Commit 5f79785c authored by Delaigue Olivier's avatar Delaigue Olivier
Browse files

v1.1.2.17 CLEAN: syntaxe revision of vignette param_optim

parent c16b6196
Package: airGR
Type: Package
Title: Suite of GR Hydrological Models for Precipitation-Runoff Modelling
Version: 1.1.2.16
Version: 1.1.2.17
Date: 2018-10-23
Authors@R: c(
person("Laurent", "Coron", role = c("aut", "trl"), comment = c(ORCID = "0000-0002-1503-6204")),
......
......@@ -13,7 +13,7 @@ output:
### 1.1.2.16 Release Notes (2018-10-23)
### 1.1.2.17 Release Notes (2018-10-23)
......
......@@ -17,7 +17,7 @@ library(hydroPSO)
library(Rmalschains)
# source("airGR.R")
set.seed(321)
load(system.file("vignettes_data/Vignette_Param.rda", package = "airGR"))
load(system.file("vignettesData/vignetteParamOptim.rda", package = "airGR"))
```
......@@ -33,9 +33,10 @@ In this vignette, we use the **GR4J** model to illustrate the different optimiza
In particular, we assume that the R global environment contains input climate data, observed discharge and functions from the [Get Started](V01_get_started.html) vignette, as shown below.
Please note that the calibration period is defined in the `CreateRunOptions()` function .
```{r, warning=FALSE, fig.keep='none', results='hide', fig.height=10, fig.width=10, eval=TRUE, echo=FALSE, message=FALSE}
example("Calibration_Michel", echo = FALSE, ask = FALSE)
```
<!-- ```{r, warning=FALSE, fig.keep='none', results='hide', fig.height=10, fig.width=10, eval=TRUE, echo=FALSE, message=FALSE} -->
<!-- example("Calibration_Michel", echo = FALSE, ask = FALSE) -->
<!-- ``` -->
```{r, echo=TRUE, eval=FALSE}
example("Calibration_Michel")
```
......@@ -54,15 +55,15 @@ Here we choose to minimize the root mean square error.
The change of the repository from the "real" parameter space to a "transformed" space ensures homogeneity of displacement in the different dimensions of the parameter space during the step-by-step procedure of the calibration algorithm of the model.
```{r, warning=FALSE, results='hide'}
OptimGR4J <- function(Param_Optim) {
```{r, warning=FALSE, results='hide', eval=FALSE}
OptimGR4J <- function(ParamOptim) {
## Transformation of the parameter set to real space
Param_Optim_Vre <- airGR::TransfoParam_GR4J(ParamIn = Param_Optim,
Direction = "TR")
RawParamOptim <- airGR::TransfoParam_GR4J(ParamIn = ParamOptim,
Direction = "TR")
## Simulation given a parameter set
OutputsModel <- airGR::RunModel_GR4J(InputsModel = InputsModel,
RunOptions = RunOptions,
Param = Param_Optim_Vre)
Param = RawParamOptim)
## Computation of the value of the performance criteria
OutputsCrit <- airGR::ErrorCrit_RMSE(InputsCrit = InputsCrit,
OutputsModel = OutputsModel,
......@@ -73,7 +74,7 @@ OptimGR4J <- function(Param_Optim) {
In addition, we need to define the lower and upper bounds of the four **GR4J** parameters in the transformed parameter space:
```{r, warning=FALSE, results='hide'}
```{r, warning=FALSE, results='hide', eval=FALSE}
lowerGR4J <- rep(-9.99, times = 4)
upperGR4J <- rep(+9.99, times = 4)
```
......@@ -100,19 +101,19 @@ optPORT_ <- function(x) {
lower = lowerGR4J, upper = upperGR4J,
control = list(trace = 1))
}
list_opt <- apply(startGR4J, 1, optPORT_)
listOptPORT <- apply(startGR4J, MARGIN = 1, FUN = optPORT_)
```
We can then extract the best parameter sets and the value of the performance criteria:
```{r, warning=FALSE, results='hide'}
list_par <- t(sapply(list_opt, function(x) x$par))
list_obj <- sapply(list_opt, function(x) x$objective)
df_port <- data.frame(list_par, RMSE = list_obj)
```{r, warning=FALSE, results='hide', eval=FALSE}
parPORT <- t(sapply(listOptPORT, function(x) x$par))
objPORT <- sapply(listOptPORT, function(x) x$objective)
parPORT <- data.frame(parPORT, RMSE = objPORT)
```
As can be seen below, the optimum performance criterion values (column *objective*) can differ from the global optimum value in many cases, resulting in various parameter sets.
```{r, warning=FALSE}
summary(df_port)
summary(parPORT)
```
The existence of several local minima illustrates the importance of defining an appropriate starting point or of using a multi-start strategy or a global optimization strategy.
......@@ -153,16 +154,18 @@ optMALS <- Rmalschains::malschains(fn = OptimGR4J,
As it can be seen in the table below, the four additional optimization strategies tested lead to very close optima.
```{r, warning=FALSE, echo=FALSE, eval=FALSE}
parGLOB <- data.frame(Algo = c("airGR", "PORT", "DE", "PSO", "MA-LS"),
round(rbind(
OutputsCalib$ParamFinalR ,
airGR::TransfoParam_GR4J(ParamIn = optPORT$par , Direction = "TR"),
airGR::TransfoParam_GR4J(ParamIn = as.numeric(optDE$optim$bestmem), Direction = "TR"),
airGR::TransfoParam_GR4J(ParamIn = as.numeric(optPSO$par) , Direction = "TR"),
airGR::TransfoParam_GR4J(ParamIn = optMALS$sol , Direction = "TR")),
digits = 3))
```
```{r, warning=FALSE, echo=FALSE}
data.frame(Algo = c("airGR", "PORT", "DE", "PSO", "MA-LS"),
round(rbind(
OutputsCalib$ParamFinalR ,
airGR::TransfoParam_GR4J(ParamIn = optPORT$par , Direction = "TR"),
airGR::TransfoParam_GR4J(ParamIn = as.numeric(optDE$optim$bestmem), Direction = "TR"),
airGR::TransfoParam_GR4J(ParamIn = as.numeric(optPSO$par) , Direction = "TR"),
airGR::TransfoParam_GR4J(ParamIn = optMALS$sol , Direction = "TR")),
digits = 3))
parGLOB
```
<!-- This is an expected result because the response surface for quadratic performance criteria of the **GR4J** model is generally sufficiently well defined in the transformed parameter space to allow using a local optimization strategy instead of a more time consuming global optimization strategy. -->
......
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment