In this study, the input-output model (IO model) was used to quantitatively estimate indirect damages caused to industries by those that are directly impacted by natural disasters; the corresponding impact on the national economy was also evaluated. Indirect damages from other industries due to direct damages of natural disasters were mainly observed in the manufacturing industry, while the impact on the service industry was minimal. For example, natural disasters have led to the largest direct damages to the construction industry, as well as to industries involved in the manufacture of textiles, leather, and transport equipment. The results of the evaluation of the impact on the national economy, by comparing the average annual GDP and natural disaster damage, showed that the total damage (direct damage + indirect damage) is almost three times higher than when only considering direct damage. Therefore, in order to implement effective disaster management, it is necessary to consider indirect damages by industries along with direct damages during decision making.

Many researches are conducted to evaluate benefit from local disaster prevention projects. However, since such evaluation can only be assessed at the local district level, there is a limitation to applying this assessment to the local government's disaster prevention management in that it requires the local characteristics. To compensate for this, in this study, the effect of disaster damage reduction through local governments' local disaster prevention budget was estimated, and that used for disaster damage reduction in the area was calculated. Further, the effect was applied to the damage prediction model that is typical local government-level disaster prevention management, and heavy rain damage prediction was done considering the effect. As a result of applying it to the study area, the effect was prominent in Pyeongtaek-si and Pocheon-si in Gyeonggi-do. Also, considering the results of the effect, it was confirmed that error in the heavy rain damage prediction function was reduced. The results of this study can be used to establish disaster prevention budget management and disaster mitigation plans.