characteristics, type of vegetation, etc. The findings can be useful for land use management, infrastructure design, and rainfall disaster management.
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
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This study performed analysis on typhoon-induced coastal morphodynamics for the Mokpo coast. Wetland vegetation was severely impacted by Typhoon Soulik, with 87.35 % of shoreline transects experiencing seaward migration. This result highlights the fact that sediment resuspension controls the land alteration process over the typhoon period. The land accretion process dominated during the pre- to post-typhoon periods.
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
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The prediction of the size of rainfall-induced debris in South Korea was analyzed. The model suitability was carried out and Random forest was the most suitable for the Size of debris prediction. The most contributing factor in the model was slope length and the most vulnerable region to higher frequency and severe debris was Gangwon province. The findings may be used for rainfall induced-debris prevention policies and post-disaster rehabilitation planning.
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
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This study advances previous models through machine learning and multi-sensor-verified results. Using spatial and meteorological data from the study area (Suncheon–Wanju Highway in Gurye-gun), the amount and location of black ice were modelled based on system dynamics to predict black ice and then simulated with the geographic information system (m2). Based on the model results, multiple sensors were buried at four selected points in the study area, and the model was compared with sensor data.
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
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In this study, emerging engineering techniques such as machine learning and deep learning technique was applied to predict heavy snowfall prediction in the Korean Peninsula. More specifically, it was observed that the predictive model using the RFR algorithm had the best performance based on a comparison between the observed and predicted data. In addition, it was observed that the performance of the ensemble models (RFR and XGB) was better than that of the single regression models.
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
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Developed statistical models to predict the non-exceedance probability of extreme storm surge-induced typhoons. Various probability distribution models were applied to find the best fitting to empirical storm-surge data.
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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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