# Using AI to expand global access to reliable flood forecasts

DevFeed: [Using AI to expand global access to reliable flood forecasts](<https://devfeed.tech/articles/using-ai-to-expand-global-access-to-reliable-flood-forecasts-28566.md>)

Original publisher: [Read original article](<http://blog.research.google/2024/03/using-ai-to-expand-global-access-to.html>)

Author: Google AI (noreply@blogger.com)

Published: 2024-03-20T16:06:00Z

Content type: article

Language: en

Sources: [Google Research](<https://devfeed.tech/sources/google-research.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Google](<https://devfeed.tech/topics/google.md>), [real-time](<https://devfeed.tech/topics/real-time.md>), [data](<https://devfeed.tech/topics/data.md>), [notifications](<https://devfeed.tech/topics/notifications.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [data](<https://devfeed.tech/tags/data.md>), [environment](<https://devfeed.tech/tags/environment.md>), [google](<https://devfeed.tech/tags/google.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [notifications](<https://devfeed.tech/tags/notifications.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [research](<https://devfeed.tech/tags/research.md>), [weather](<https://devfeed.tech/tags/weather.md>)

## AI overview

Google Research describes how AI and machine learning improved global flood forecasting in regions with scarce flood-related data. The work extended the average reliability of global nowcasts from zero to five days and supports real-time river forecasts up to seven days ahead across more than 80 countries.

## Source excerpt

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 people, communi