TechCrunch: “Flash floods are among the deadliest weather events in the world, killing more than 5,000 people each year. They’re also among the most difficult to predict. But Google thinks it has cracked that problem in an unlikely way — by reading the news. While humans have assembled a lot of weather data, flash floods are too short-lived and localized to be measured comprehensively, the way the temperature or even river flows are monitored over time. That data gap means that deep learning models, which are increasingly capable of forecasting the weather, aren’t able to predict flash floods. To solve that problem, Google researchers used Gemini — Google’s large language model — to sort through 5 million news articles from around the world, isolating reports of 2.6 million different floods, and turning those reports into  With Groundsource as a real-world baseline, the researchers trained a model built on a Long Short-Term Memory (LSTM) neural network to ingest global weather forecasts and generate the probability of flash floods in a given area.

• Google’s flash flood forecasting model is now highlighting risks for urban areas in 150 countries on the company’s Flood Hub platform, and sharing its data with emergency response agencies around the world. António José Beleza, an emergency response official at the Southern African Development Community who trialed the forecasting model with Google, said it helped his organization respond to floods more quickly…”