AI Improves the Accuracy, Reliability, and Economic Value of Continental‐Scale Flood Predictions

Accurate flood early warnings are critical to minimize damage and loss of life. Current large‐scale operational forecasting systems, however, have limited accuracy, description of uncertainty, and computational efficiency. While Artificial intelligence (AI) can address these limitations in principle, the accuracy and reliability of AI forecasts have thus far proven insufficient. Here we present a novel hybrid framework that integrates AI‐based machinery termed Errorcastnet (ECN) with the National Water Model (NWM) to showcase the potential of ensemble AI flood forecasts over the contiguous U.S. ECN boosts prediction accuracy four‐ to six‐fold across lead times of 1–10 days, while providing uncertainty quantification. It also outperforms Google's state‐of‐the‐art global AI model. ECN‐based forecasts offer superior economic value (up to four‐fold) for decision‐making as compared to those from NWM alone. ECN performs well in varied ecoregions, physiography, and land management conditions. The framework is computationally efficient, enabling national‐scale ensemble forecasts in minutes.. Authors: Tran, Vinh Ngoc [Univ. of Michigan, Ann Arbor, MI (United States)] (ORCID:0000000182724843); Kim, Taeho [Univ. of Michigan, Ann Arbor, MI (United States)]; Xu, Donghui [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)] (ORCID:0000000228592664); Tran, Hoang [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)] (ORCID:0000000277680863); Le, Manh‐Hung [NASA Goddard Space Flight Center (GSFC), Greenbelt, MD (United States); Science Applications International Corporation, Greenbelt, MD (United States)] (ORCID:0000000288208492). DOE Contract: AC05-76RL01830. Subjects: artificial intelligence