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Google Develops AI Model Capable of Forecasting Floods Several Days in Advance

The team concluded that increasing open hydrological data remains key to enhancing global flood prediction using AI and traditional models.

Researchers at Google have developed an artificial intelligence system called the “AI streamflow forecasting model” that can predict extreme floods several days in advance – even in ungauged river basins with no stream data, a research paper by a team of Google researchers published in the journal Nature said.

The AI model in question used long short-term memory (LSTM) networks that simulated sequences of streamflow data (required for water resources planning and management) from sequences of publicly available meteorological input data.

Google claimed in the research paper that the AI system matched or outperformed current state-of-the-art global forecasting methods. Their AI system was able to “improve the expected precision, recall and lead time of short-term (0–7 days) forecasts of extreme riverine events” which basically means that the AI model learned to predict floods up to 7 days out using publicly available meteorological and geophysical data. These forecasts are available at https://g.co/floodhub and cover 80 countries across the world.

Why was the research conducted in the first place?

The paper aimed to “evaluate the extent to which artificial intelligence (AI) trained on open, public datasets can be used to improve global access to forecasts of extreme events in global rivers.”

The team concluded that increasing open hydrological data remains key to enhancing global flood prediction using AI and traditional models. By leveraging open data, prediction systems, such as their AI model, can overcome the lack of on-the-ground measurements that hampered previous modeling approaches in data-sparse regions.

What is open data?

Freely available data that can be used, re-used, and redistributed with appropriate attribution is referred to as open data. Open data has been hailed as an important asset in the kind of data-driven times we are living in (you must have heard of the whole “data is the new oil” mantra) for increasing interoperability across different systems and organizations allowing them to work together.

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