Multi-objective calibration by combination of stochastic and gradient-like parameter generation rules – the caRamel algorithm

Monteil, Céline; Zaoui, Fabrice; Le Moine, Nicolas; Hendrickx, Frédéric

doi:https://doi.org/10.5194/hess-24-3189-2020

Articles | Volume 24, issue 6

https://doi.org/10.5194/hess-24-3189-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/hess-24-3189-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 24, issue 6

Research article

|

19 Jun 2020

Research article |

| 19 Jun 2020

Multi-objective calibration by combination of stochastic and gradient-like parameter generation rules – the caRamel algorithm

Céline Monteil, Fabrice Zaoui, Nicolas Le Moine, and Frédéric Hendrickx

Model code and software

R: A language and environment for statistical computing R Core Team https://www.R-project.org/

caRamel version 1.1 F. Zaoui and C. Monteil https://doi.org/10.5281/zenodo.3895601

caRamel: Automatic Calibration by Evolutionary Multi Objective Algorithm N. Le Moine, C. Monteil, and F. Zaoui https://CRAN.R-project.org/package=caRamel

airGR: Suite of GR Hydrological Models for Precipitation-Runoff Modelling L. Coron, O. Delaigue, G. Thirel, C. Perrin, and C. Michel https://cran.r-project.org/package=airGR

Short summary

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