The city/county-level flood risk index is a qualitative flood risk assessment result derived using 100 m × 100 m grid data for each sub-indicator, flood risk maps, and a genetic algorithm. The index comprehensively evaluates annual flood risk at the city/county level using 11 sub-indicators, including flood risk maps for national, local, and small streams, maximum rainfall, number of buildings, farmland area, number of older buildings, and disaster prevention facility density. To improve accuracy, only assets exposed to flood risk were selected for use in the evaluation, and genetic algorithm-based optimal weights were applied to minimize the error relative to the heavy-rain and typhoon damage rankings reported in the Disaster Yearbook. The sub-indicator indices and the city/county-level flood risk index and rankings derived through this analysis provide a scientific basis for decision-making related to disaster prevention and flood control policies.

The grid-level flood risk index represents the results of annual flood risk assessments conducted from 2016 to 2023 at 100 m × 100 m grid units within the expected inundation areas around rivers in Gyeonggi-do, using grid data for nine sub-indicators and flood risk maps for national, local, and small streams under extreme rainfall events. The flood risk index is classified into 10 grades by applying entropy weights and Euclidean distance to the nine grid-level sub-indicator indices calculated for each year. The grid-level flood risk index enables the precise identification of areas with high concentrations of buildings, population, farmland, and other assets exposed to flood risk within cities and counties with high city/county-level flood risk. Because flood risk is assessed at the 100 m × 100 m grid level, the index can also be used effectively for localized disaster response and for spatially tailored policy development at the project-district level.

Information on flood-vulnerable facilities in Gyeonggi-do was constructed by integrating attribute data from building registration records and LiDAR-based high-resolution spatial data. Spatial information from LiDAR, including building location, height, and area, was combined with registration attributes—use, floors, structure, construction year—to identify facilities vulnerable to extreme rainfall, such as basements or old buildings. This data can support practical policy-making, including disaster response planning and prioritization of disaster prevention facilities.

The Flood Risk Map visualizes the extent and depth of inundation caused by embankment failure or overtopping under extreme flood conditions that exceed the design frequency. It covers national and local rivers and provides scenario-based flood risk maps based on planned flood levels for different return periods. The Urban Flood Map shows the extent and depth of inland flooding, classified into five levels, in urban areas under the assumption that stormwater drainage systems exceed capacity or malfunction. Both maps are produced and distributed by the Ministry of Environment and are used in disaster management policy, including disaster risk area designation, disaster prevention facility planning, and early warning system development.

A flood trace map is spatial data recording the extent and depth of flood damage from typhoons or heavy rainfall based on field surveys and measurements. In accordance with the Natural Disaster Countermeasures Act, local government heads prepare it within six months of the damage, and it is provided by the Ministry of the Interior and Safety. The map includes information on flood extent, depth, and duration, and is used to identify frequently flooded areas, designate disaster risk zones, establish recovery plans, and determine priorities for installing disaster prevention facilities.