Climate change has been a global issue since the 19th century. The increase in rainfall variability, which covers the increase in the earth’s total precipitation, will definitely lead to frequent and more severe flood disasters. As the damage increases year after year with floods as the most chronic and costly disaster among these hazards, Korea has to improve its technological responses and countermeasures to better visualize the hazards brought about by such disasters. Gunsan City ranked number eight in the country’s most susceptible region to floods. From 2004 to 2013, Korea has experienced a total of 174 flood disasters which were estimated to cost USD 7.32 billion. But reports showed that the total expenditure of the government amounted to 1.4 times the estimated losses and damages and the private companies have spent twice the said estimated amount. To summarize, the post-disaster loss and damage reports showed underestimated values. This study aims to develop a semi-parametric geographically weighted regression which can implement a flood damage estimation model of Gunsan City. The model building process include parameters like flood depth, flood duration, inundated area, family income and land price. The datasets are composed of both untransformed and transformed data (using Box-Cox Method). Both Ordinary Least Squares (OLS) Regression and Geographically Weighted Regression (GWR) were evaluated in this study, but the search for best fit resulted to the use of GWR.

• Imee V. Necesito(Member, Master Candidate, Department of Civil and Environmental Engineering, Kongju National University)
• Tae Sung Cheong(Non –member, Ph.D., National Disaster Management Institute)
• Insang Yu(Member, Ph.D. Candidate, Department of Civil and Environmental Engineering, Kongju National University)
• Sangman Jeong(Member, Professor, Department of Civil and Environmental Engineering, Kongju National University) Corresponding Author