In this webinar, we will focus on the classification of NHD catchments across CONUS into three types based on their hydrological characteristics, specifically examining drought propagation mechanisms based on Apurv et al., (2017). Utilizing StreamCat data, we will train a machine-learning model and leverage the HyRiver software stack for efficient data retrieval and processing operations.
We use the following software stack:
- Python 3.10
- HyRiver: For data retrieval and processing
- PyTorch Tabular: For training a machine learning model and making predictions over the entire CONUS
Note that PyTorch Tabular is not available on conda-forge
yet, so we will install it from
pip. First, create a new conda/mamba environment:
conda create -n cop-webinar python=3.10
conda activate cop-webinar
Then, install the dependencies:
pip install -r requirements.txt