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).

src/ml/features.py

@@ -20,10 +20,48 @@ logger = logging.getLogger(__name__)
 
 # Per-station (warning, danger) level thresholds in meters. "*" is the default
 # applied to any station without an explicit override.
+# Per-station (warning, danger) levels in metres on each gauge's own datum.
+# Calibrated 2026-08-10 from the DB's discharge_percent (RID % of channel
+# capacity): warning = median level at 75-85% capacity, danger = median level
+# at 95-105%. P.1 instead uses the official Chiang Mai inundation map keyed to
+# the P.1 gauge: city flooding begins at 3.70 m (stage 1) and reaches most
+# districts by 4.20 m (stage 5) — see P1_FLOOD_STAGES.
 THRESHOLDS: Dict[str, Tuple[float, float]] = {
     "*": (3.0, 4.5),
+    "P.1": (3.70, 4.20),
+    "P.103": (5.95, 6.75),
+    "P.20": (2.35, 2.80),
+    "P.21": (3.20, 3.60),
+    "P.4A": (3.40, 3.90),
+    "P.5": (4.55, 4.95),
+    "P.67": (2.45, 2.90),
+    "P.75": (2.75, 3.50),
+    "P.76": (5.35, 5.45),
+    "P.77": (2.85, 3.35),
+    "P.81": (5.15, 6.30),
+    "P.82": (3.40, 3.80),
+    "P.84": (3.45, 3.90),
+    "P.85": (2.90, 3.35),
+    "P.87": (3.75, 4.05),
+    "P.92": (2.95, 3.60),
 }
 
+# Official Chiang Mai flood-onset stages at the P.1 gauge (Nawarat Bridge),
+# from the municipal inundation map (พื้นที่ท่วมตัวเมืองเชียงใหม่, events of
+# 2548/2554/2565 BE): gauge level in m, RID discharge in m³/s. Each stage
+# floods progressively more city zones.
+P1_FLOOD_STAGES: List[Dict[str, float]] = [
+    {"stage": 1, "level": 3.70, "discharge_cms": 405},
+    {"stage": 2, "level": 3.90, "discharge_cms": 438},
+    {"stage": 3, "level": 4.00, "discharge_cms": 458},
+    {"stage": 4, "level": 4.10, "discharge_cms": 478},
+    {"stage": 5, "level": 4.20, "discharge_cms": 493},
+    {"stage": 6, "level": 4.30, "discharge_cms": 508},
+    {"stage": 7, "level": 4.60, "discharge_cms": 558},
+]
+
+FLOOD_STAGES: Dict[str, List[Dict[str, float]]] = {"P.1": P1_FLOOD_STAGES}
+
 MONSOON_MONTHS = {6, 7, 8, 9, 10}
 FFILL_LIMIT_H = 3
 MIN_WINDOW_COVERAGE = 0.5

src/ml/predict.py

@@ -154,22 +154,36 @@ def _model_forecast(
         p_warning = _clip_probability(p_warning)
         p_danger = min(_clip_probability(p_danger), p_warning)
 
-        results.append(
-            {
-                "station_code": station_code,
-                "horizon_hours": horizon_h,
-                "p_warning": p_warning,
-                "p_danger": p_danger,
-                "predicted_max_level": predicted_max,
-                "current_level": current_level,
-                "as_of": as_of.isoformat(),
-                "model_version": bundle["model_version"],
-                "trained_at": bundle["trained_at"],
-                "source": "model",
-                "threshold_warning": warn_thr,
-                "threshold_danger": danger_thr,
-            }
-        )
+        row = {
+            "station_code": station_code,
+            "horizon_hours": horizon_h,
+            "p_warning": p_warning,
+            "p_danger": p_danger,
+            "predicted_max_level": predicted_max,
+            "current_level": current_level,
+            "as_of": as_of.isoformat(),
+            "model_version": bundle["model_version"],
+            "trained_at": bundle["trained_at"],
+            "source": "model",
+            "threshold_warning": warn_thr,
+            "threshold_danger": danger_thr,
+        }
+        stages = features.FLOOD_STAGES.get(station_code)
+        if stages:
+            # Exceedance probability per official inundation stage, from the
+            # regression head and its validation-residual sigma. These are
+            # threshold-agnostic, so no retraining is needed to serve them.
+            row["stages"] = [
+                {
+                    "stage": s["stage"],
+                    "level": s["level"],
+                    "p_exceed": _clip_probability(
+                        _sigmoid_probability(predicted_max, s["level"], sigma_h)
+                    ),
+                }
+                for s in stages
+            ]
+        results.append(row)
     return results