docs/img/backtest-2024-p1.png: observed P.1 level vs the 24h-ahead
predicted peak issued at each hour by a model trained only on pre-flood
data, with the warning-probability panel below (first alert 24 Sep
01:00, 24 h before flooding began). Embedded in FLOOD_FORECASTING.md's
headline-validation section with an honest reading, including the
~0.4 m peak under-prediction.
Replay pacing is now adaptive: 4 h/frame through quiet days, 2 h when
risk is elevated, 1 h (hour-by-hour) while the model is alerting or the
river is near/above flood stage - so viewers can watch the detection
sequence unfold.
The 2024 snapshot now carries a model track: p_warning and predicted
24 h peak from HGB heads trained ONLY on pre-flood data (an honest
backtest, cutoff 2024-08-31), plus its calibration sigma. During replay:
- the map clock shows the system's state at each moment: 'model: no
flood expected' -> 'elevated risk' -> amber 'MODEL ALERT - flooding
within 24 h likely (predicted peak X m)' -> red 'FLOODING' once the
river actually crosses stage 1. First alert fires 24 Sep 01:00, a
full day before the water crossed the flood line on 25 Sep.
- the flood-risk outlook stage chips re-render each frame from the
historic model prediction (sigmoid over the stage levels), so the
whole outlook panel time-travels with the map.
The replay now drives the entire map from a 120 kB all-station snapshot
(static/flood-2024-all.json: 649 hourly frames x 16 stations of level +
discharge, forward-filled on an aligned grid):
- station markers recolor/resize per frame from historic discharge
- river segments restyle per frame (color/width from the same
nearest-gauge grading as live mode)
- flood zones flood/recede from P.1's historic level
- a large clock overlay on the map shows replay date/time, P.1 level
and total basin flow
- the LIVE DATA pill switches to an amber '2024 REPLAY' (or
'SIMULATION' for the demo_level/demo_rise hooks) and back on finish
- replay end or stop restores the live view via a full dashboard reload
Replaces the P.1-only snapshot (flood-2024-p1.json removed).
- header button 'Replay Oct 2024 flood': animates the real Sep 15 -
Oct 12 2024 P.1 hourly series (~45 s) through the flood-zone display;
zones flood blue as the river climbs to the 5.30 m record and recede
as it falls; click again to stop; restores live state when done
- replay reads a pre-extracted 15 kB static snapshot
(static/flood-2024-p1.json, 335 frames) instead of pulling the 4.6 MB
all-time history on every click; falls back to the history API if the
snapshot is missing
- demo hooks for testing/presentations: ?demo_level=4.4 pins a simulated
P.1 level, ?demo_rise=1 animates rising water to 5.30 m; both label
the outlook line as SIMULATION with the real level alongside
When P.1's current level reaches a zone's trigger level the zone fills
water-blue (solid border, .62 opacity) instead of the amber risk ramp,
and the zone layer adds itself to the map automatically - no toggle
needed during an actual flood. A manual hide sets a session flag so
auto-show does not fight the user.
src/static/custom-markers.json holds user points of interest
({name, lat, lon, optional note}); the dashboard renders them as pin
markers whose popups show which inundation zone the point sits in, its
P.1 trigger level, and the live 24 h exceedance probability (ray-cast
point-in-polygon against the zone geojson; smallest matching zone wins).
Seeded with the owner's four properties.
The previous affine fit slid along the mostly north-south river (an
ill-conditioned direction) leaving the zones ~1 km south-west of their
true position. New approach: extract the scanned river band by color,
match it to the OSM Ping mainstem centerline by arc length (which pins
the along-river position), then correct residual translation using
known district locations cross-checked against the river residual
(533 m -> ~300 m median; Chang Khlan zone centroid now within 200 m).
Mountain-area artifacts are clipped away via the municipality hull.
Zones were also nearly invisible at fillOpacity 0.16 - base opacity is
now 0.38, rising with the live 24 h exceedance probability, and 0.72
with a solid red border once the river is at or above a zone's level.
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