Articles | Volume 29, issue 7
https://doi.org/10.5194/hess-29-1939-2025
https://doi.org/10.5194/hess-29-1939-2025
Research article
 | 
17 Apr 2025
Research article |  | 17 Apr 2025

Long short-term memory networks for enhancing real-time flood forecasts: a case study for an underperforming hydrologic model

Sebastian Gegenleithner, Manuel Pirker, Clemens Dorfmann, Roman Kern, and Josef Schneider

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Latest update: 19 Apr 2025
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Short summary
Accurate early-warning systems are crucial for reducing the damage caused by flooding events. In this study, we explored the potential of long short-term memory networks for enhancing the forecast accuracy of hydrologic models employed in operational flood forecasting. The presented approach elevated the investigated hydrologic model’s forecast accuracy for further ahead predictions and at flood event runoff.
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