grabowski
a610abcdb8
CMU Water Center marked the October 2024 high-water line at ~4,900 places in Chiang Mai and Lamphun (depth above the road while P.1 stood at 5.30 m). - src/static/flood-marks-2024.json (position and depth, 110 KB), built by scripts/build_flood_marks.py from CMU's GeoJSON. - "Flood marks 2024" layer: canvas dots from zoom 12 in CMU's five depth classes, legend row, CMU credit; turning it on below zoom 12 flies to the city. A mark's popup gives the depth and its flood zone. - Zone and marker popups name the nearest mark within 250 m (loaded with the zones). - No depth estimate for other P.1 levels: the marks do not scale one-to-one with P.1 (marks in the 5.30 m zone stood a median 65 cm deep in 2024). - The layer bar wraps on narrow maps; popups pan clear of it. - Docs: DATA_SOURCES 5.1, DASHBOARD_REDESIGN, README.
205 lines
12 KiB
Markdown
205 lines
12 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, serves a bilingual dashboard and an open REST
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API, forecasts the flood risk per gauge 6/12/24 hours ahead, and pushes alerts when
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a gauge crosses its warning or danger level.
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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://git.b4l.co.th/B4L/Northern-Thailand-Ping-River-Monitor/actions)
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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 state of the large RID dams, Mae Ngat
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above all. Every source and 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 %. RID's report often arrives
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hours late in the evening, so late readings are always stored and holes are
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re-fetched. `GET /api/stats` reports the current coverage.
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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.** One screen: Chiang Mai's flood verdict, the gauges with their status
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and trend, a self-hosted map of the basin with rain, the city's flood zones and
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the high-water marks of 2024, and a panel per station with its history since
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2018, the forecast and how accurate it has been. ThaiWater's basin stations, a
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replay of the 2024 flood, Thai/English, dark/light.
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- **Notifies.** Public push alerts over a self-hosted [ntfy](https://ntfy.sh): one
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message when a gauge crosses its warning or danger level, one all-clear on the
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way down, an opt-in early-warning topic from the model, nothing in between.
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Subscribe from the free app, no account. A Matrix room can get the same
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messages for a team. [docs/NOTIFICATIONS.md](docs/NOTIFICATIONS.md).
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## How it runs
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```text
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RID gauges · ThaiWater/HII · RID dams · Open-Meteo
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│ hourly
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▼
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┌──────────────────────────────────────┐ ┌───────────────────────────┐
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│ ping-collector (Rust, one process) │ │ monthly retrain (Python) │
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│ collect → forecast → notify │◄───────│ scikit-learn, exports │
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│ website + API on :8000 │ models │ models/flood_*.json.gz │
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└──────┬──────────────┬────────────────┘ └─────────────┬─────────────┘
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│ │ │
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▼ ▼ │
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ntfy topics, PostgreSQL ◄──────────── history ────────────┘
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Matrix room (gauges, rain, dams, forecasts, alert state)
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```
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- **The service** is one Rust binary, `ping-collector` ([rust/README.md](rust/README.md)),
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run by systemd. It polls RID at :05 past each hour and retries every 10 minutes
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while the report is behind, fetches HII, the dams and the rain once an hour,
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forecasts after every cycle that brought new readings, sends the notifications,
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and serves the dashboard and API, in under 100 MB of memory with every model
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loaded.
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- **The models** are trained in Python (`src/ml`, scikit-learn
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HistGradientBoosting) by a monthly timer and exported for the Rust forecaster,
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which reproduces Python's features bit for bit and reloads new models without
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a restart.
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- Until October 2026 all of this was a Python/FastAPI app; how and why it moved:
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[docs/RUST_REWRITE.md](docs/RUST_REWRITE.md).
