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186a48a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | {
"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"
]
}
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