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160617e87b |
feat: openmeteo_rain 2021+ backfill entry point
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rain.backfill_db pushes the full cached Open-Meteo archive into the openmeteo_rain table in 5k-row idempotent upsert chunks; scripts/backfill_rain_db.py is the thin CLI (DB from Config/.env or --db-url). Safe to re-run and safe alongside the hourly live writer. |
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df0ae8cda3 |
feat: hgb-v3 — Open-Meteo rain features clear the 12h warning gate
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The rolling-origin harness (models/eval_rain.json) showed catchment rain halving flood-year Brier scores, cutting flood-regime MAE 20-40%, and extending the hard 2024 leads (+6h -> +11h at P.1, +10h -> +19h at P.103). Ported: train_all loads the catchment-mean series (use_rain / --no-rain to opt out; without it bundles train as v2), predict fetches live rain hourly and passes an empty series on failure so rain-trained bundles serve with NaN features instead of tripping the feature guard, and the leader worker persists hourly per-point + catchment-mean rows to a new openmeteo_rain table. Regenerated backtest: the 2024 record flood now gets a 13-HOUR WARNING (alert 04:00 vs 17:00 crossing, river at 2.9m at alert time) — the >=12h acceptance gate PASSES for the first time. Journey on that crossing: v1 -18h, v2 +6h, v3 +13h. The marginal 2025 double-crest trades its artifact +46h latch for a calibrated +2h with zero false alarms. P.1 MAE 4.9/7.2/8.7 cm at 6/12/24h. Docs updated throughout. |
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cbb3bf7369 |
feat: Open-Meteo catchment rainfall series + rain features + harness variant
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src/ml/rain.py fetches hourly precipitation for five upper-Ping catchment points (Chiang Dao, Mae Taeng, Mae Ngat, Mae Rim, city) from the Open-Meteo forecast-model archive (2021-03 onward, no API key, Bangkok-local timestamps, year-chunked local cache) plus the live forecast endpoint for serving (trailing days + next 48h). features.build_features/build_matrix accept the catchment-mean series and add rain_6h/24h/72h trailing sums and rain_fc24 — the forward 24h sum, a genuine forecast feature (archived forecasts at training time, a real weather forecast at serving; never contains river data). Columns exist only when a series is provided: HGB rejects all-NaN columns at fit, so no-rain training omits them and serving passes an empty series for alignment. evaluate.py gains the rise_rain variant (and --no-rain) so the harness can judge whether rain beats the deployed rise baseline. |