Abstract
Local search methods have been applied successfully in calibration of simple groundwater models, but might fail in locating the optimum for models of increased complexity, due to the more complex shape of the response surface. Global search algorithms have been demonstrated to perform well for these types of models, although at a more expensive computational cost. The main purpose of this study is to investigate the performance of a global and a local parameter optimization algorithm, respectively, the Shuffled Complex Evolution (SCE) algorithm and the gradient-based Gauss-Marquardt-Levenberg algorithm (implemented in the PEST software), when applied to a steady-state and a transient groundwater model. The results show that PEST can have severe problems in locating the global optimum and in being trapped in local regions of attractions. The global SCE procedure is, in general, more effective and provides a better coverage of the Pareto optimal solutions at a lower computational cost.
Original language | English |
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Title of host publication | Calibration and reliability in groundwater modelling: from uncertainty to decision making |
Editors | M.F.P. Bierkens, H.C. Gehrels, K. Kovar |
Volume | 304 |
Place of Publication | Oxfordshire, UK |
Publisher | IAHS Press |
Publication date | 2006 |
Pages | 67-72 |
ISBN (Print) | 978-1-901502-58-9 |
Publication status | Published - 2006 |
Event | 5th international conference on Calibration and Reliability in Groundwater Modelling - Scheveningen, Netherlands Duration: 6 Jun 2005 → 9 Jun 2005 https://geo.ua.edu/ICGW/Newsletter/Report%20on%20Meeting%20-%20June%202005.pdf (model care 2005 look-back document) |
Conference
Conference | 5th international conference on Calibration and Reliability in Groundwater Modelling |
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Country/Territory | Netherlands |
City | Scheveningen |
Period | 06/06/2005 → 09/06/2005 |
Internet address |
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Series | I A H S Proceedings and Reports |
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Volume | 304 |
ISSN | 0144-7815 |
Keywords
- automatic calibration
- hydrological modelling
- MIKE SHE model
- optimization algorithms
- parameter estimation