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Machine learning for streamflow prediction

Project description


Machine learning for streamflow prediction.

Oxford English Dictionary:

maelstrom, n.   /ˈmeɪlˌstrɑm/

  1. A powerful whirlpool, originally (usually Maelstrom) one in the Arctic Ocean off the west coast of Norway, which was formerly supposed to suck in and destroy all vessels within a wide radius.
  2. Any state of turbulence or confusion; a swirling mass of small objects.


This project is still 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',
                 forcing_attributes=['precip', 'tmax', 'tmin'],
                 static_attributes=['area', 'regulation'])



run_dir = Path('./experiments')
exp = Experiment(data_path, is_train=False, 
                 start_date='01012016', end_date='31122018')
model.load(run_dir / 'model.pkl')
results = exp.predict()

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Files for mlstream, version 0.1
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