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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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Discussion papers
https://doi.org/10.5194/hess-2019-259
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-2019-259
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: technical note 05 Jul 2019

Submitted as: technical note | 05 Jul 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Hydrology and Earth System Sciences (HESS).

Technical note: the caRamel R package for Automatic Calibration by Evolutionary Multi Objective Algorithm

Céline Monteil1, Fabrice Zaoui1, Nicolas Le Moine2, and Frédéric Hendrickx1 Céline Monteil et al.
  • 1EDF R&D LNHE -- Laboratoire National d’Hydraulique et Environnement, Chatou, 78400, France
  • 2UMR 7619 Metis (SU/CNRS/EPHE), Sorbonne Université, 4 Place Jussieu, Paris, 75005, France

Abstract. Environmental modelling is complex, and models often require the calibration of several parameters that are not directly evaluable from a physical quantity or a field measurement. The R package caRamel has been designed to easily implement a multi-objective optimizer in the R environment to calibrate these parameters. A multiobjective calibration allows to find a compromise between different goals by defining a set of optimal parameters. The algorithm is a hybrid of the Multiobjective Evolutionary Annealing Simplex method (MEAS) and the Nondominated Sorting Genetic Algorithm II (ε-NSGA-II algorithm). The optimizer was initially developed for the calibration of hydrological models but can be used for any environmental model. The main function of the package, caRamel(), requires to define a multi-objective calibration function as well as bounds on the variation of the underlying parameters to optimize.

CaRamel is well adapted to complex modelling. As an example, caRamel converges quickly and has a stable solution after 5,000 model evaluations with robust results for a real study case of a hydrological problem with 8 parameters and 3 objectives of calibration. The comparison with another well-known optimizer (i.e. MCO, for Multiple Criteria Optimization) confirms the quality of the algorithm.

Céline Monteil et al.
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Short summary
Environmental modelling is complex, and models often require the calibration of several parameters that are not directly evaluable from a physical quantity or a field measurement. Based on our experience in hydrological modelling, we propose to combine two algorithms to have a fast and accurate way of calibrating complex models (many parameters, many objectives). We built an R-package, caRamel, so that this multi-objective calibration algorithm can be easily implementable.
Environmental modelling is complex, and models often require the calibration of several...
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