March 20, 2024via Google Research Blog
Using AI to expand global access to reliable flood forecasts
Why it matters
Google's machine learning system is solving a massive real-world problem at scale: providing reliable flood forecasts to 1.5 billion people in data-scarce regions. This demonstrates how AI can be operationalized for humanitarian impact while advancing climate resilience globally.
Key signals
- $50 billion in annual flood damages worldwide
- 1.5 billion people (19% of world population) exposed to severe flood risk
- Flood-related disasters doubled since 2000
- ML extends forecast reliability from 0 to 5 days average lead time
- Coverage expanded to 80+ countries via Flood Hub
- Up to 7-day advance river forecasts now available
- 5,680 streamflow gauges used for model training (1980-2023)
- LSTM-based model matches GloFAS nowcast accuracy at 4-5 day lead times
- Research published in Nature (peer-reviewed)
- Collaboration with WMO, Red Cross, Yale, JKU Institute for Machine Learning
The hook
Not a pilot. Google deployed ML-powered flood forecasts across 80+ countries—extending warning time from zero to five days.
Posted by Yossi Matias, VP Engineering & Research, and Grey Nearing, Research Scientist, Google Research
Floods are the most common natural disaster, and are responsible for roughly $50 billion in annual financial damages worldwide. The rate of flood-related disasters has more than doubled since the year 2000 partly due to climate change. Nearly 1.5 billion people, making up 19% of the world’s population, are exposed to substantial risks from severe flood events. Upgrading early warning systems to make accurate and timely information accessible to these populations can save thousands of lives per year.
Driven by the potential impact of reliable flood forecasting on people’s lives globally, we started our flood forecasting effort in 2017. Through this multi-year journey, we advanced research over the years hand-in-hand with building a real-time operational flood forecasting system that provides alerts on Google Search, Maps, Android notifications and through the Flood Hub. However, in order to scale globally, especially in places where accurate local data is not available, more research advances were required.
In “Global prediction of extreme floods in ungauged watersheds”, published in Nature, we demonstrate how machine learning (ML) technologies can significantly improve global-scale flood forecasting relative to the current state-of-the-art for countries where flood-related data is scarce. With these AI-based technologies we extended the reliability of currently-available global nowcasts, on average, from zero to five days, and improved forecasts across regions in Africa and Asia to be similar to what are currently available in Europe. The evaluation of the models was conducted in collaboration with the European Center for Medium Range Weather Forecasting (ECMWF).
These technologies also enable Flood Hub to provide real-time river forecasts up to seven days in advance, covering river reaches across over 80 countries. This information can be used by p...
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