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"model_name": "GlobalSurgeML",
"model_type": "globalsurgeml",
"architectures": [
"GlobalSurgeML"
],
"framework": "PyTorch",
"domain": "physical-oceanography",
"task": "daily-maximum-storm-surge-regression",
"implementation": {
"entry_point": "model/globalsurgeml.py",
"scope": "core-method and real-feature-dimension scaled end-to-end 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": {
"family": "station-wise PCA stepwise-linear and random-forest regression ensemble",
"configurations": [
"LR-RS",
"LR-RS-lag",
"RF-RS-lag",
"LR-AR",
"LR-AR-lag",
"RF-AR-lag"
],
"daily_feature_count": 50,
"lagged_feature_count": 300,
"p_value_threshold": 0.05,
"random_forest_trees": 50,
"engineering_random_forest_max_depth": 4,
"paper_pca_explained_variance": 0.9,
"paper_post_pca_feature_range": "300-500"
},
"data": {
"datasets": [
"GESLA-2",
"CCMP",
"20CRV2c",
"Microwave OI SST",
"GPCP",
"ERA-Interim",
"GTSR"
],
"protocol": "global_storm_surge_pca_station_daily_v1",
"format": "NPZ",
"input_layout": "NF",
"target_layout": "N1",
"input_shapes": {
"rs_daily": ["N", 50],
"rs_lagged": ["N", 300],
"ar_daily": ["N", 50],
"ar_lagged": ["N", 300]
},
"target": "daily maximum non-tidal residual",
"target_unit": "m",
"sample_interval_hours": 24,
"lag_interval_hours": 6,
"maximum_lag_hours": 30,
"rs_variables": ["u", "u2", "u3", "v", "v2", "v3", "slp", "sst", "precipitation"],
"ar_variables": ["u", "u2", "u3", "v", "v2", "v3", "slp"]
},
"configuration_sources": [
"conf/config.yaml",
"model/globalsurgeml.py",
"scripts/fake_data.py",
"scripts/train.py",
"scripts/inference.py",
"scripts/result.py"
]
}
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