AI shows promise for flood forecasting and water security in data scarce regions

New research reveals that "foundation models" trained on vast, general time-series data may be able to forecast river flows accurately, even in regions with little or no local hydrological records. The approach could improve flood warnings, drought planning and water-resource management in parts of the world where monitoring data is limited. The study, published in Machine Learning: Earth, was conducted by researchers from The University of Texas at Austin and Hydrotify