coastal_flooding: Coastal flooding as function of offshore forcing conditions.

In profExtrema: Compute and Visualize Profile Extrema Functions

Description

A dataset containing the results of a numerical simulation conducted with the MARS model (Lazure and Dumas, 2008) for coastal flooding. The numerical model was adapted to the Boucholeurs area (French Atlantic coast), close to La Rochelle, and validated with data from the 2010 Xynthia storm event. See Azzimonti et al. (2017+) and Rohmer et al. (2018) for more details.

Usage

coastal_flooding

Format

A data frame with 200 rows and 6 variables:

Tide

High tide level in meters;

Surge

Surge peak amplitude in meters;

phi

Phase difference between high tide and surge peak;

t-

Duration of the increasing part of the surge temporal signal (assumed to be triangular);

t+

Duration of the decreasing part of the surge temporal signal (assumed to be triangular);

Area

Flooded area in m^2.

Details

The data frame contains 5 input variables: Tide, Surge, phi, t-, t+ detailing the offshore forcing conditions for the model. All input variables are normalized in [0,1]. The response is Area, the area flooded in m^2.

References

Azzimonti, D., Ginsbourger, D., Rohmer, J. and Idier, D. (2017+). Profile extrema for visualizing and quantifying uncertainties on excursion regions. Application to coastal flooding. arXiv:1710.00688.

Rohmer, J., Idier, D., Paris, F., Pedreros, R., and Louisor, J. (2018). Casting light on forcing and breaching scenarios that lead to marine inundation: Combining numerical simulations with a random-forest classification approach. Environmental Modelling & Software, 104:64-80.

Lazure, P. and Dumas, F. (2008). An external-internal mode coupling for a 3D hydrodynamical model for applications at regional scale (MARS). Advances in Water Resources, 31:233-250.

Examples

# Define inputs
inputs<-data.frame(coastal_flooding[,-6])
colnames(inputs)<-colnames(coastal_flooding[,-6])
colnames(inputs)[4:5]<-c("tPlus","tMinus")

# put response in areaFlooded variable
areaFlooded<-data.frame(coastal_flooding[,6])
colnames(areaFlooded)<-colnames(coastal_flooding)[6]
response = sqrt(areaFlooded)


model <- km(formula=~Tide+Surge+I(phi^2)+tMinus+tPlus,
            design = inputs,response = response,covtype="matern3_2")
# Fix threshold
threshold<-sqrt(c(1.2e6,1.9e6,3.1e6,6.5e6))

# use the coordinateProfile function
## set up plot options
options_plots <- list(save=FALSE, folderPlots = "./" ,
                      titleProf = "Coordinate profiles",
                      title2d = "Posterior mean",qq_fill=TRUE)
# set up full profiles options
options_full<-list(multistart=15,heavyReturn=TRUE)
# set up approximation options
d <- model@d
init_des<-lhs::maximinLHS(5*d , d )
options_approx<- list(multistart=2,heavyReturn=TRUE, initDesign=init_des,
                      fullDesignSize=100, smoother="quantSpline")

# run the coordinate profile extrema on the mean
CF_CoordProf_mean<- coordinateProfiles(object = model, threshold = threshold,
                                       uq_computations = FALSE, options_approx = options_approx,
                                       plot_level=3, plot_options= options_plots, return_level=3,
                                       options_full=options_full)

## Not run: 
## UQ computations might require a long time
# set up simulation options
## reduce nsims and batchsize for faster/less accurate UQ
nsims=200
opts_sims<-list(algorithm="B", lower=rep(0,d ),
                upper=rep(1,d ), batchsize=150,
                optimcontrol=list(method="genoud", pop.size=100,print.level=0),
                integcontrol = list(distrib="sobol",n.points=2000),nsim=nsims)

opts_sims$integration.param <- integration_design(opts_sims$integcontrol,
                                                  d , opts_sims$lower,
                                                  opts_sims$upper,
                                                  model,threshold)
opts_sims$integration.param$alpha <- 0.5

# run UQ computations
CF_CoordProf_UQ<- coordinateProfiles(object = CF_CoordProf_mean, threshold = threshold,
                                     uq_computations = TRUE, options_approx = options_approx,
                                     plot_level=3, plot_options= options_plots, return_level=3,
                                     options_sims=opts_sims,options_full=options_full,
                                     options_bound = list(beta=0.024,alpha=0.05))

## End(Not run)

profExtrema documentation built on March 22, 2020, 1:07 a.m.