random-forest-substrate

Project information

Generates a model predicting substrate class (rock, mixed, sand, or mud) using random forest and a stack of environmental predictors. Model is tested against withheld testing observations. Model can be run using a 1-step or 2-step approach. The 2-step approach generates a binary rock/not-rock layer and a mixed/sand/mud layer and the two are combined. This approach was created to reduce the prevalence of predicted rock in the output raster. Substrate index layers (densities per class) can also be produced.

substrate classification random forest

14 Commits
5 Branches
3 Tags
3 Releases

README
MIT License

Created on

January 31, 2020

Copy SSH clone URLgit@gitlab.com:dfo-msea/environmental-layers/random-forest-substrate.git
Copy HTTPS clone URLhttps://gitlab.com/dfo-msea/environmental-layers/random-forest-substrate.git

Loading