Models and Predictions for "The Proper Care and Feeding of CAMELS: How Limited Training Data Affects Streamflow Prediction"
| Main Authors: | Gauch, Martin, Mai, Juliane, Lin, Jimmy |
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| Format: | info dataset eJournal |
| Terbitan: |
, 2019
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/3550915 |
Daftar Isi:
- Models and Predictions This dataset contains the trained XGBoost and EA-LSTM models and the models' predictions for the paper The Proper Care and Feeding of CAMELS: How Limited Training Data Affects Streamflow Prediction. For each combination of model (XGBoost, EA-LSTM), training years (3, 6, 9), number of basins (13, 26, 53, 265, 531), and seed (111-888), there are five folders. Each corresponds to a random basin sample (for 531 basins there's only one folder, since it's all basins). In each folder, there are three files: \(\texttt{model.pkl}\) (XGBoost) or \(\texttt{model_epoch30.pt}\) (EA-LSTM), which stores the pickled trained model \(\texttt{xgboost_seedNNN.p}\) or \(\texttt{ealstm_seedNNN.p}\), which stores a pickled dictionary that maps each basin to the DataFrame of predicted and actual daily streamflow. \(\texttt{attributes.db}\), which stores static catchment attributes needed for inference. In addition to each folder, there is a SLURM submission script called \(\texttt{<foldername>.sbatch}\) that was used to create and evaluate the model in the folder.