| --- |
| 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%"> |
| |
