grabowski
2e19974fad
The README was the original template: P.1 "in Nakhon Sawan", P.103 "in Bangkok", VictoriaMetrics as the recommended database, Docker/Grafana sections, github.com/your-username support links. Rewritten around what runs: sources, gap fill, the forecast and its 13 h result, the live API table, systemd deployment with the retrain timer, repository layout, real docs links, data-source credits. Other DB adapters are mentioned as supported-but-not-production. CLAUDE.md replaces the untracked Ruflo boilerplate with project rules: Python 3.11, format/test gates, Bangkok timestamps, harness-first model changes judged on lead, the RainUnavailableError guard, no git add -A.
151 lines
8.5 KiB
Markdown
151 lines
8.5 KiB
Markdown
# Northern Thailand Ping River Monitor
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Live water levels, discharge, rainfall and machine-learning flood forecasts for the
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Ping River basin around Chiang Mai. Collects hourly gauge data from public sources,
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keeps the full history in PostgreSQL, and serves a bilingual dashboard, an open REST
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API, and 6/12/24-hour flood-risk forecasts per gauge.
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**Live: [water.buildfor.life](https://water.buildfor.life/)** · API reference at
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[/docs](https://water.buildfor.life/docs) · built by [buildfor.life](https://buildfor.life)
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after the [October 2024 flood](https://buildfor.life/blog/chiang-mai-flood-2024/) —
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background in [Teaching a Model to See the Ping River Rise 13 Hours Early](https://buildfor.life/blog/ping-river-monitor/).
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[](https://git.b4l.co.th/B4L/Northern-Thailand-Ping-River-Monitor/actions)
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[](https://git.b4l.co.th/B4L/Northern-Thailand-Ping-River-Monitor/actions)
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[](https://git.b4l.co.th/B4L/Northern-Thailand-Ping-River-Monitor/actions)
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[](https://python.org)
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[](LICENSE)
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## What it does
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- **Collects** hourly water level and discharge from 16 Royal Irrigation Department
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(RID) telemetry gauges, Chiang Dao to the southern basin, since 2018-08; hourly
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rainfall and water level from 400+ ThaiWater/HII stations; Open-Meteo catchment
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rainfall (archive + 48 h forecast); daily Mae Ngat reservoir state. Every source and
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its quirks: [docs/DATA_SOURCES.md](docs/DATA_SOURCES.md).
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- **Fills gaps.** The raw RID grid had readings for ~56 % of hours; a full-history
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re-fetch plus HII cross-fill brought it to ~93 %. `GET /api/stats` reports the
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current figure.
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- **Forecasts.** Per gauge and horizon, a gradient-boosted model predicts the rise
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within 6/12/24 h and the probability of crossing the station's warning and danger
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levels. Trained on the monitor's own history plus catchment rain; evaluated
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rolling-origin, event by event. On the October 2024 record flood, trained only on
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data through August 2024, the first alert came **13 hours before** P.1 crossed
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3.70 m. Everything about the model, including what did not work:
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[docs/FLOOD_FORECASTING.md](docs/FLOOD_FORECASTING.md).
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- **Shows it.** A Leaflet map with the river drawn as OSM geometry and styled by live
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discharge, rain gauges, the Chiang Mai inundation zones, per-station history, the
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forecast card, a replay of the 2024 flood, English/Thai, light/dark.
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- **Alerts** (optional) to a Matrix room when a gauge crosses its thresholds.
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## Quick start
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Python **3.11** (3.13 breaks the pinned `psycopg2-binary`), PostgreSQL for anything
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beyond a quick look, [uv](https://docs.astral.sh/uv/).
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```bash
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git clone https://git.b4l.co.th/B4L/Northern-Thailand-Ping-River-Monitor.git
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cd Northern-Thailand-Ping-River-Monitor
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uv sync --python 3.11
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cp .env.example .env # DB_TYPE, POSTGRES_CONNECTION_STRING, optional MATRIX_*
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uv run python run.py --web-api # dashboard + API on http://localhost:8000
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```
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`DB_TYPE=sqlite` works for the dashboard and API; the forecasting path expects the
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PostgreSQL history.
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```bash
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uv run python run.py --status # collector status
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uv run python run.py --test # one collection cycle
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uv run python run.py --fill-gaps 7 # re-fetch the last 7 days from RID
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uv run python run.py --collect-hii # one ThaiWater/HII collection cycle
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uv run python run.py --alert-check # evaluate thresholds, notify Matrix
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uv run python scripts/train_flood_model.py --stations all # retrain (~12 min)
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make test # pytest, synthetic data, no network
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make format # black + isort (the CI contract)
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```
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## API
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Read-only, no key, JSON. Base URL `https://water.buildfor.life`; timestamps are
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Asia/Bangkok wall-clock without an offset suffix.
