Operations Research in Hydrology ()
| Vortragende/r (Mitwirkende/r) | |
|---|---|
| Nummer | 0000005419 |
| Art | Vorlesung mit integrierten Übungen |
| Umfang | 2 SWS |
| Semester | Wintersemester 2025/26 |
| Unterrichtssprache | English |
| Stellung in Studienplänen | Siehe TUMonline |
| Termine | Siehe TUMonline |
Teilnahmekriterien
Beschreibung
1. Introduction to Operations Research in Hydrology
2. Various terms used, and their definitions
3. Brief introduction to conceptual rainfall-runoff modeling and a few models
4. Problems that need specialized algorithms to find optimal solutions
5. Global optimization
6. Optimization with constraints
7. The Differential Evolution optimization algorithm
8. The simulated Annealing optimization algorithm
9. Tricks to achieve results faster
10. Model parameter over-fitting
11. Determining parameter bounds for a global search
12. Diagnostics to verify the progress and validity of an optimization run
13. Scenario simulations using various model parameter vectors
2. Various terms used, and their definitions
3. Brief introduction to conceptual rainfall-runoff modeling and a few models
4. Problems that need specialized algorithms to find optimal solutions
5. Global optimization
6. Optimization with constraints
7. The Differential Evolution optimization algorithm
8. The simulated Annealing optimization algorithm
9. Tricks to achieve results faster
10. Model parameter over-fitting
11. Determining parameter bounds for a global search
12. Diagnostics to verify the progress and validity of an optimization run
13. Scenario simulations using various model parameter vectors
Inhaltliche Voraussetzungen
• ED130088 Computer Programming in Hydrology
Lehr- und Lernmethoden
The module consists of lectures with integrated exercises. The students are required to bring their own laptops. Portable open-source software will be made available to all. The students should be able to code in the Python programming language. After the introduction and definition of essential terms, the problem of optimization is broken in to small chunks that the students are supposed to complete in each exercise. The aim is that the students are able to find a good set of model parameters that fulfill a given criteria using predefined models, and input data using the learned optimization schemes. Finally, the students write a report (as a group) where they demonstrate their ability to understand a given situation and provide the solution in the form of a running computer code that takes input data, fits a model to it by using the either the Differential Evolution and/or the Simulated Annealing algorithm, analyzes the resulting model parameters and outputs, and summarize them in the form of statistics and graphs.