pcmGridsearch: Grid search optimization for PCM runout distance

In jngtz/runout.opt: GPP regional runout model optimization and validation in R

Description

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

Usage

pcmGridsearch(
  dem,
  slide_plys,
  slide_src,
  slide_id = NULL,
  rw_slp,
  rw_ex,
  rw_per,
  pcm_mu_v,
  pcm_md_v,
  gpp_iter = 1000,
  buffer_ext = 500,
  buffer_source = NULL,
  predict_threshold = 0.5,
  save_res = FALSE,
  plot_eval = 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_v	A vector of PCM model sliding friction coefficients
pcm_md_v	A vector of PCM model mass-to-drag ratios (m)
gpp_iter	Number of model iterations
buffer_ext	(Optional) Defines buffer distance (in meters) around runout polygon to crop source DEM. This helps to reduce computational time when working with large regions.
buffer_source	(Optional) Can define a buffer distance (in meters) to extend source point to a source area
predict_threshold	A cutoff value to define what quantile of simulated runout frequencies is the predicted runout.
save_res	(Logical) if TRUE, will save results in current working directory
plot_eval	(Logical) ff TRUE, will plot simulated runout and runout polygon
saga_lib	The initiated SAGA-GIS geoprocessor object

Value

A list with performances for each parameter combinations across grid search space. The performances measures include relative error, relative difference, error and AUROC.

jngtz/runout.opt documentation built on Sept. 8, 2021, 2:15 p.m.