Preprints
https://doi.org/10.5194/nhess-2021-294
https://doi.org/10.5194/nhess-2021-294

  08 Oct 2021

08 Oct 2021

Review status: this preprint is currently under review for the journal NHESS.

A Strategic Framework for Natural Disaster-Induced Cost Risk Analysis and Mitigation: A Two-Stage Approach Using Deep Learning and Cost-Benefit Analysis

Ji-Myong Kim1, Sang-Guk Yum2, Hyunsoung Park3, and Junseo Bae4 Ji-Myong Kim et al.
  • 1Department of Architectural Engineering, Mokpo National University, Mokpo 58554, South Korea
  • 2Department of Civil Engineering, Gangneung-Wonju National University, Gangneung 25457, South Korea
  • 3Department of Mechanical and Civil Engineering, University of Evansville, Evansville, Indiana 47722, United States
  • 4School of Computing, Engineering and Physical Sciences, University of the West of Scotland, Paisley PA1 2BE, United Kingdom

Abstract. Due to gradual increases in the frequency and severity of natural disasters, risks to human life and property from natural disasters are exploding. To reduce these risks, various risk mitigation activities have been widely conducted. Risk mitigation activities are becoming more and more important for economic analysis of risk mitigation effects due to limited public budget and the need for economic development. To respond to this urgent need, this study aims to develop a strategic evaluation framework for natural disaster risk mitigation strategies. The proposed framework predicts natural disaster losses using a deep learning algorithm (stage I) and introduces a new methodology that quantifies the effect of natural disaster reduction projects adopting cost-benefit analysis (stage II). To achieve the main objectives of this study, data of insured loss amounts due to natural disasters associated with the identified risk indicators were collected and trained to develop the deep learning model. The robustness of the developed model was then scientifically validated. To demonstrate the proposed quantification methodology, reservoir maintenance projects affected by floods in South Korea were adopted. The results and main findings of this study can be used as valuable guidelines to establish natural disaster mitigation strategies. This study will help practitioners quantify the loss from natural disasters and thus evaluate the effectiveness of risk reduction projects. This study will also assist decision-makers to improve the effectiveness of risk mitigation activities.

Ji-Myong Kim et al.

Status: open (until 19 Nov 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Ji-Myong Kim et al.

Ji-Myong Kim et al.

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Short summary
Insurance data has been utilized with deep learning techniques to predict natural disaster damage losses in South Korea.
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