NEWS · RESEARCH · #242
Google Research paper shows ML improves global flood forecasts for ungauged watersheds
Google Research published a Nature paper, “Global prediction of extreme floods in ungauged watersheds,” reporting that machine learning models (including LSTM-based approaches) can materially improve global-scale flood forecasting where local streamflow data are scarce—extending forecast reliability on average from zero to five days and enabling Flood Hub to deliver up to seven-day river forecasts across reaches in over 80 countries; model evaluation was done in collaboration with ECMWF and the team has open-sourced related hydrology datasets.
KEY POINTS
- Google Research published a Nature paper, “Global prediction of extreme floods in ungauged watersheds,” reporting that machine learning models (including LSTM-based approaches) can materially improve global-scale flood forecasting where local streamflow data are scarce—extending forecast reliability on average from zero to five days and enabling Flood Hub to deliver up to seven-day river forecasts across reaches in over 80 countries; model evaluation was done in collaboration with ECMWF and the team has open-sourced related hydrology datasets.
- This matters because ML-based, globally trained forecasts can provide reliable warnings in data-poor regions, enabling anticipatory action that can reduce flood damage and save lives.
- Using AI to expand global access to reliable flood forecasts
WHY IT MATTERS
This matters because ML-based, globally trained forecasts can provide reliable warnings in data-poor regions, enabling anticipatory action that can reduce flood damage and save lives.