Digitize the 7 Chiang Mai inundation zones from the municipal map into
geojson polygons: pixels classified by legend color, vectorized via
marching squares, and georeferenced by a 6-parameter affine fitted
least-squares to the OSM Ping centerline (109 m median residual after
outlier-filtered refits - the scanned image overlay could never scale
correctly and is removed).
The dashboard renders the zones as a Leaflet geoJSON layer styled live
from the forecast: fill opacity scales with each zone's 24 h exceedance
probability, zones whose trigger level the river has already reached get
a solid red border, and each polygon's popup shows its trigger level and
live probability. Zone styles refresh with every forecast load.
Replace the network-wide (3.0, 4.5) m thresholds with per-station values
calibrated from the DB's discharge_percent (RID % of channel capacity):
warning = median level at 75-85% capacity, danger = median at 95-105%.
Fixes P.103 over-alerting (bank-full ~6.75 m, not 4.5) and P.67
under-alerting (overflow ~2.9 m). Requires a retrain to take effect in
the classifier heads.
P.1 uses the official Chiang Mai municipal inundation map instead:
warning 3.70 m (stage 1, city flooding begins), danger 4.20 m (stage 5),
with the full 7-stage table (3.70-4.60 m + discharge) in
features.P1_FLOOD_STAGES. Forecast rows for P.1 now include per-stage
exceedance probabilities computed from the regression head + calibration
sigma - available immediately without retraining.
Dashboard: "Chiang Mai city flood outlook" block above the forecast grid
(predicted peak + 7 stage-probability chips) and a toggleable
georeferenced overlay of the official flood-zone map
(static/flood-zones-p1.jpg, bounds tunable in FLOOD_ZONE_BOUNDS).
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).
Station selection showed no history since 21ca844: Chart.js v4 datasets
had parsing:false with plain number arrays, drawing empty axes. Remove
the flag so the chart parses values again.
Backend hardening for the same flow:
- /measurements/history/{code} no longer 503s on non-Postgres configs;
it falls back to the configured adapter (reversed to ascending order)
- DB_TYPE defaults to postgresql when POSTGRES_CONNECTION_STRING is set
and DB_TYPE is unset, so the .env psql wins over the sqlite default
- zero readings (0.0) are no longer coerced to None, which would fail
MeasurementResponse validation and 500 /measurements/latest
Map visualization:
- river segments are now colored, widened and dash-speed-animated by
the discharge at the nearest gauge (same scale as the marker legend)
- fix z-order bug that drew the animated flow line behind its casing
- legend entries for river lines, reduced-motion fallback
River geometry: rebuild ping-river-network.geojson from Overpass
(110 -> 202 features), restoring missing Ping mainstem reaches through
the Bhumibol reservoir and the Tak-Kamphaeng Phet braided section
(unnamed waterway=river ways in OSM), with short synthetic connectors
(connector: true) bridging remaining sub-8 km holes.
Extract the inline root() dashboard markup into src/static/dashboard.html,
loaded once at import. Keeps HTML out of the Python module (web_api 616 -> 576
lines) with a defensive fallback if the file is missing.
Full <500 compliance for web_api still needs the endpoints split into
APIRouter modules; tracked as remaining #4 work.
grabowski