bestFM: Function to select best standard fuel model for observed fire...
In Rothermel: Rothermel fire spread model for R

Description Usage Arguments Value Author(s) References See Also Examples

The function preloads the 13 fire behavior fuel models by Albini (1976) and the 40 fuel models by Scott & Burgan (2005), computes rate of spread using Rothermel's (1972) model for a vector or data frame of fire experiment data, and computes root mean square error and mean bias of each fuel model to observed rate of spread.

1	bestFM(obs, m, u, slope)

`obs`	a single value, or vector, of observed ROS [m/min].
`m`	a vector, or data frame, 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 or columns; 0 if fuel class is absent).
`u`	a single value, or vector, of midflame windspeed [km/h].
`slope`	a single value, or vector, of percent site slope [percent].

If a single observation of rate of spread is analyzed (length of obs =1), a vector of bias values, computed as (predicted - observed ROS) for every standard fire behavior fuel model. If more observations are analyzed (length of obs >1), a data.frame of root mean square error and mean bias computed from observed and predicted ROS using every standard fire behavior fuel model.

Giorgio Vacchiano, Davide Ascoli (DISAFA, University of Torino, Italy)

Albini, F. A. (1976). Estimating wildfire behavior and effects. Gen. Tech. Rep. INT-30. Ogden, UT: US Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station.

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.

Scott, J., Burgan, R. E. (2005). A new set of standard fire behavior fuel models for use with Rothermel's surface fire spread model. Gen. Tech. Rep. RMRS-GTR-153. Fort Collins, CO: US Department of Agriculture, Forest Service, Rocky Mountain Research Station.

ros, SFM_metric

data ("firexp")
a <- bestFM (obs = firexp$ros,
            m = firexp[,18:22],
            u = firexp[,"u"],
            slope = firexp[,"slope"])  
a      

# plot results
barplot (a$error,
        cex.names = 0.7,
        las = 2,
        ylab = "rmse",
        col = sign (a$bias) + 3,
        names.arg = rownames (a))
          
legend (1, max (a [, 1]), 
        c ("Overprediction", "Underprediction"),
        col = c ("blue", "red"),
        pch = 15)

Loading required package: GA
Loading required package: foreach
Loading required package: iterators
Package 'GA' version 3.2
Type 'citation("GA")' for citing this R package in publications.

Attaching package: 'GA'

The following object is masked from 'package:utils':

    de

Loading required package: ftsa
Loading required package: forecast
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

Loading required package: zoo

Attaching package: 'zoo'

The following objects are masked from 'package:base':

    as.Date, as.Date.numeric

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':

    sd, var

        error     bias
GR5  3.121975  0.44600
SH7  3.257931 -0.55875
GR3  3.656886 -0.76075
GR4  3.933516 -1.21750
GS3  4.054163 -1.47200
SH5  4.225140  0.80575
GR6  4.246033  2.98375
SH4  4.545255 -2.13925
GS4  4.650780 -2.20350
SH9  4.885132  3.58800
SB4  5.147378 -3.02850
A3   5.943305  4.65800
SH8  5.967397 -3.58075
TU3  6.006835 -3.85950
GR7  6.276689  2.49800
SH6  6.412111 -4.03875
A7   6.582937 -4.37750
A2   6.869257 -5.28950
GR2  6.912736 -5.29125
A6   7.227605 -5.07250
A5   7.430227 -5.92650
GS2  7.539070 -5.74975
A1   7.701067 -6.58725
A4   7.786666  6.47750
SB3  8.097855 -5.92275
GS1  9.120844 -7.36925
A13  9.271153 -6.86125
GR1  9.552145 -7.52175
A12  9.652704 -7.28200
SB2  9.726723 -7.40950
TU2  9.731777 -7.39975
TU5  9.816123 -7.41500
A10  9.880522 -7.54325
SH3  9.967687 -7.84925
SH2 10.224830 -7.98175
TL9 10.556723 -8.13300
A9  10.647256 -8.26450
GR8 10.709574  9.43550
TU4 10.808675 -8.67775
A11 10.812739 -8.38675
SB1 10.835564 -8.39950
TU1 10.924462 -8.49400
TL8 10.945395 -8.49425
TL6 10.946705 -8.51675
TL5 11.101478 -8.64875
SH1 11.199926 -8.79650
TL7 11.251466 -8.76775
TL4 11.362737 -8.88300
A8  11.457112 -8.97200
TL3 11.459450 -8.97425
TL2 11.530464 -9.03925
TL1 11.592971 -9.09400
GR9 22.050927 19.46475