South Korea's Korea Institute of Civil Engineering and Building Technology (KICT) has deployed an AI-powered system that detects hazardous rainfall patterns in real time, potentially extending urban flood warnings by up to two hours. The technology analyzes weather radar data to identify linear rainbands and sudden torrential downpours before they cause flooding, giving emergency responders critical time to evacuate residents and prepare defenses.

Why Does Real-Time Rainfall Detection Matter for Cities?

Urban flooding has become increasingly dangerous as climate change intensifies extreme weather events. Traditional flood forecasting systems often struggle to predict sudden, localized downpours or narrow bands of intense rainfall that can overwhelm city drainage systems within minutes. Linear rainbands, which are elongated bands of precipitation that hover over a single area, can dump rainfall far exceeding what urban infrastructure can handle. When these systems fail to provide advance warning, the results are catastrophic: flash floods, rapid currents, stranded residents, and infrastructure damage.

The KICT system addresses this gap by using only weather radar observation data to track rainfall patterns as they develop. Rather than waiting for flooding to occur, the technology identifies the formation, movement, and intensity of dangerous weather in real time, allowing city officials to issue alerts and coordinate evacuations before water levels rise.

How Does the AI System Detect Dangerous Rainfall Patterns?

• Rainband Classification: The system analyzes meteorological characteristics of linear rainbands to identify which ones pose a flooding threat, distinguishing dangerous patterns from routine rainfall.
• Real-Time Tracking: Using radar data alone, the technology tracks the formation range and propagation path of rainbands as they move across urban areas.
• Sudden Downpour Detection: The system monitors the initiation and development of localized torrential rainfall, flagging when intensity reaches levels likely to trigger flash floods.

The Urban Flood Forecasting Platform, currently being pilot-tested in Seoul's Gangnam and Kwanak districts, provides real-time detection results to the Han River Flood Control Office and other emergency management agencies. This enables rapid assessment and decision-making about flood risk, helping secure additional response time for proactive measures.

"This achievement represents a notable example of putting into practice the hydrometeorological technology developed by KICT for urban water disaster response by applying it to urban flood forecasting," said Dr. Yoon Seong-Sim of KICT. "Once the AI-based hazardous rainfall prediction technology is fully developed, it is expected to enable flood prediction up to two hours in advance, contributing to faster response and securing critical response time."

Dr. Yoon Seong-Sim, Researcher at Korea Institute of Civil Engineering and Building Technology

What Makes This Different From Existing Flood Warning Systems?

Most conventional flood forecasting relies on historical rainfall patterns and general weather models that may not capture the sudden intensity of localized storms. The KICT system's advantage lies in its ability to detect the specific meteorological mechanisms that trigger urban flooding, rather than simply predicting rainfall amounts. By focusing on the characteristics of dangerous weather patterns themselves, the technology can provide warnings even when rainfall totals might not seem extreme on paper.

The system has been integrated into Seoul's flood response infrastructure since November 2025, when KICT researchers joined the Urban Flood Forecasting Task Force of the Ministry of Climate, Energy and Environment. The team contributed to reviewing key technologies and establishing operational procedures for the platform, ensuring the AI system works seamlessly with existing emergency management workflows.

As extreme rainfall events become more frequent due to climate change, the ability to predict floods two hours in advance could mean the difference between a managed evacuation and a disaster. For cities already facing increased flooding risk, this AI-powered early warning system represents a practical application of climate adaptation technology that directly protects lives and infrastructure.