| { |
| "model_name": "Streamflow-LSTM", |
| "model_type": "gauge_specific_stacked_lstm", |
| "architectures": ["StreamflowLSTM"], |
| "framework": "PyTorch", |
| "torch_dtype": "float32", |
| "transformers_version": "4.44.0", |
| "domain": "hydrology", |
| "task": "six-hourly-streamflow-forecasting", |
| "license": "apache-2.0", |
| "paper": { |
| "title": "Using a long short-term memory (LSTM) neural network to boost river streamflow forecasts over the western United States", |
| "doi": "10.5194/hess-26-5449-2022" |
| }, |
| "implementation": { |
| "entry_point": "model/streamflow_lstm.py", |
| "scope": "core-method reduced-ensemble engineering reproduction", |
| "train_script": "scripts/train.py", |
| "inference_script": "scripts/inference.py", |
| "evaluation_script": "scripts/result.py", |
| "synthetic_data_script": "scripts/fake_data.py" |
| }, |
| "architecture": { |
| "input_shape": ["B", 28, 23], |
| "output_shape": ["B"], |
| "paper_hidden_sizes": [50, 50, 50], |
| "activations": ["ReLU", "ReLU", "tanh"], |
| "dense_outputs": 1 |
| }, |
| "data": { |
| "format_version": "streamflow-lstm-v1", |
| "gauges": 10, |
| "input_shape": ["B", 28, 23], |
| "forecast_steps": 40, |
| "step_hours": 6, |
| "unit": "m3 s-1", |
| "synthetic": true |
| }, |
| "checkpoint": { |
| "path": "result/checkpoints/streamflow_lstm.pt", |
| "format": "single-file multi-gauge multi-member PyTorch checkpoint", |
| "official_weights": false |
| }, |
| "configuration_sources": [ |
| "conf/config.yaml", |
| "model/streamflow_lstm.py", |
| "scripts/fake_data.py", |
| "scripts/train.py", |
| "scripts/inference.py", |
| "scripts/result.py" |
| ] |
| } |
|
|
