Articles | Volume 24, issue 6
https://doi.org/10.5194/hess-24-3189-2020
https://doi.org/10.5194/hess-24-3189-2020
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

Viewed

Total article views: 4,533 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,046 1,405 82 4,533 65 77
  • HTML: 3,046
  • PDF: 1,405
  • XML: 82
  • Total: 4,533
  • BibTeX: 65
  • EndNote: 77
Views and downloads (calculated since 05 Jul 2019)
Cumulative views and downloads (calculated since 05 Jul 2019)

Viewed (geographical distribution)

Total article views: 4,533 (including HTML, PDF, and XML) Thereof 3,754 with geography defined and 779 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 14 Dec 2024
Download
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.