Wednesday, March 20, 2024

g-f(2)2111 Harnessing the Power of AI: Google's Innovative Approach to Reliable Flood Forecasting

 


genioux Fact post by Fernando Machuca and Claude


Introduction:


In a groundbreaking research effort, Google has demonstrated the transformative potential of artificial intelligence (AI) and machine learning (ML) in advancing global flood forecasting, particularly in regions where flood-related data is scarce. The article "Using AI to expand global access to reliable flood forecasts" by Yossi Matias and Grey Nearing highlights how these cutting-edge technologies can significantly improve the accuracy and timeliness of flood predictions, empowering vulnerable populations to take anticipatory action and mitigate the devastating impact of floods.



genioux GK Nugget:


"Google's AI-powered flood forecasting system revolutionizes early warning capabilities, extending the reliability of global nowcasts and improving forecasts in data-scarce regions, potentially saving thousands of lives annually." — Fernando Machuca and Claude



Classical Summary:


Google's multi-year flood forecasting initiative, launched in 2017, has made significant strides in leveraging AI and ML to enhance global flood forecasting accuracy and accessibility. The research, conducted in collaboration with academic institutions, governments, international organizations, and NGOs, has led to the development of a real-time operational flood forecasting system that provides alerts through various Google platforms and the Flood Hub.


A key breakthrough in this effort is the ability of ML technologies to improve flood forecasting in countries where flood-related data is scarce. Google's AI-based technologies have extended the reliability of currently-available global nowcasts from zero to five days on average and improved forecasts across regions in Africa and Asia to be on par with those in Europe. These advancements enable the Flood Hub to provide real-time river forecasts up to seven days in advance, covering river reaches across more than 80 countries.


The ML models powering the Flood Hub tool are the result of extensive research and collaboration. Google's journey began with a pilot early warning system in the Ganges-Brahmaputra river basin in India, followed by the development of ML-based hydrologic models in partnership with the JKU Institute for Machine Learning. These models outperformed traditional conceptual and physics-based hydrology models, leading to expanded forecasting coverage in India and Bangladesh.


Google's river forecast model uses two LSTMs (Long Short-Term Memory networks) applied sequentially, ingesting historical and forecasted weather data, along with static geographical and geophysical characteristics of watersheds. The model is trained on a diverse set of global streamflow gauge data to improve accuracy and is capable of making predictions for ungauged basins where no data is available.


Compared to the current state-of-the-art global flood forecasting system, GloFAS version 4, Google's model demonstrates significant improvements in accuracy and lead time. On average, their model achieves reliability scores at up to 4-day or 5-day lead times that are similar to or better than the reliability of GloFAS nowcasts (0-day lead time).


Looking ahead, Google remains committed to addressing climate change and helping global communities become more resilient through its Adaptation and Resilience efforts. Collaborations with international aid organizations and the World Meteorological Organization aim to provide actionable flood forecasts and understand how AI can help address real-world challenges faced by national flood forecasting agencies. Future work will focus on expanding flood forecasting coverage to more locations globally and other types of flood-related events and disasters.





REFERENCE

The g-f GK Article


Yossi Matias and Grey Nearing, Using AI to expand global access to reliable flood forecasts, Google, Google Research, March 20, 2024.



Yossi Matias and Grey Nearing


Yossi Matias is the Vice President, Engineering & Research at Google¹. He is a global leader of AI for Health, Climate and Education, and leads pioneering efforts in Generative AI¹. His team works on impact-driven breakthrough research, innovation, and moonshots, advancing society-centered AI to help transform products and address societal challenges¹. He is the global executive lead of Google’s Health AI, driving AI research to help transform healthcare¹. He also works on climate crisis mitigation and adaptation as the founding lead of Google’s Crisis Response initiative¹. Yossi is on the Computer Science faculty at Tel Aviv University, and previously a Research Scientist at Bell Labs and visiting professor at Stanford¹. He has published over 150 papers and is the inventor of over 70 patents¹.


Grey Nearing is a Research Scientist on the Google Flood Forecasting team, working on water-related topics⁵. His research areas include General Science, Climate and Sustainability, Machine Intelligence, and Machine Perception⁵. He has authored several publications on these topics⁵. His work focuses on leveraging machine learning for flood forecasting and other hydrological applications⁵⁶.


Source: Conversation with Bing, 3/21/2024

(1) Yossi Matias - Google Research. https://research.google/people/yossi-matias/.

(2) Grey Nearing - Google Research. http://research.google/people/grey-nearing/.

(3) ‪Grey Nearing‬ - ‪Google Scholar‬. https://scholar.google.com/citations?user=nWW7D-IAAAAJ.

(4) ‪Yossi Matias‬ - ‪Google Scholar‬. https://scholar.google.com/citations?user=IwSe1-MAAAAJ.

(5) Yossi Matias - Wikipedia. https://en.wikipedia.org/wiki/Yossi_Matias.

(6) Yossi Matias - Vice President, Engineering @ Google - Crunchbase. https://www.crunchbase.com/person/yossi-matias.



Complementary g-f GK


How AI is Improving Global Access to Reliable Flood ForecastsGoogle ResearchYouTube channel, March 20, 2024.





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