Calibration of the 1D shallow water equations: a comparison of Monte Carlo and gradient-based optimization methods
| Autor: | Asier Lacasta, Pilar García-Navarro, Mario Morales-Hernández, J. Burguete, P. Brufau |
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| Přispěvatelé: | Ministerio de Economía y Competitividad (España), Gobierno de Aragón |
| Rok vydání: | 2017 |
| Předmět: |
Atmospheric Science
Mathematical optimization Calibration (statistics) 0208 environmental biotechnology Monte Carlo method Context (language use) 02 engineering and technology 01 natural sciences 010305 fluids & plasmas 0103 physical sciences Applied mathematics Water Equations Boundary value problem Monte Carlo Shallow water equations Civil and Structural Engineering Water Science and Technology Mathematics Adjoint method Gradient Method Geotechnical Engineering and Engineering Geology Discharge coefficient 020801 environmental engineering Test case Shallow Calibration Gradient method |
| Zdroj: | Digital.CSIC. Repositorio Institucional del CSIC instname |
| ISSN: | 1465-1734 1464-7141 |
| DOI: | 10.2166/hydro.2017.021 |
| Popis: | 34 Pags.- 14 Figs.- 2 Algorithms. The definitive version is available at: http://jh.iwaponline.com/ The calibration of parameters in complex systems usually requires a large computational effort. Moreover, it becomes harder to perform the calibration when non-linear systems underlie the physical process, and the direction to follow in order to optimize an objective function changes depending on the situation. In the context of shallow water equations (SWE), the calibration of parameters, such as the roughness coefficient or the gauge curve for the outlet boundary condition, is often required. In this work, the SWE are used to simulate an open channel flow with lateral gates. Due to the uncertainty in the mathematical modeling that these lateral discharges may introduce into the simulation, the work is focused on the calibration of discharge coefficients. Thus, the calibration is performed by two different approaches. On the one hand, a classical Monte Carlo method is used. On the other hand, the development and application of an adjoint formulation to evaluate the gradient is presented. This is then used in a gradient-based optimizer and is compared with the stochastic approach. The advantages and disadvantages are illustrated and discussed through different test cases. This research has been partially funded by the Spanish MINECO/FEDER through the Research Project CGL2015-66114-R and by Diputación General de Aragón, DGA, through FEDER funds. The first author was also supported by the Spanish Ministry of Economy and Competitiveness fellowship BES-2012-053691. |
| Databáze: | OpenAIRE |
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