R: Function to carry out uncertainty propagation analysis on...
rosunc
R Documentation
Function to carry out uncertainty propagation analysis on Rothermel's (1972) fire spread model
Description
This function offers the user the possibility to perturb inputs to Rothermel's (1972) fire behavior model and propagate the uncertainty to the resulting estimate of Rate of spread [m/min] by means of Monte Carlo iterative sampling. Random values are extracted from Gaussian distributions with mean = observed values, and spread defined by a custom ratio of standard deviation to the mean defined by the user.
a vector of fuel load [t/ha] for fuel classes 1-hour, 10-hour, 100-hour, live herbs and live woody, respectively (5 values; 0 if fuel class is absent).
s
a vector of surface-to-volume ratio [m2/m3] for fuel classes 1-hour, 10-hour, 100-hour, live herbs and live woody, respectively (5 values; 0 if fuel class is absent).
delta
atomic vector, fuel bed depth [cm]
mx.dead
atomic vector, dead fuel moisture of extinction [percent]
h
a vector of heat content [kJ/kg] for fuel classes 1-hour, 10-hour, 100-hour, live herbs and live woody, respectively (5 values; 0 if fuel class is absent).
m
a vector of percent moisture on a dry weight basis [percent] for fuel classes 1-hour, 10-hour, 100-hour, live herbs and live woody, respectively (5 values; 0 if fuel class is absent).
u
atomic vector, midflame windspeed [km/h]
slope
atomic vector, site slope [percent]
sdu
coefficient of variation for wind speed (ratio of standard deviation to the mean; default = no perturbation)
sdm
coefficient of variation for fuel moistures (ratio of standard deviation to the mean; default = no perturbation)
sds
coefficient of variation for slope (ratio of standard deviation to the mean; default = no perturbation)
sdw
coefficient of variation for fuel loadings (ratio of standard deviation to the mean; default = no perturbation)
sdd
coefficient of variation for fuel bed depth (ratio of standard deviation to the mean; default = no perturbation)
nsim
number of Monte Carlo iterations (default =1000)
Value
A vector of predicted ROS [m/min] from Monte Carlo simulations.
Author(s)
Giorgio Vacchiano, Davide Ascoli (DISAFA, University of Torino, Italy)
References
Cruz M. G. (2010). Monte Carlo-based ensemble method for prediction of grassland fire spread. International Journal of Wildland Fire 19: 521-530.
Jimenez E., Hussaini M. Y., Goodrick S. (2008). Quantifying parametric uncertainty in the Rothermel model. International Journal of Wildland Fire, 17: 638-649.
Rothermel, R. C. (1972). A mathematical model for fire spread predictions in wildland fires. Research Paper INT-115. Ogden, UT: US Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station.
See Also
ros, SFM_metric, firexp
Examples
data ("firexp")
varnames <- names (firexp)
# select only one observation and create a numeric vector for function input
firexp <- as.numeric (firexp [5,])
names (firexp) <- varnames
pred <- rosunc (
modeltype = "D",
w = firexp [1:5],
s = firexp [6:10],
delta = firexp ["Fuel_Bed_Depth"],
mx.dead = firexp ["Mx_dead"],
h = firexp [13:17],
m = firexp [18:22],
u = firexp ["u"],
slope = firexp ["slope"],
sdm = 0.3,
nsim = 100)
summary (pred)
# Figure
hist (pred,
xlab = "ROS [m/min]",
freq = FALSE,
xlim = c (0, max (pred)),
breaks = 20,
main = "")
lines (density (pred), lty=2, lwd=2)
abline (v = firexp ["ros"],col = "red")
text (firexp ["ros"],
max (density (pred)$y),
labels = "obs",
pos = 4)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(Rothermel)
Loading required package: GA
Loading required package: foreach
Loading required package: iterators
Package 'GA' version 3.0.2
Type 'citation("GA")' for citing this R package in publications.
Loading required package: ftsa
Loading required package: forecast
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
Loading required package: timeDate
This is forecast 7.1
Loading required package: rainbow
Loading required package: MASS
Loading required package: pcaPP
Loading required package: sde
Loading required package: stats4
Loading required package: fda
Loading required package: splines
Loading required package: Matrix
Attaching package: 'fda'
The following object is masked from 'package:forecast':
fourier
The following object is masked from 'package:graphics':
matplot
sde 2.0.15
Companion package to the book
'Simulation and Inference for Stochastic Differential Equations With R Examples'
Iacus, Springer NY, (2008)
To check the errata corrige of the book, type vignette("sde.errata")
Attaching package: 'ftsa'
The following objects are masked from 'package:stats':
median, sd, var
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/Rothermel/rosunc.Rd_%03d_medium.png", width=480, height=480)
> ### Name: rosunc
> ### Title: Function to carry out uncertainty propagation analysis on
> ### Rothermel's (1972) fire spread model
> ### Aliases: rosunc
> ### Keywords: models
>
> ### ** Examples
>
> data ("firexp")
> varnames <- names (firexp)
>
> # select only one observation and create a numeric vector for function input
> firexp <- as.numeric (firexp [5,])
> names (firexp) <- varnames
>
> pred <- rosunc (
+ modeltype = "D",
+ w = firexp [1:5],
+ s = firexp [6:10],
+ delta = firexp ["Fuel_Bed_Depth"],
+ mx.dead = firexp ["Mx_dead"],
+ h = firexp [13:17],
+ m = firexp [18:22],
+ u = firexp ["u"],
+ slope = firexp ["slope"],
+ sdm = 0.3,
+ nsim = 100)
>
> summary (pred)
Min. 1st Qu. Median Mean 3rd Qu. Max.
8.57 11.39 12.80 13.16 13.47 26.65
>
> # Figure
> hist (pred,
+ xlab = "ROS [m/min]",
+ freq = FALSE,
+ xlim = c (0, max (pred)),
+ breaks = 20,
+ main = "")
> lines (density (pred), lty=2, lwd=2)
> abline (v = firexp ["ros"],col = "red")
> text (firexp ["ros"],
+ max (density (pred)$y),
+ labels = "obs",
+ pos = 4)
>
>
>
>
>
> dev.off()
null device
1
>