A flood map reaches places without gauges.

The human problem

Many flood-prone communities lack dense river gauges or local observations, especially for fast urban flooding.

The prior constraint

Hydrological models need measurements, while public reports arrive in inconsistent language and formats.

AI’s actual role

Flood models combine weather and hydrology; Groundsource structures public reports into additional event data.

The documented result

Google reports the coverage figures and provides public forecasts/API access. Coverage is not a measure of accuracy or warning delivery.

Why it may matter

A public forecast can help close an information gap only when local responders can interpret and act on it.

Limitations

Coverage and population counts are company-reported.

Flash-flood accuracy depends on highly local drainage and rainfall conditions.

The service is not a substitute for official warnings.

Unresolved questions

What are false-negative and false-positive rates by region?

How quickly do Groundsource reports become usable forecast data?

Which communities can receive and act on warnings?