Reply on RC1

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.