gauchm / mlstream

Machine Learning for Streamflow Prediction

mlstream

Machine learning for streamflow prediction.

PyPI: https://pypi.org/project/mlstream/

Documentation: https://mlstream.readthedocs.io/

Usage

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.

Training

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()

Inference

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()