Multi-objective calibration of Tank model using multiple genetic algorithms and stopping criteria
ABSTRACT Calibration of hydrologic models estimates parameter values that cannot be measured and enable the rainfall-runoff processes simulation. Multi-objective evolutionary algorithms can make the calibration faster and more efficient through an iterative process. However, the standard stopping cr...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Associação Brasileira de Recursos Hídricos
2022-11-01
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| Series: | Revista Brasileira de Recursos Hídricos |
| Subjects: | |
| Online Access: | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312022000100230&lng=en&tlng=en |
| _version_ | 1811318985777479680 |
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| author | Juan Carlos Ticona Gutierrez Cassia Brocca Caballero Sofia Melo Vasconcellos Franciele Maria Vanelli Juan Martín Bravo |
| author_facet | Juan Carlos Ticona Gutierrez Cassia Brocca Caballero Sofia Melo Vasconcellos Franciele Maria Vanelli Juan Martín Bravo |
| author_sort | Juan Carlos Ticona Gutierrez |
| collection | DOAJ |
| description | ABSTRACT Calibration of hydrologic models estimates parameter values that cannot be measured and enable the rainfall-runoff processes simulation. Multi-objective evolutionary algorithms can make the calibration faster and more efficient through an iterative process. However, the standard stopping criterion used to stop the iterative process is to reach a pre-defined number of iterations defined by the modeller. Alternatively, the Ticona stopping criterion is based on the minimum number of iterations required to achieve a determined number of non-dominated solutions in the Pareto front, resulting in a reduction of the computational time without losing performance during the calibration processes. We evaluated the Ticona stopping criterion in the Tank Model calibration. The calibration processes were performed using data from two river basins, with three genetic algorithms and two objective functions. The Ticona stopping criterion required a computational time 27.4% to 44.1% lower than using the standard stopping criterion and were obtaining similar results in simulated streamflow time series and similar values of the best set of parameters. |
| first_indexed | 2024-04-13T12:34:33Z |
| format | Article |
| id | doaj.art-5fc8ce4b8c1248c6b8795c937e3b2926 |
| institution | Directory Open Access Journal |
| issn | 2318-0331 |
| language | English |
| last_indexed | 2024-04-13T12:34:33Z |
| publishDate | 2022-11-01 |
| publisher | Associação Brasileira de Recursos Hídricos |
| record_format | Article |
| series | Revista Brasileira de Recursos Hídricos |
| spelling | doaj.art-5fc8ce4b8c1248c6b8795c937e3b29262022-12-22T02:46:42ZengAssociação Brasileira de Recursos HídricosRevista Brasileira de Recursos Hídricos2318-03312022-11-012710.1590/2318-0331.272220220046Multi-objective calibration of Tank model using multiple genetic algorithms and stopping criteriaJuan Carlos Ticona Gutierrezhttps://orcid.org/0000-0001-5412-6993Cassia Brocca Caballerohttps://orcid.org/0000-0001-7634-2902Sofia Melo Vasconcelloshttps://orcid.org/0000-0003-2677-1172Franciele Maria Vanellihttps://orcid.org/0000-0001-8763-5786Juan Martín Bravohttps://orcid.org/0000-0001-5585-1257ABSTRACT Calibration of hydrologic models estimates parameter values that cannot be measured and enable the rainfall-runoff processes simulation. Multi-objective evolutionary algorithms can make the calibration faster and more efficient through an iterative process. However, the standard stopping criterion used to stop the iterative process is to reach a pre-defined number of iterations defined by the modeller. Alternatively, the Ticona stopping criterion is based on the minimum number of iterations required to achieve a determined number of non-dominated solutions in the Pareto front, resulting in a reduction of the computational time without losing performance during the calibration processes. We evaluated the Ticona stopping criterion in the Tank Model calibration. The calibration processes were performed using data from two river basins, with three genetic algorithms and two objective functions. The Ticona stopping criterion required a computational time 27.4% to 44.1% lower than using the standard stopping criterion and were obtaining similar results in simulated streamflow time series and similar values of the best set of parameters.http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312022000100230&lng=en&tlng=enMulti-objective evolutionary algorithmTank modelStopping criterionNSGA-IINSGA-IIISPEA-II |
| spellingShingle | Juan Carlos Ticona Gutierrez Cassia Brocca Caballero Sofia Melo Vasconcellos Franciele Maria Vanelli Juan Martín Bravo Multi-objective calibration of Tank model using multiple genetic algorithms and stopping criteria Revista Brasileira de Recursos Hídricos Multi-objective evolutionary algorithm Tank model Stopping criterion NSGA-II NSGA-III SPEA-II |
| title | Multi-objective calibration of Tank model using multiple genetic algorithms and stopping criteria |
| title_full | Multi-objective calibration of Tank model using multiple genetic algorithms and stopping criteria |
| title_fullStr | Multi-objective calibration of Tank model using multiple genetic algorithms and stopping criteria |
| title_full_unstemmed | Multi-objective calibration of Tank model using multiple genetic algorithms and stopping criteria |
| title_short | Multi-objective calibration of Tank model using multiple genetic algorithms and stopping criteria |
| title_sort | multi objective calibration of tank model using multiple genetic algorithms and stopping criteria |
| topic | Multi-objective evolutionary algorithm Tank model Stopping criterion NSGA-II NSGA-III SPEA-II |
| url | http://www.scielo.br/scielo.php?script=sci_arttext&pid=S2318-03312022000100230&lng=en&tlng=en |
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