idw – Informationsdienst Wissenschaft
Nachrichten, Termine, Experten
Nachrichten, Termine, Experten
idw - Informationsdienst
Wissenschaft
Research from Bonn combines modeling and Machine Learning – NeurIPS paper shows potential for climate adaptation and disaster control
Bonn, November 5, 2025. Extreme weather events such as heavy rain and flooding pose growing challenges for early warning systems worldwide. Researchers at the Rheinische Friedrich-Wilhelms-University Bonn, the Forschungszentrum Jülich (FZJ), and the Lamarr Institute for Machine Learning and Artificial Intelligence have developed RiverMamba, a new AI model that can predict river discharges and flood risks more accurately than previous methods. The research paper has been accepted for NeurIPS 2025 – a sign of scientific excellence in Bonn-based research. RiverMamba thus makes an important contribution to climate adaptation and risk prevention – topics that are receiving special attention worldwide, particularly around UN World Tsunami Awareness Day on November 5th.
AI learns from environmental and climate data
RiverMamba is based on the so-called Mamba architecture, a new generation of deep learning models that can handle temporal and spatial environmental and climate data particularly efficiently. The system continuously evaluates data on precipitation, temperature, soil moisture, and flow velocity and recognizes patterns that are decisive for the development of floods.
RiverMamba combines the strengths of classic, physics-based models such as the Global Flood Awareness System (GloFAS), which makes global predictions but does not fully model local characteristics and is very computationally intensive, with local, learning-based models such as Google's Flood Hub, which is very efficient but can only predict river flows at existing measuring stations. RiverMamba learns both from data from physics-based models and directly from extensive environmental and observational data. This enables it to make reliable predictions even when measurement series are incomplete or missing—for example, in smaller catchment areas or regions with limited data availability.
This ability to independently model complex interactions between weather, topography, and runoff behavior opens up new perspectives for more accurate flood forecasts worldwide.
Bonn AI research receives international acclaim
The development was led by Prof. Dr. Jürgen Gall, Principal Investigator at the Lamarr Institute, in close collaboration with the Transdisciplinary Research Area “Modeling” (TRA Modeling), the Integrated Research Training Group at the DFG Collaborative Research Centre “DETECT – Regional Climate Change: Disentangling the Role of Land Use and Water Management” ( (SFB 1502 DETECT) at the University of Bonn, and the project „Foundation Model for Weather Forecasting“ (RAINA), a joint project of the University of Bonn, the Deutscher Wetterdienst (DWD), and the Forschungszentrum Jülich (FZJ). The interdisciplinary project combines AI research with climate modeling, hydrology, and weather forecasting – and shows how excellent research from North Rhine-Westphalia contributes to overcoming global challenges. “With RiverMamba, we are showing how AI can be used in a targeted manner to model environmental processes more realistically and efficiently,” says Prof. Dr. Jürgen Gall. “Such data-based approaches can usefully complement existing early warning systems – an important step toward more reliable forecasts in the face of increasing extreme weather events.”
The research team will present its findings on December 4 at this year's NeurIPS conference in San Diego – one of the world's most prestigious conferences for machine learning and artificial intelligence, where only a fraction of the submissions are accepted each year. The acceptance of the paper underscores the international visibility and scientific excellence of Bonn-based research: cutting-edge research from North Rhine-Westphalia is making a significant contribution to the further development of data-based environmental and climate models.
Mohamad Hakam Shams Eddin, Yikui Zhang, Stefan Kollet, Juergen Gall: „RiverMamba: A State Space Model for Global River Discharge and Flood Forecasting“, Preprint via arxiv [https://arxiv.org/abs/2505.22535]
https://hakamshams.github.io/RiverMamba/ Project Website RiverMamba
https://neurips.cc/ Further Information on the NeurIPS 2025 conference
Illustration 1: Artificial intelligence in river modeling: The RiverMamba project uses deep learning ...
Copyright: © Lamarr Institute / University of Bonn (AI-generated)
Illustration 2: Flood forecast using the AI model “RiverMamba”: At the beginning of June 2024, south ...
Copyright: © Mohamad Hakam Shams Eddin, Yikui Zhang, Stefan Kollet, Jürgen Gall. © Sea texture NASA
RiverMamba: New AI architecture improves flood forecasting
Merkmale dieser Pressemitteilung:
Journalisten, Wissenschaftler, jedermann
Geowissenschaften, Informationstechnik, Meer / Klima, Umwelt / Ökologie
überregional
Forschungsergebnisse, Wissenschaftliche Publikationen
Englisch
Sie können Suchbegriffe mit und, oder und / oder nicht verknüpfen, z. B. Philo nicht logie.
Verknüpfungen können Sie mit Klammern voneinander trennen, z. B. (Philo nicht logie) oder (Psycho und logie).
Zusammenhängende Worte werden als Wortgruppe gesucht, wenn Sie sie in Anführungsstriche setzen, z. B. „Bundesrepublik Deutschland“.
Die Erweiterte Suche können Sie auch nutzen, ohne Suchbegriffe einzugeben. Sie orientiert sich dann an den Kriterien, die Sie ausgewählt haben (z. B. nach dem Land oder dem Sachgebiet).
Haben Sie in einer Kategorie kein Kriterium ausgewählt, wird die gesamte Kategorie durchsucht (z.B. alle Sachgebiete oder alle Länder).
