Snow depth prediction using Random Forest for a watershed

Contains Code and data to train the algorithm

This repo contains code for Meloche et al. (2022) : High-resolution snow depth prediction using Random Forest algorithm with topographic parameters: A case study in the Greiner watershed, Nunavut (https://doi.org/10.1002/hyp.14546)

Training data are magnaprobe measurements or point observations (y) with all topographic and ecotype parameters for training (x)

Training data : magnaprobe_clean_Greiner.csv

Code in notebook : RF_Greiner_Github.ipynb

Prediction (X) data (for full map) as raster data : RF_variable_GreinerWatershed.nc

Final snow depth map : Ecotype_Greiner_snowRF.tif

To access all data and snow depth map (https://zenodo.org/records/10362396) DOI : 10.5281/zenodo.10362395