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

caRamel version 1.1 F. Zaoui and C. Monteil

caRamel: Automatic Calibration by Evolutionary Multi Objective Algorithm N. Le Moine, C. Monteil, and F. Zaoui

airGR: Suite of GR Hydrological Models for Precipitation-Runoff Modelling L. Coron, O. Delaigue, G. Thirel, C. Perrin, and C. Michel

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.