Precision Forecasting for 100+ Countries
Google’s flood-prediction AI has been operating for several years, but its latest iteration delivers higher-resolution, location-specific forecasts that can warn communities days in advance.
More than 100 countries now rely on these alerts to mobilize first responders, stage relief supplies, and communicate life-saving guidance to residents before the waters rise.
Virtual Gauges Fill Critical Data Gaps
Traditional river gauges are sparse, especially in low-income regions, so Google’s team built “virtual gauges” that simulate stream-flow data in places where physical sensors don’t exist.
Over 250,000 of these virtual monitoring points feed real-time and historical data into the model, sharpening forecast accuracy even in remote areas with no conventional hydrological network.
Flood Hub: Real-Time Insights at a Glance
All model outputs are compiled in Flood Hub, a free, web-based platform that visualizes flood risk on an interactive map and summarizes impact projections.
Users can review historical trends, monitor current river conditions, and download detailed forecast data via an upcoming public API—ideal for governments, NGOs, and insurers seeking deeper integration.
Proven Success: Rio de Janeiro
When catastrophic floods struck Rio de Janeiro in May 2024, Google’s system flagged the escalating risk early, giving city officials and humanitarian organizations crucial lead time to evacuate vulnerable neighborhoods and deploy emergency shelters.
Post-event analysis prompted Brazilian authorities to upgrade their disaster-response plans, confident that AI-driven alerts will keep improving outcomes.
Early Warning Saves Lives and Property
Once a river breaches its banks, options are limited. Accurate, timely predictions, delivered through accessible digital channels, are the strongest defense against economic loss and tragic fatalities.
Google’s machine-learning model continually refines its forecasts as new data streams in, ensuring each season’s warnings are more precise than the last.