Speaker
Description
The increasing frequency and severity of extreme precipitation events necessitate innovative approaches to flood forecasting and risk management. Traditional hydrodynamic modeling, while physically robust, faces significant computational constraints that limit its applicability for real-time flood prediction and ensemble-based probabilistic forecasting. This paper presents DeepWaive, a novel hybrid AI foundation model that fundamentally transforms the landscape of flood forecasting.
DeepWaive overcomes the traditional limitations of both hydrodynamic and machine learning approaches through a unique AI foundation architecture that combines the physical understanding of flood processes with the computational efficiency of deep learning. Our model achieves unprecedented processing speeds, enabling the real-time computation of two-dimensional flood inundation mapping with a computational acceleration factor of 10^6 compared to conventional hydrodynamic models. This breakthrough allows, for the first time, the parallel processing of ensemble precipitation forecasts from the German Weather Service's SINFONY products, enabling probabilistic flood forecasting at previously unattainable spatial and temporal resolutions.
A key innovation of DeepWaive lies in its generalization capabilities. Unlike conventional deep learning models that require retraining for each geographical domain, our approach demonstrates remarkable transferability across diverse topographical and hydrological conditions. This is achieved through extensive training on a comprehensive dataset of hydrodynamic simulations encompassing various geographical and meteorological scenarios. Validation results show that DeepWaive maintains high accuracy levels (R² > 0.9) across different regions while providing real-time inundation predictions.
The model's capabilities extend beyond pure forecasting to include real-time evaluation of flood protection measures, enabling dynamic assessment of intervention strategies during extreme events. This feature represents a significant advancement in operational flood management, offering decision-makers the ability to rapidly evaluate and optimize response strategies.
These developments mark a significant step forward in flood forecasting and risk management, offering new possibilities for early warning systems and risk mitigation planning. The implementation of DeepWaive demonstrates the potential of AI-driven approaches to address critical challenges in hydrological-hydraulic engineering while maintaining the physical basis necessary for reliable predictions.
| Presenting Author | Julian Hofmann |
|---|---|
| Email Address of Presenting Author | hofmann@floodwaive.de |
| Affiliation of Presenting Author | CEO | FloodWaive Predictive Intelligence GmbH |
| Address of Presenting Author | Jülicher Straße 209d, 52070 Aachen |
| Session | Precipitation and Hydrological Models: Evaluation, verification and interfaces |
| Preferred Contribution Type | Oral Presentation |
