---
license: mit
pretty_name: FloodGate v1
tags: [rl-environment, openenv, flood-control, water-resources, operations-research, benchmark, beam]
configs:
- config_name: tasks
  data_files: tasks/flood-v1-sample/tasks.jsonl
---

# FloodGate v1: Cumberland River flood control on the real 2025 floods

Tasks, data and every model run for [FloodGate](https://huggingface.co/spaces/bpHigh/FloodGate). The task: run the
U.S. Army Corps' four flood-control dams above Nashville through the February and April 2025 floods. The code is MIT.
All data are U.S. federal works in the public domain (USACE, USGS, NOAA NWS); NWS products come via the Iowa
Environmental Mesonet archive.

| path | what |
|---|---|
| `tasks/flood-v1-sample/tasks.jsonl` | 24 tasks around 13-16 February and 1-4 April 2025, in three tiers: the record, a storm 1.5 times bigger, lakes starting half full. Each holds the dams, gauges, inflows and local runoff per 6-hour step, the travel times, and the reference (LP optimum and its plan, baselines, the Corps' real releases) |
| `data/basin.json` | the dams' official pool levels and release limits, the gauges' NWS flood categories and ratings, the travel times |
| `data/record.csv.gz` | the curated record, Jan-Jun 2025, 6-hour steps: dam releases, storage and pool elevations, gauge flows |
| `data/forecasts.csv.gz` | the 9,540 river-forecast values the NWS Ohio River Forecast Center issued for Burkesville, Celina and Nashville in Jan-Jun 2025 (stage, and flow through the NWS rating) |
| `data/SOURCE.md` | every rule and number, as real, derived or assumed |
| `runs/<run>/index.json` | every episode: reward, grade, the submitted plan (plan mode) or the commits (live mode), tokens, time |
| `runs/<run>/board.md` | the run's board |
| `runs/live-baselines-24h.md` | live mode's policies without a model: scenario-optimal 1.000, a perfect solver on the mean forecast 0.552, one scenario 0.444, hold 0.215 |

## Results (live mode, 24 tasks)

| model | mean reward |
|---|---:|
| a perfect solver planning on the mean forecast (no model) | 0.552 |
| DeepSeek-V4.1-Flash | 0.364 |
| Beam-1.0-501B-A23B (default effort) | 0.236 |
| Beam-1.0-501B-A23B (reasoning_effort high) | 0.237 |
| hold every release | 0.215 |

## Grading

- **Plan mode:** reward = 0.2 + 0.8·exp(−ρ/0.25), where ρ = (cost − optimum) / (lazy − optimum). Plans that break a
  rule score below 0.2.
- **Live mode:** ρ = Σ regret / Σ (the lazy operator's regret). Each decision's regret is its expected extra cost over
  the scenario-optimal decision, from a two-stage stochastic LP over the 8 scenarios.
- **Both are deterministic, with no LLM judge.**

<img src="assets/replay_beam_live.webp" alt="FloodGate 3D replay: Beam's live-mode plan with Nashville in major flood" width="100%">