5 Commits

Include renames

Author	SHA1	Message	Date
grabowski	6d4beae65a	fix(ml): serve the forecast rain the model was trained with ... build_features cut the rain series to the gauge grid before the forward sum, and the grid ends at the newest reading, so the row every live forecast is made from had rain_fc24 = NaN. Training and all evaluations had the real Open-Meteo forecast there. The forward sum now runs over the grid extended by 24 h; earlier rows are unchanged. Harness (new opt-in variant rise_rain_fc24_missing = rise_rain with rain_fc24 blanked on test rows, five folds, --from-cache): P.1 and P.103 nearly unaffected, P.21 false alarms 10 -> 5 and MAE 0.232 -> 0.215 with the fix. Write-up in docs/FLOOD_FORECASTING.md section 5; results in models/eval_2026-10-18_fc24.json. Also: HII mirror rows in one hour now resolve to the latest reading via a stable sort on the raw time (was an unstable sort on the floored hour, i.e. row-order dependent). Needed for an exact Rust port.	2026-10-18 14:07:56 +07:00
grabowski	d621aa9ce7	eval: quantile heads and fc48 on top of hgb-v3 (rejected/deferred); HII gauge-rain aggregate ... Rolling-origin harness gains rise_rain_quantile, rise_rain_quantile_uw, rise_rain_qsigma (L2 point + quantile sigma) and rise_rain_fc48, all opt-in, plus --from-cache for reproducible offline reruns. Results in models/eval_2026-09-12*.json, write-up in docs/FLOOD_FORECASTING.md: - quantile point prediction: better MAE, worse first-alert lead at 5 of 11 events (P.103 2022-08-14 +6h -> +1h) -> rejected - quantile sigma only: Brier within noise (0.0031 -> 0.0029) -> not worth 3x heads - rain_fc48: neutral everywhere except 2024-10-03 P.1 (+21h -> +72h), n=1 -> deferred to after the 2026 season src/ml/hii_rain.py: catchment-mean hourly rain from the ~130 HII gauges in the upper-Ping box and a 24h-sum comparison against Open-Meteo. Not a training feature (table exists only since 2026-08-11, no archive); exposed at GET /api/hii/rainfall/catchment so the two sources' agreement is on record by the time a fold can test it. data._read_cache now skips non-station files in models/cache/ (the shared dir also holds rain_openmeteo / dam_* caches, which crashed the reader). scripts/summarize_eval.py prints per-variant lead/peak-error tables.	2026-09-11 21:55:37 +02:00
grabowski	df0ae8cda3	feat: hgb-v3 — Open-Meteo rain features clear the 12h warning gate ... 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.	2026-08-12 17:05:01 +07:00
grabowski	21e9d2e114	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
grabowski	4358d52d55	feat: ML flood-event forecasting from 8 years of gauge history ... Add src/ml/ package predicting, per station and per 6/12/24 h horizon, the probability of exceeding warning (3.0 m) and danger (4.5 m) levels plus expected peak level, trained on the 592k-row PostgreSQL history: - features.py: hourly grid with coverage gating and no future leakage; upstream stations enter at empirically measured travel-time lags (P.20 +17h ... P.103 +1h vs P.1); hour-of-day deliberately excluded (it encodes the scrape schedule, not hydrology) - train.py: HistGradientBoosting regression + warn/danger classifier heads per station x horizon, >=30-positives gate with calibrated sigmoid-on-regression fallback, strict temporal splits, per-event lead-time evaluation; guards against sklearn 1.9.0 crash on degenerate feature columns - predict.py: bundle loading with feature-name checks, heuristic fallback tier, get_latest_forecasts() for the API; raises when no models are trained so the endpoint 503s instead of serving persistence output as forecasts - data.py: Postgres-first loader (FLOOD_ML_DB_URL override), HTTP API fallback (flagged: that path backfills synthetic discharge), csv.gz cache - /forecast endpoint (15-min TTL cache) + dashboard flood-risk panel (hidden until models exist) - docs/FLOOD_FORECASTING.md: full system doc with measured deployment numbers (~335 MB RSS, CPU negligible, ~6 min full retrain) and retraining policy Validation: out-of-sample backtest of the record 2024 flood season (train <= Aug 2024) alerted 24-48 h ahead of the Oct 5 peak; 2025-26 test split: P.1 6h PR-AUC 0.974, recall 98.3% at 1% false-alarm rate. Also: fix P.81 station coordinates (was Ban Pong/Ratchaburi, 493 km out of basin; now 18.6936 N 99.0819 E per RID station page), pin scikit-learn==1.9.0 and numpy<2, gitignore model artifacts (~100 MB, train on the server via scripts/train_flood_model.py).	2026-08-10 12:49:47 +07:00