Miami-Dade, FloridaCoastGuard AI
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Live now
Water
Roads cut
Cut off
Field app
Precomputing the flood model…

Running the connectivity flood fill across all 193 timesteps up front, so scrubbing the timeline never has to re-simulate.

Timeline position
Day 1 · 04:00
Water level here
0.07 m
Day 1 · 00:00▲ storm peak · h26Day 3 · 00:00

Legend

  • Flooded (hatched, darker = deeper)
  • Road open
  • Road impassable (dashed)
  • Selected route (solid)
  • Other risk mode (dashed)
  • Cut off

Conditions now

Normal conditions
Water level
0.07 m
tide 0.05 + surge 0.00
Rainfall
1.2 mm/h
1 cm accumulated
Roads impassable
31 / 4659
1% of the network
Landmarks cut off
0
all reachable

0.8% of the town’s land area is under water, and wind is gusting to 14 km/h.

6339 cells sit below the water level but have no path to the sea, so the model correctly leaves them dry. A plain elevation threshold would have flooded them.

Safe route

Risk tolerance

Excluding only roads that are under water at this moment.

Distance
4.11 km
Drive time
6 min
Clear

How this works

  1. 1 · Flood model. Water level is tide plus storm surge plus accumulated rainfall draining away on a 5-hour time constant. A cell floods only if it is below that level and connected to the open sea, found by breadth-first search from the coast.
  2. 2 · Safe routing. Each road segment is sampled every 15 m against the flood grid; anything standing in more than 30 cm of water is dropped from the graph. A* with a haversine heuristic then finds the shortest path through what is left.

The field app runs this exact engine — same numbers, same hour.