Machine learning for streamflow prediction
Machine learning for streamflow prediction.
Oxford English Dictionary:
maelstrom, n. /ˈmeɪlˌstrɑm/
- 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.
- 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', 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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