PCM runout distance performance

Computes the error for runout distances simuluated using the random walk and PCM model components of the GPP tool in SAGA-GIS.

pcmPerformance(
  dem,
  slide_plys,
  slide_src,
  slide_id = 1,
  rw_slp = 33,
  rw_ex = 3,
  rw_per = 2,
  pcm_mu = 0.3,
  pcm_md = 75,
  buffer_ext = 500,
  buffer_source = 50,
  gpp_iter = 1000,
  predict_threshold = 0.5,
  plot_eval = FALSE,
  return_features = FALSE,
  saga_lib
)

Arguments

dem	A DEM as a RasterLayer object
slide_plys	Runout tracks as a SpatialPolygonsDataFrame
slide_src	Source points as a SpatialPointsDataFrame or source areas as a SpatialPolygonsDataFrame
slide_id	Selects a single runout polygon from slide_plys by row
rw_slp	Random walk slope threshold - below lateral spreading is modelled
rw_ex	Random walk exponent controlling lateral spread
rw_per	Random walk persistence factor to weight flow direction consistency
pcm_mu	PCM model sliding friction coefficient
pcm_md	PCM model mass-to-drag ratio (m)
buffer_ext	(Optional) Defines buffer distance (in meters) around runout polygon to crop source DEM. This helps to reduce computational time
buffer_source	(Optional) Can define a buffer distance (in meters) to extend source point to a source area
gpp_iter	Number of model iterations
predict_threshold	A cutoff value to define what quantile of simulated runout frequencies is the predicted runout.
plot_eval	logical. If TRUE will plot simulated runout and runout polygon
return_features	logical. If TRUE, returned list will include GPP input and output data, in addition to a list of error measures.
saga_lib	The initiated SAGA-GIS geoprocessor object

Value

A list of runout distance performance measures.

Examples

if (FALSE) {
# Initialize a saga object
saga <- Rsagacmd::saga_gis()

# Load elevation model (DEM)
dem <- raster(system.file("extdata/elev_12_5m.tif", package="runout.opt"))

# Load runout polygons and source points
runout_plys <- rgdal::readOGR(system.file("extdata/dflow_runout_ply.shp", package="runout.opt"))
source_pnts <- rgdal::readOGR(system.file("extdata/dflow_source_pnt.shp", package="runout.opt"))

# Run GPP PCM model for a rounout polygon
pcm <- pcmPerformance(dem, slide_plys = runout_plys[1,], slide_src = source_pnts,
  rw_slp = 40, rw_ex = 3, rw_per = 1.5,
  pcm_mu = 0.15, pcm_md = 120,
  gpp_iter = 1000, buffer_ext = 500, buffer_source = 50,
  plot_eval = TRUE, return_features = TRUE)

# Runout distance relative error
pcm$length.relerr

# Plot GPP PCM runout modelling ouputs
gpp_output <- stack(pcm$gpp.parea, pcm$gpp.stop, pcm$gpp.maxvel)
names(gpp_output) <- c("Process_area", "Stop_positions", "Max_velocity")
plot(gpp_output)

}