This study develops a framework to quantify the flood risk vulnerability in South Korea by considering climate change impacts. On the basis of the concept of exposure-sensitivity-adaptive capacity, 21 proxy variables are selected and screened, and their weights are determined for their objectivity by using the Delphi technique. The data from 16 provinces of South Korea and the weighting values of all proxy variables are fuzzified to consider uncertainty. In addition, the National Center for Atmosphere Research Community Climate System Model 3 (CCSM3) in conjunction with the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emission Scenario (SRES) A1B, A2, B1, A1T, A1FI, and B2 are used for future climate data (2020s, 2050s, and 2080s). Therefore, 19 flood risk vulnerabilities of South Korea, including present conditions, are quantitatively evaluated and compared. Three Multi-Criteria Decision Making (MCDM) techniques - Weighted Sum Method (WSM), Technique for Order Preference by Similarity to Ideal Situation (TOPSIS), and fuzzy TOPSIS - are used to quantify all spatial vulnerabilities. As a result, some fuzzy TOPSIS rankings are quite different to those of WSM and TOPSIS, and the ranking patterns of the 19 climate change scenarios are also derived in a dissimilar way. In addition, if the variances of the provinces' rankings are considered, some provinces showing low values can plan their climate change adaptation strategies by taking into consideration their relatively certain rankings. In the end, the vulnerability assessment for climate change should consider not only various MCDM techniques but also the uncertainty of weighting values and proxy variable data.
Bibliographical noteFunding Information:
This study was supported, in part, by the Construction Technology Innovation Program (08-Tech-Inovation-F01) of the Research Center of Flood Defense Technology for the Next Generation of the Ministry of Land, Transport, and Maritime Affairs (MLTM) (60%). It was also supported by the Basic Science Research Program of the National Research Foundation of Korea (NRF), which is funded by the Ministry of Education, Science, and Technology (2010-0010609) (40%).
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Artificial Intelligence