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).

.gitignore

@@ -137,3 +137,14 @@ vm_data/
 grafana_data/
 # Runtime station config (persisted CRUD); bundled default lives in src/data/
 /stations.json
+
+# Ruflo local secrets and runtime data
+.env.*.local
+.claude-flow/data/
+.claude-flow/logs/
+.claude-flow/sessions/
+
+# Trained flood-forecast model artifacts (produced on the server, ~100 MB; see docs/FLOOD_FORECASTING.md)
+models/*.joblib
+models/cache/
+models/metrics.json