Machine learning for streamflow prediction.
PyPI: https://pypi.org/project/mlstream/
Documentation: https://mlstream.readthedocs.io/
This project is work in progress. The idea is to create an easy way of training machine learning streamflow models: Just provide your data, select a model (or provide your own), and get the predictions.
exp = Experiment(data_path, is_train=True, run_dir=run_dir,
start_date='01012000', end_date='31122015',
basins=train_basin_ids,
forcing_attributes=['precip', 'tmax', 'tmin'],
static_attributes=['area', 'regulation'])
exp.set_model(model)
exp.train()run_dir = Path('./experiments')
exp = Experiment(data_path, is_train=False,
run_dir=run_dir,
basins=test_basin_ids,
start_date='01012016', end_date='31122018')
model.load(run_dir / 'model.pkl')
exp.set_model(model)
results = exp.predict()
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