South Korea adds real-time rainband detection to flood forecasting tool

The Korea Institute of Civil Engineering and Building Technology (KICT) has added a technology for spotting hazardous rainfall patterns early to the Urban Flood Forecasting Platform run by the Han River Flood Control Office in Korea. The platform is currently being tested in the Gangnam and Kwanak areas of Seoul. The addition targets two specific rainfall threats: quasi-stationary linear rainbands, which are narrow, elongated bands of rain that stay parked over one area and dump large volumes of water, and sudden, localized torrential downpours that can send water levels in urban streams up quickly.

Both patterns can overwhelm city drainage systems built for more ordinary rain, and both are becoming more frequent as the climate changes. Linear rainbands produce rainfall that exceeds what urban drains can carry away, causing flooding. Sudden localized downpours are described as a major cause of flash floods, fast-moving currents, and people getting stranded in urban streams. KICT researchers have been part of the Urban Flood Forecasting Task Force under Korea's Ministry of Climate, Energy and Environment since November 2025, helping review the technology needed for the platform and set up how it would operate.

The detection technology works using only weather radar observation data, without other inputs. It analyzes the meteorological mechanisms and characteristics behind linear rainbands and sudden torrential rainfall to flag conditions likely to cause flooding before they fully develop. In practice, this means it can identify and track where a rainband is forming and which direction it is moving, and separately catch the early stages of a sudden downpour as it starts and intensifies. The platform then displays these detection results in real time, so operators can see the likelihood of hazardous weather as it emerges rather than waiting for rain gauges or river levels to confirm a flood is already underway.

Dr. Yoon Seong-Sim of KICT described this as an example of turning hydrometeorological research the institute has developed for urban water disaster response into something applied directly in flood forecasting. She said that once an AI-based version of the hazardous rainfall prediction technology is fully developed, it is expected to be able to forecast flooding up to two hours in advance. Her work was published in the KSCE Journal of Civil and Environmental Engineering Research.

The stated goal of the system is to secure what KICT calls the golden hour: extra lead time for people managing flood response to assess risk and act before water rises. Urban flooding is particularly dangerous because it develops fast and in places that are easy to overlook. Research on urban flood monitoring has noted that even at low water levels, fast currents on paved roads or in residential areas can cause people to fall or be swept away, since there are few obstacles to slow the water down. That same research also points out that monitoring the flood status of individual lowland houses, underpasses, underground parking lots, and road culverts is, in practice, extremely difficult to do at scale.

Providing earlier warning of hazardous rainfall does not by itself solve that monitoring gap, but it can support faster decision-making at the city level. The Urban Flood Forecasting Platform is meant to give operators monitoring information to support rapid assessment of urban flood potential, which in turn is meant to help with proactive response rather than reaction after flooding has begun.

The rainband detection work sits alongside other approaches researchers are pursuing for the same underlying problem: predicting urban flooding fast enough to act on it. One method proposes using industrial radar and signal-processing to directly measure water level and flow velocity at flood-prone sites like underpasses and underground facilities, reporting centimeter-level accuracy in indoor and river-basin testing. Another approach trains a machine-learning model, using support vector machines, on the output of established hydrodynamic simulation models rather than on historical observations, aiming to produce flood-depth predictions within seconds for areas where real flood records are scarce. These are separate research efforts from the KICT rainband work and use different techniques, but they point toward the same goal of faster, more localized flood information.

The KICT announcement itself leaves some questions open. The AI-based version of the hazardous rainfall prediction technology that would enable the two-hour advance warning is described as still in development, not yet complete. The platform is currently in pilot operation in specific Seoul districts, and the announcement does not describe results from that pilot or a timeline for wider rollout.