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.

Highlights

  • 1.1 Natural disaster and riskThe frequency and intensity of extreme weather events due to climate change are rapidly increasing, causing various damages.These damages are expected to affect extreme weather events in the short term, with various long-term effects such as sea level rise and disease spread

  • There was no significant difference in mean absolute error (MAE) or root mean squared error (RMSE) between results with the test set of data and those with the verification set of data since the overfitting problem of the final model could be overlooked

  • In Stage I, this study developed a model for predicting economic losses due to natural disasters using the deep neural network (DNN) algorithm among deep learning algorithms

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Summary

Introduction

The frequency and intensity of extreme weather events due to climate change are rapidly increasing, causing various damages. These damages are expected to affect extreme weather events in the short term, with various long-term effects such as sea level rise and disease spread. Examples of extreme weather events include flooding, drought, heavy rain, tropical cyclone, heat waves, and cold waves. These extreme weather events are rapidly increasing losses associated with their increases in frequency and intensity. Western European countries such as France, Germany, and Switzerland were hit by three consecutive tropical cyclones (e.g., Anatol, Lothar, and Martin) in 1999, resulting in a loss of 13 billion euros (Ulbrich et al, 1999).

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