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feat: hgb-v2 — regression heads predict rise, recovering flood warning lead 
Rolling-origin evaluation (5 monsoon folds x 4 variants, P.1 + P.103;
results in models/eval_variants.json) showed the absolute-level target
alerting AT the crossing on essentially every event, while the rise
target (future max - current level, level added back at serving) gives
+6h on the hard 2024 crossings, +45h in 2025, fewer false alarms than
weighted/quantile variants, and ~11% better MAE. Weighted and quantile
variants rejected: more false alarms, no Brier-score calibration gain.

Ported to production: train.py fits rise in both eval and refit passes
(sigma/metrics computed in absolute space), bundles stamped hgb-v2 with
regression_target='rise', predict.py adds the level back for v2 and
stays compatible with v1 bundles, backtest_render.py mirrors the same
math. Regenerated backtest charts: 2024 first alert 11:00 24 Sep (6h
BEFORE the 17:00 crossing, was 18h after), 2025 alert 45h ahead, and
the record-peak underprediction is gone (rise models can exceed the
training max). The >=12h acceptance gate still fails honestly at +6h —
closing that needs rainfall inputs. New P.1 MAE 5.0/7.2/9.4 cm at
6/12/24h; docs updated throughout.
2026-08-12 15:43:29 +07:00

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"peak_level": 6.64,
"peak_pred_24h_before": 5.73
},
{
"crossing": "2025-10-03T06:00:00",
"lead_h": 42.0,
"peak_level": 6.14,
"peak_pred_24h_before": 6.00597561680636
}
],
"false_alarm_episodes": 1
}
}
}
]
}
]
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