diff --git a/vignettes/V02.1_param_optim.Rmd b/vignettes/V02.1_param_optim.Rmd index ac2e701e23f71439ac3fea33b3b185260c7da23e..bcd3f3e8d21ef017391aaac5f94c8a61650678bc 100644 --- a/vignettes/V02.1_param_optim.Rmd +++ b/vignettes/V02.1_param_optim.Rmd @@ -205,6 +205,7 @@ resGLOB Multiobjective optimization is used to explore possible trade-offs between different performances criteria. Here we use the following R implementation of an efficient strategy: + * [caRamel: Automatic Calibration by Evolutionary Multi Objective Algorithm](https://cran.r-project.org/package=caRamel) Motivated by using the rainfall-runoff model for low flow simulation, we explore the trade-offs between the KGE values obtained without any data transformation and with the inverse transformation. diff --git a/vignettes/V03_param_sets_GR4J.Rmd b/vignettes/V03_param_sets_GR4J.Rmd index 36eb29c530948dd21a738fae09cb3df3f24d2d79..5409731a0187b2f8fcf33f8543af5fe1befa8f84 100644 --- a/vignettes/V03_param_sets_GR4J.Rmd +++ b/vignettes/V03_param_sets_GR4J.Rmd @@ -141,7 +141,7 @@ Now we can compute the Nash-Sutcliffe Efficiency criterion on the validation per -# Calibration of the GR4J model with the built-in `Calibration_Michel()` function +# Calibration of the GR4J model with the built-in Calibration_Michel function As seen above, the Michel's calibration algorithm is based on a local search procedure.