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The technology’s ability to forecast floods is limited by the poor quality of data and the irregular behavior of weather events such as a DANA

Artificial intelligence (AI) is already capable of identifying tumors from thousands of images, performing precise surgical operations, discovering new antibiotics and proteins, analyzing market trends, and optimizing industrial processes, among numerous other applications. Yet, when it comes to adverse phenomena like floods — the most destructive natural disasters after earthquakes and tsunamis — its technological prowess falls short.

Over the past two decades, floods have impacted 2.5 billion people, claimed the lives of a quarter of a million individuals, and caused an estimated $936 billion in damages, according to the International Disaster Database (EM-DAT) from the Catholic University of Louvain in Brussels. Despite the focus of technological giants and global institutions, developments to predict and mitigate floods remain ineffective. Why is that the case?

“Existing climate models aren’t very good for certain extreme weather events, Heatwaves in Europe are, for example, increasing much faster in the real world compared to what the models tell us should happen,” Dim Coumou, a climate expert at the University of Amsterdam, told Horizon magazine. “It’s important to forecast extremes so we can have early warnings.”