Selection and Justification of Hydro-Morphodynamic Models using Information Theory: Active Learning on Surrogate Emulators

• Applicants: Professor Dr.-Ing. Wolfgang Nowak; Professor Dr.-Ing. Sergey Oladyshkin, Ph.D.; Professorin Dr.-Ing. Silke Wieprecht
• Subject Area: Hydrogeology, Hydrology, Limnology, Urban Water Management, Water Chemistry, Integrated Water Resources Management
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 513054523

Modelling hydro-morphodynamic processes in river ecosystems faces the challenges to reproduce complex, dynamic, and highly variable systems by making expert-driven simplification hypotheses. For this reason, a model for reproducing hydro-morphodynamics over long spatio-temporal scales, for instance, for climate change analysis, involves vast uncertainty. The input data required for modelling hydro-morphodynamics involve information on ecosystem characteristics, such as sediment grain size and surface elevation. Yet, every dataset has gaps in time or in space with often considerable uncertainty. Thus, the modelling procedure involves a chain of data acquisition and processing, and substantial simplifications of complex systems, which result in various types of uncertainty. These steps (and their weaknesses) in the modelling chain constitute substantial research challenges regarding uncertainty quantification for sophisticated hydro-morphodynamic models. Moreover, the selection of multi-dimensional hydro-morphodynamic modelling concepts is challenging since a multitude of different modelling approaches exist that need justified decisions. Therefore, hydro-morphodynamic modelling can benefit from rigorous and statistical methods for model selection, callibration and justification. To address these modelling challenges at feasible computational costs, our project proposes a machine learning approach based on Bayesian analysis, information theory, and active learning that will enable to emulate non-linear hydro-morphodynamic models. The proposed approach accounts for the sparse nature of measurement data and aims to significantly shorten computationally demanding simulations. The pathway to solving the modelling challenges implies the development of (1) a hybrid modelling chain for deterministic modelling; (2) a surrogate emulator based on stochastic approaches and information theory; (3) stochastic routines to leverage model selection, calibration and justification; and (4) a transfer concept to real-world systems for justifiability analysis. This project will boost hydro-morphodynamic modelling to evolve from a subjective deterministic workflow to a sophisticated, stochastically optimized, and objectively transparent sequence of algorithms.

• DFG Programme: Research Grants