The streamflow (or discharge) of rivers is strongly related to flooding. We aim to make a long-term prediction (for 2050) of rivers around Canada, using different machine learning/statistical models, such as ARIMA and local linear regression.
The data comes from the CANOPEX dataset and combines streamflow discharge combined with gridded climate data (temperature and precipitation) of 10 x 10m resolution. Contains data from 1968 to 2012.
We also make many climate datasets machine learning-friendly and share them in data.md.
data.md contains a summary of pre and post-processed datasets and links for download.
preprocess_geo_data contains notebooks used to preprocess the data quickly.
notebooks contains draft ipython notebooks of models and analyses.
env.sh creates a conda virtual environment and installs all the dependencies. Read the file before executing, you need to install miniconda3.
numpypandasnetCDF4rasteriojupyterlabpyprojkerastensorflow-gpustatsmodelsscikit-learncftimematplotlibpmdarimaplotlycufflinks