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”, 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 RhineWestphalia is making a significant contribution to the further development of databased environmental and climate models.
