A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Automatic Model Structure Identification for Conceptual Hydrologic Models
Hydrological models play a crucial role in forecasting future water resource availability and water-related risks. It's essential that they realistically represent and simulate the processes of interest. However, which model structure is most suitable for a given task, catchment and data situation is often difficult to determine. There are only few tangible guidelines for model structure selection, and comparing multiple models simply to choose one to use in further work is a cumbersome process. It is therefore not surprising that the hydrological community has spent considerable effort on improving model parameter estimation, which can be treated as an automatized process, but the selection of a suitable model structure (i.e., the specific set of equations describing catchment function) has received comparatively little attention. To facilitate easier testing of different model structures, this thesis introduces an approach for Automatic Model Structure Identification (AMSI), which allows for the simultaneous calibration of model structural choices and model parameters. Model structural choices are treated as integer decision variables while model parameters are treated as continuous model variables in this approach. Through combining the modular modelling framework Raven with the mixed-integer optimization algorithm DDS, the testing of different structural hypotheses can thus be automated. AMSI then allows to effectively search a vast number of model structure and parameter choices to identify the most suitable model structures for a specific objective function. This thesis uses four experiments to test and benchmark AMSI's performance and capabilities. First, a synthetic experiment generates “observations” with known model structures and tests AMSI’s ability to re-identify these same structures. Second, AMSI is used in a real-world application on twelve diverse MOPEX catchments to test the feasibility of the approach. Third, a comprehensive benchmark study explores how reliably AMSI searches the available ...
Automatic Model Structure Identification for Conceptual Hydrologic Models
Hydrological models play a crucial role in forecasting future water resource availability and water-related risks. It's essential that they realistically represent and simulate the processes of interest. However, which model structure is most suitable for a given task, catchment and data situation is often difficult to determine. There are only few tangible guidelines for model structure selection, and comparing multiple models simply to choose one to use in further work is a cumbersome process. It is therefore not surprising that the hydrological community has spent considerable effort on improving model parameter estimation, which can be treated as an automatized process, but the selection of a suitable model structure (i.e., the specific set of equations describing catchment function) has received comparatively little attention. To facilitate easier testing of different model structures, this thesis introduces an approach for Automatic Model Structure Identification (AMSI), which allows for the simultaneous calibration of model structural choices and model parameters. Model structural choices are treated as integer decision variables while model parameters are treated as continuous model variables in this approach. Through combining the modular modelling framework Raven with the mixed-integer optimization algorithm DDS, the testing of different structural hypotheses can thus be automated. AMSI then allows to effectively search a vast number of model structure and parameter choices to identify the most suitable model structures for a specific objective function. This thesis uses four experiments to test and benchmark AMSI's performance and capabilities. First, a synthetic experiment generates “observations” with known model structures and tests AMSI’s ability to re-identify these same structures. Second, AMSI is used in a real-world application on twelve diverse MOPEX catchments to test the feasibility of the approach. Third, a comprehensive benchmark study explores how reliably AMSI searches the available ...
Automatic Model Structure Identification for Conceptual Hydrologic Models
Spieler, Diana (author) / Schütze, Niels / Merz, Ralf / Melsen, Lieke / Technische Universität Dresden
2024-03-06
Theses
Electronic Resource
English
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