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| Endpoint | Returns |
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| --- | --- |
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| `GET /stations` | The 16 RID gauges: code, Thai/English names, coordinates |
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| `GET /measurements/latest?limit=N` | Newest reading per station |
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| `GET /measurements/history/{code}?hours=N` | Hourly history; or `?start=YYYY-MM-DD&end=YYYY-MM-DD`; `limit` ≤ 100000 |
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| `GET /forecast` | Current flood-risk forecast, every station × horizon, with thresholds and P.1 inundation-stage probabilities |
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| `GET /api/forecast/history/{code}?hours=N&horizon=24` | Forecasts as issued, for auditing lead time after the fact |
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| `GET /api/hii/rainfall/latest`, `/api/hii/waterlevel/latest` | Latest ThaiWater/HII gauge readings |
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| `GET /api/hii/rainfall/catchment?days=N` | HII gauge catchment-mean rain next to the Open-Meteo series the model uses |
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| `GET /api/stats` | Row counts per source, date range, coverage |
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| `GET /health` | DB / upstream / memory checks |
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Interactive reference with schemas: [water.buildfor.life/docs](https://water.buildfor.life/docs).
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Responses are cached briefly server-side; poll no faster than once a minute — the data
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changes hourly.
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## Deployment
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Production is a systemd unit on a small VPS behind Cloudflare, updated by `git pull`.
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`scripts/install.sh` (run as root from a checkout) creates the `water-monitor` user,
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deploys to `/opt/thailand-water-monitor`, runs `uv sync` into `.venv`, installs
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`water-monitor.service` and the monthly `water-monitor-retrain.timer`.
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```bash
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sudo bash scripts/install.sh
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sudo systemctl start water-monitor.service
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systemctl list-timers water-monitor-retrain.timer
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```
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The retrain timer runs `scripts/retrain.sh`, which trains into `models/.staging`,
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refuses to promote anything that is not a rain-enabled (`hgb-v3+`) set covering the
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expected stations, and renames the bundles into place. Details and the operations
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runbook: [docs/FLOOD_FORECASTING.md](docs/FLOOD_FORECASTING.md) sections 6–8.
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## Repository layout
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```
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src/ collector, API (web_api.py), dashboard (static/dashboard.html)
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src/ml/ features, training, evaluation harness, prediction, rain/dam/HII loaders
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scripts/ train_flood_model.py, retrain.sh, evaluate_variants.py, install.sh, dev_proxy.py
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tests/ pytest suite (synthetic data; no DB or network)
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docs/ FLOOD_FORECASTING.md, DATA_SOURCES.md, deployment and station guides
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models/ trained bundles + metrics.json (gitignored) and evaluation results (tracked)
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.gitea/workflows/ ci (format/lint/tests), security (pip-audit/bandit), docs (link + OpenAPI checks)
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```
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## Documentation
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- [docs/FLOOD_FORECASTING.md](docs/FLOOD_FORECASTING.md) — the model: data, features, evaluation, measured performance, negatives, deployment, retraining
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- [docs/DATA_SOURCES.md](docs/DATA_SOURCES.md) — every ingested and candidate source, endpoints, quirks
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- [docs/STATION_MANAGEMENT_GUIDE.md](docs/STATION_MANAGEMENT_GUIDE.md) — adding/editing gauges
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- [docs/DATABASE_DEPLOYMENT_GUIDE.md](docs/DATABASE_DEPLOYMENT_GUIDE.md), [POSTGRESQL_SETUP.md](POSTGRESQL_SETUP.md) — database setup
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- [docs/MATRIX_QUICK_START.md](docs/MATRIX_QUICK_START.md) — alert delivery
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- [docs/GAP_FILLING_GUIDE.md](docs/GAP_FILLING_GUIDE.md) — data integrity tooling
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- [docs/references/NOTABLE_DOCUMENTS.md](docs/references/NOTABLE_DOCUMENTS.md) — official Thai government resources
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- Public overview: [buildfor.life/docs/tooling/ping-river-monitor](https://buildfor.life/docs/tooling/ping-river-monitor/)
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Other database backends (VictoriaMetrics, InfluxDB, MySQL, SQLite) and the Grafana
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dashboards under `grafana/` are supported by the adapters but not what production
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runs; see [docs/VICTORIAMETRICS_SETUP.md](docs/VICTORIAMETRICS_SETUP.md) if you want them.
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## Contributing
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`make format` before committing (black 88 columns, isort black profile — the CI gate),
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`make test` must stay green, tests use synthetic data only. See
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[CONTRIBUTING.md](CONTRIBUTING.md). Issues and merge requests on
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[git.b4l.co.th](https://git.b4l.co.th/B4L/Northern-Thailand-Ping-River-Monitor).
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## Data sources and thanks
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Royal Irrigation Department (RID) gauge telemetry; Hydro-Informatics Institute (HII) /
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ThaiWater open API; Open-Meteo; OpenStreetMap contributors for the river geometry;
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Chiang Mai Municipality for the inundation map the P.1 stages are keyed to. All
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instruments are theirs; we aggregate, store, fill gaps and forecast.
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## License
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MIT — see [LICENSE](LICENSE).
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