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/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