Recently, an increase in the occurrence of sudden local flooding of great volume and short duration due to global climate changes has occasioned the significant danger and loss of life and property in Korea as well as most parts of the world. Most cities have important infrastructures, such as roads, and they also have high population densities. For this reason, cities are severely damaged if inundation occurs in the cities.
Looking into previous research, most of the studies are about flood risk index for large units such as a city or a large basin. On the other hands, this study is about the development of flood risk index of an intensive unit targeted at roads which are the basis of all means of transportation.
To calculate the real-time flood risk index on roads; the flooding history, the rainfall range causing flooding, flooding expectation of roads, low-lying elements, probability frequency precipitation and real-time accumulated rainfall, are used as factors.
One of factors, rainfall range causing flooding is named in this study and it means the criterion of rainfall which there is a possibility of flooding on a road using the rainfall information when a road was flooded in the past.
Rainfall is the most important factor to calculate the flood risk index. However, the rainfall isn''t actually observed at a road. For this reason, the revised methods of missing rainfall in the field of hydrometeorology are introduced in this study. This means a road is regarded as a missing rainfall site and the rainfall of a road is calculated using observed rainfalls around the area.
The Inverse Distance Weighting (IDW) and Coefficient of Correlation Weighting (CCW) methods are selected among many revised methods of missing rainfall because they are suitable for processing by computer systems and the most commonly used methods in weather-related systems.
The ultimate goal of this study is to suggest a model to develop the real-time road''s flood risk index using IDW and CCW and to compare and analyze their results of simulation in the research object region, Busan.
The algorithm for developing a model of the real-time road''s flood risk index is decided using data related to flooded roads in the last five years, from 2007 to 2011. In total, nine cases are simulated using data of flooded roads in the last nine years from 2007 to 2014 to verify the algorithm.
The results show CCW which considers the relevance of data, produced excellent results as compared with IDW which considers the spatial correlation if the difference of real-time rainfalls of observing points is relatively small. Although, IDW is appropriate if the difference of real-time rainfalls of observing points is remarkably big in the case of 2014. However, in this case, it is suitable to inform drivers of flood risk regions like unit of GU to prevent inundation instead of informing road''s flood risk index.