feat: per-station flood thresholds and Chiang Mai inundation stages for P.1
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).
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@@ -20,10 +20,48 @@ logger = logging.getLogger(__name__)
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# Per-station (warning, danger) level thresholds in meters. "*" is the default
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# applied to any station without an explicit override.
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# Per-station (warning, danger) levels in metres on each gauge's own datum.
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# Calibrated 2026-08-10 from the DB's discharge_percent (RID % of channel
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# capacity): warning = median level at 75-85% capacity, danger = median level
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# at 95-105%. P.1 instead uses the official Chiang Mai inundation map keyed to
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# the P.1 gauge: city flooding begins at 3.70 m (stage 1) and reaches most
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# districts by 4.20 m (stage 5) — see P1_FLOOD_STAGES.
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THRESHOLDS: Dict[str, Tuple[float, float]] = {
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"*": (3.0, 4.5),
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"P.1": (3.70, 4.20),
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"P.103": (5.95, 6.75),
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"P.20": (2.35, 2.80),
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"P.21": (3.20, 3.60),
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"P.4A": (3.40, 3.90),
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"P.5": (4.55, 4.95),
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"P.67": (2.45, 2.90),
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"P.75": (2.75, 3.50),
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"P.76": (5.35, 5.45),
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"P.77": (2.85, 3.35),
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"P.81": (5.15, 6.30),
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"P.82": (3.40, 3.80),
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"P.84": (3.45, 3.90),
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"P.85": (2.90, 3.35),
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"P.87": (3.75, 4.05),
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"P.92": (2.95, 3.60),
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}
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# Official Chiang Mai flood-onset stages at the P.1 gauge (Nawarat Bridge),
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# from the municipal inundation map (พื้นที่ท่วมตัวเมืองเชียงใหม่, events of
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# 2548/2554/2565 BE): gauge level in m, RID discharge in m³/s. Each stage
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# floods progressively more city zones.
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P1_FLOOD_STAGES: List[Dict[str, float]] = [
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{"stage": 1, "level": 3.70, "discharge_cms": 405},
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{"stage": 2, "level": 3.90, "discharge_cms": 438},
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{"stage": 3, "level": 4.00, "discharge_cms": 458},
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{"stage": 4, "level": 4.10, "discharge_cms": 478},
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{"stage": 5, "level": 4.20, "discharge_cms": 493},
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{"stage": 6, "level": 4.30, "discharge_cms": 508},
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{"stage": 7, "level": 4.60, "discharge_cms": 558},
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]
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FLOOD_STAGES: Dict[str, List[Dict[str, float]]] = {"P.1": P1_FLOOD_STAGES}
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MONSOON_MONTHS = {6, 7, 8, 9, 10}
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FFILL_LIMIT_H = 3
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MIN_WINDOW_COVERAGE = 0.5
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+30
-16
@@ -154,22 +154,36 @@ def _model_forecast(
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p_warning = _clip_probability(p_warning)
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p_danger = min(_clip_probability(p_danger), p_warning)
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results.append(
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{
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"station_code": station_code,
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"horizon_hours": horizon_h,
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"p_warning": p_warning,
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"p_danger": p_danger,
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"predicted_max_level": predicted_max,
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"current_level": current_level,
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"as_of": as_of.isoformat(),
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"model_version": bundle["model_version"],
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"trained_at": bundle["trained_at"],
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"source": "model",
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"threshold_warning": warn_thr,
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"threshold_danger": danger_thr,
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}
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)
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row = {
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"station_code": station_code,
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"horizon_hours": horizon_h,
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"p_warning": p_warning,
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"p_danger": p_danger,
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"predicted_max_level": predicted_max,
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"current_level": current_level,
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"as_of": as_of.isoformat(),
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"model_version": bundle["model_version"],
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"trained_at": bundle["trained_at"],
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"source": "model",
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"threshold_warning": warn_thr,
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"threshold_danger": danger_thr,
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}
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stages = features.FLOOD_STAGES.get(station_code)
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if stages:
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# Exceedance probability per official inundation stage, from the
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# regression head and its validation-residual sigma. These are
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# threshold-agnostic, so no retraining is needed to serve them.
