What does Flood Hub do?
Flood Hub takes leading weather data from around the world and uses AI to create prediction alerts on a map. This is a major leap from traditional flood models, which typically require local historical data like water levels for calibration. Our AI allows us to pull from information across the world to provide predictions in locations even without historical data — which is critical since people who need warnings the most usually live in data-scarce regions.
When we first built the Flood Hub tool, it was limited to predicting when a river would flood. Over the last few years, we’ve built a new AI methodology called Groundsource, which expanded our coverage to include urban flash flooding.
Why did it take us longer to integrate urban flash flooding into Flood Hub?
We had a huge amount of global data on river floods and almost none on urban flash floods. Flood Hub predicts river floods with two AI models that process a range of publicly available data sources. The Hydrologic Model uses weather and land conditions to forecast the amount of water that will flow through a river, and the Inundation Model uses streamflow data to predict what areas will be affected. There are a lot of physical gauges — or rulers — in rivers around the world to measure water levels over time, which helps with those predictions. It’s really unusual to find sensors monitoring flash flooding in urban areas, so we don’t have that kind of historical data for urban flash floods.
Where did you go to find data on urban flooding?
Without an authoritative global data set on urban flooding, we knew we’d need to build it ourselves. That’s when we came up with the idea for Groundsource. First, it used Gemini to read over 5 million news reports about flooding spanning a 20-year period, creating a massive, unique dataset of 2.6 million historical flood events in more than 150 countries. We then integrated that data into a new urban flash flood model that’s live in Flood Hub.
