{"id":298,"date":"2026-03-12T11:36:43","date_gmt":"2026-03-12T11:36:43","guid":{"rendered":"https:\/\/gajed.com\/blogs\/google-is-using-old-news-reports-and-ai-to-predict-flash-floods\/"},"modified":"2026-03-12T11:38:28","modified_gmt":"2026-03-12T11:38:28","slug":"google-is-using-old-news-reports-and-ai-to-predict-flash-floods","status":"publish","type":"post","link":"https:\/\/gajed.com\/blogs\/google-is-using-old-news-reports-and-ai-to-predict-flash-floods\/","title":{"rendered":"Google\u2019s Groundsource: Using Historical News and AI to Predict Flash Floods"},"content":{"rendered":"<p><strong>Flash floods<\/strong> are among the deadliest weather events worldwide, killing more than 5,000 people each year. They\u2019re also notoriously hard to predict because they happen fast, are highly localized, and often occur where continuous monitoring infrastructure is limited. Google believes it has found a creative workaround: using old news reports and AI to build a new kind of forecasting data set.<\/p>\n<h2>Why flash floods are so difficult to forecast<\/h2>\n<p>Traditional meteorological forecasting depends on dense, continuous observations like river gauges, radars, and long-term records. Flash floods, however, are short-lived and localized \u2014 so many events never get captured by official sensors. That data gap makes it difficult to train machine-learning models that require reliable, ground-truth records.<\/p>\n<h2>How Google built Groundsource from the news<\/h2>\n<p>Google researchers turned to a nontraditional data source: the news. Using Gemini, Google\u2019s large language model, they processed roughly <strong>5 million news articles<\/strong> and extracted details about about <strong>2.6 million flood events<\/strong>. The result is a geo-tagged time series called <strong>Groundsource<\/strong>, a structured data set that turns written reports into usable, quantifiable records of past flooding.<\/p>\n<p>According to Google Research product manager Gila Loike, this is the first time Google has used language models in this specific way to generate a large-scale geophysical data set.<\/p>\n<h2>Training a forecasting model<\/h2>\n<p>With Groundsource as a real-world baseline, the team trained a Long Short-Term Memory (LSTM) neural network to combine global weather forecasts and the historical reports. The model outputs the probability of flash-flood risk in a given area, turning qualitative news reports into a quantitative tool for prediction.<\/p>\n<h2>Deployment and real-world use<\/h2>\n<p>Google has integrated the forecasts into its <strong>Flood Hub<\/strong> platform, highlighting urban risk areas across <strong>150 countries<\/strong> and sharing data with emergency response agencies. Emergency officials who trialed the system say it helped speed response efforts \u2014 for example, Ant\u00f3nio Jos\u00e9 Beleza from the Southern African Development Community reported that the forecasts improved his organization\u2019s ability to react to flooding.<\/p>\n<h2>Strengths and limitations<\/h2>\n<ul>\n<li><strong>Strengths<\/strong> \u2705\n<ul>\n<li>Creative use of public, historical text to fill observational gaps.<\/li>\n<li>Globally scalable approach suited to regions without extensive weather infrastructure.<\/li>\n<li>Groundsource is publicly shared, enabling broader research and collaboration.<\/li>\n<\/ul>\n<\/li>\n<li><strong>Limitations<\/strong> \u26a0\ufe0f\n<ul>\n<li>Relatively low spatial resolution \u2014 risk is identified across ~20 km\u00b2 grid cells, so it\u2019s not pinpoint-precise.<\/li>\n<li>Less precise than systems that incorporate local radar and real-time sensors (for example, US National Weather Service alerts).<\/li>\n<li>Dependent on the quantity and quality of historical reporting in different regions.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<h2>What experts are saying<\/h2>\n<p>Juliet Rothenberg, a program manager on Google\u2019s Resilience team, explained the broader aim: \u201cBecause we\u2019re aggregating millions of reports, the Groundsource data set actually helps rebalance the map. It enables us to extrapolate to other regions where there isn\u2019t as much information.\u201d<\/p>\n<p>Marshall Moutenot, CEO of Upstream Tech and co-founder of dynamical.org, praised the approach as a creative fix to a common problem: \u201cData scarcity is one of the most difficult challenges in geophysics. Simultaneously, there\u2019s too much Earth data, and then when you want to evaluate against truth, there\u2019s not enough. This was a really creative approach to get that data.\u201d<\/p>\n<h2>Why this matters<\/h2>\n<p>Groundsource demonstrates how language models can transform qualitative historical records into quantitative data that improves forecasting where traditional sensor networks are sparse. The technique could be extended to other ephemeral hazards \u2014 such as heat waves, landslides, or mudslides \u2014 to create new data products that support early warning systems and disaster response worldwide.<\/p>\n<h2>Bottom line<\/h2>\n<p>By reading the archives of global news with AI, Google has converted human reports into a large-scale, geotagged flood data set and used it to power predictive models. The result isn\u2019t a replacement for high-resolution, radar-based alerts, but it is a practical, scalable tool to reduce blind spots in global flood forecasting \u2014 especially in regions that need it most. \ud83c\udf0d\ud83c\udf27\ufe0f<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Google analyzed 5 million news articles with Gemini to build Groundsource \u2014 a geotagged flood dataset that powers flash-flood forecasts on Flood Hub, helping emergency responders in 150 countries. \ud83c\udf27\ufe0f\ud83e\udd16\ud83d\udce1<\/p>\n","protected":false},"author":1,"featured_media":301,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"tdm_status":"","tdm_grid_status":"","footnotes":""},"categories":[1],"tags":[],"class_list":{"0":"post-298","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-tech-news"},"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin 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