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row["stages"] = [
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{
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"stage": s["stage"],
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"level": s["level"],
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"p_exceed": _clip_probability(
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_sigmoid_probability(predicted_max, s["level"], sigma_h)
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),
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}
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for s in stages
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]
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results.append(row)
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return results
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@@ -130,6 +130,14 @@
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.risk-chips { display: flex; gap: 6px; }
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.risk-chip { flex: 1; text-align: center; border-radius: 8px; padding: 5px 4px; font-size: .64rem; font-weight: 800; color: white; }
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.risk-chip span { display: block; font-weight: 650; font-size: .58rem; opacity: .85; }
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.p1-outlook { border: 1px solid var(--border); border-left: 4px solid var(--river); border-radius: 12px; padding: 12px 14px; margin-top: 14px; background: #f7fbfa; }
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.p1-outlook-head { display: flex; justify-content: space-between; align-items: center; gap: 10px; flex-wrap: wrap; }
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.p1-outlook-head strong { font-size: .88rem; }
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.p1-peak { color: var(--muted); font-size: .76rem; }
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.stage-strip { display: flex; gap: 6px; flex-wrap: wrap; margin-top: 10px; }
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.stage-chip { min-width: 74px; text-align: center; border-radius: 9px; padding: 6px 8px; font-size: .7rem; font-weight: 800; color: white; }
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.stage-chip small { display: block; font-weight: 650; font-size: .6rem; opacity: .88; }
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.zones-button { font-size: .72rem; padding: 7px 11px; }
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.leaflet-popup-content-wrapper { border-radius: 14px; box-shadow: 0 12px 35px rgba(14,45,54,.2); }
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.popup { min-width: 190px; }
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.popup-code { font-size: .7rem; color: var(--river); font-weight: 850; text-transform: uppercase; letter-spacing: .08em; }
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@@ -213,6 +221,15 @@
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<div><h2 style="margin:0;font-size:1rem">Flood risk outlook <span style="color:var(--muted);font-weight:650;font-size:.72rem">· experimental</span></h2>
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<p id="forecast-status" class="subtitle">Model probability of reaching warning / danger levels within 6, 12 and 24 hours</p></div>
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</div>
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<div class="p1-outlook" id="p1-outlook" style="display:none">
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<div class="p1-outlook-head">
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<div><strong>Chiang Mai city flood outlook · P.1 Nawarat Bridge</strong>
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<div class="p1-peak" id="p1-peak"></div></div>
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<button type="button" class="zones-button" id="zones-toggle">Show flood zones on map</button>
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</div>
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<div class="stage-strip" id="p1-stages"></div>
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<div class="p1-peak" style="margin-top:7px">Chance the river reaches each official inundation stage within 24 h — city flooding begins at stage 1 (3.70 m); each stage floods additional districts.</div>
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</div>
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<div class="forecast-grid" id="forecast-grid"></div>
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</section>
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@@ -589,6 +606,53 @@
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return '#1e8b60';
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}
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// Official Chiang Mai inundation map (keyed to P.1), georeferenced approximately.
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// Tune bounds if the river course in the scan drifts from the basemap.
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const FLOOD_ZONE_IMAGE = '/static/flood-zones-p1.jpg';
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const FLOOD_ZONE_BOUNDS = [[18.680, 98.925], [18.855, 99.105]];
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let zoneOverlay = null;
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function toggleFloodZones() {
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if (!state.map) return;
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const button = $('zones-toggle');
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if (zoneOverlay) {
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state.map.removeLayer(zoneOverlay);
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zoneOverlay = null;
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if (button) button.textContent = 'Show flood zones on map';
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} else {
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zoneOverlay = L.imageOverlay(FLOOD_ZONE_IMAGE, FLOOD_ZONE_BOUNDS, { opacity: .62, interactive: false }).addTo(state.map);
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state.map.flyToBounds(FLOOD_ZONE_BOUNDS, { maxZoom: 13, duration: .8 });
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if (button) button.textContent = 'Hide flood zones';
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document.getElementById('station-map').scrollIntoView({ behavior: 'smooth', block: 'nearest' });
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}
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}
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function stageColor(p) {
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if (p >= .5) return '#cc4b37';
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if (p >= .2) return '#d99018';
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if (p >= .05) return '#0a91b9';
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return '#1e8b60';
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}
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function renderP1Outlook(rows) {
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const card = $('p1-outlook');
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const p1rows = rows.filter((r) => r.station_code === 'P.1' && Array.isArray(r.stages));
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if (!p1rows.length) { card.style.display = 'none'; return; }
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const row = p1rows.reduce((best, r) => r.horizon_hours > best.horizon_hours ? r : best);
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$('p1-peak').textContent = `Now ${Number(row.current_level).toFixed(2)} m · predicted peak next ${row.horizon_hours} h: ${Number(row.predicted_max_level).toFixed(2)} m`;
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const strip = $('p1-stages');
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strip.replaceChildren();
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row.stages.forEach((s) => {
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const chip = document.createElement('div');
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chip.className = 'stage-chip';
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chip.style.background = stageColor(s.p_exceed);
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chip.title = `Stage ${s.stage}: river at ${s.level.toFixed(2)} m — ${Math.round(s.p_exceed * 100)}% within ${row.horizon_hours} h`;
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chip.innerHTML = `${Math.round(s.p_exceed * 100)}%<small>S${s.stage} · ${s.level.toFixed(2)} m</small>`;
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strip.appendChild(chip);
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});
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card.style.display = 'block';
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}
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async function loadForecasts() {
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const card = $('forecast-card');
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try {
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@@ -601,6 +665,7 @@
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if (!byStation.has(row.station_code)) byStation.set(row.station_code, []);
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byStation.get(row.station_code).push(row);
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});
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renderP1Outlook(rows);
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const grid = $('forecast-grid');
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grid.replaceChildren();
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const stations = [...byStation.entries()].map(([code, list]) => ({
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@@ -634,6 +699,7 @@
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}
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$('refresh-button').addEventListener('click', loadDashboard);
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$('zones-toggle').addEventListener('click', toggleFloodZones);
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$('history-range').addEventListener('change', () => { if (state.selectedStation) loadHistory(state.selectedStation); });
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loadDashboard();
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window.setInterval(loadDashboard, 5 * 60 * 1000);
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