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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/forecast/skill?station_code=P.1` | Issued forecasts vs what happened, per deployed model version |
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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/notifications` | ntfy server and topic names for the subscribe panel |
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| `GET /api/stats` | Row counts per source, date range, coverage |
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| `GET /health` | Database and data freshness (RID, ThaiWater as backup), the RID API, memory |
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| `GET /scraping/status` | Collection runs, the last forecast run, service version |
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Interactive reference with schemas: [water.buildfor.life/docs](https://water.buildfor.life/docs)
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(also `/redoc`, `/openapi.json`). Responses are cached briefly server-side; poll no
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faster than once a minute — the data changes hourly. Station editing
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(`POST/PUT/DELETE /stations`), `/config` and `POST /scrape/trigger` need the
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server's `ADMIN_API_KEY` in an `X-API-Key` header.
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## Run it locally
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Needs Rust >= 1.88 ([rustup](https://rustup.rs); on Linux also `pkg-config` and
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`libssl-dev`) and PostgreSQL. Python 3.11 with [uv](https://docs.astral.sh/uv/)
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only for training models.
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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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docker run -d --name ping-pg -e POSTGRES_PASSWORD=ping -e POSTGRES_DB=ping -p 55432:5432 postgres:16
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cp .env.example .env
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# POSTGRES_CONNECTION_STRING=postgresql://postgres:ping@localhost:55432/ping
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# WEB_LISTEN=127.0.0.1:8000
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cargo build --release --manifest-path rust/Cargo.toml
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rust/target/release/ping-collector check-db # create the tables
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rust/target/release/ping-collector fill-gaps 30 # the last 30 days from RID
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rust/target/release/ping-collector run # collect, forecast, serve http://127.0.0.1:8000
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```
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Notifications stay off until `NTFY_SERVER` or `MATRIX_*` is set. Without trained
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models the forecast is the persistence heuristic, marked `source: "heuristic"`.
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Training needs years of history (the server's database, or `FLOOD_ML_DB_URL`) and
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the Open-Meteo archive:
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```bash
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uv sync --python 3.11
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uv run python scripts/train_flood_model.py --stations all # writes models/flood_*
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uv run python scripts/evaluate_variants.py --stations P.1 # the rolling-origin harness
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```
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`make help` lists the rest (`make check` runs every CI gate locally).
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## Deployment
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Production is one systemd unit on a small VPS behind Cloudflare; the app directory
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`/opt/thailand-water-monitor` is a git checkout. To update:
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```bash
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cd /opt/thailand-water-monitor && git pull
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cargo build --release --locked --manifest-path rust/Cargo.toml
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sudo bash scripts/install.sh
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```
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`scripts/install.sh` (idempotent, also for a fresh host) installs the binary and
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`ping-collector.service`, syncs the Python environment for the monthly
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`water-monitor-retrain.timer`, runs a dry run of every source and a forecast check
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against the Python reference, restarts the service and waits for the website. The
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retrain trains into `models/.staging`, refuses to promote anything that is not a
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rain-enabled (`hgb-v3+`) set covering the expected stations, and renames the
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bundles into place; the service picks them up on its next forecast. Details and
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the operations runbook: [docs/FLOOD_FORECASTING.md](docs/FLOOD_FORECASTING.md)
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sections 6–8. Configuration: [.env.example](.env.example).
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## Repository layout
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```
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rust/collector/ ping-collector: collection, forecast run, ntfy/Matrix, website + API (src/web)
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rust/forecast/ forecast inference from the exported models; parity tests against Python
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src/static/ the dashboard (single file, EN/TH) and its map data
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src/data/ the bundled station list
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src/ml/ features, training, evaluation harness, export; predict.py is the reference
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src/*.py HII and RID-dam clients/stores for the archive backfills
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scripts/ install.sh, retrain.sh, train/evaluate/backfill tools, systemd units
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tests/ pytest (synthetic data; database tests need TEST_DATABASE_URL)
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docs/ model write-up, data sources, notifications, Matrix, rewrite history
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models/ trained bundles (gitignored) and evaluation results
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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/NOTIFICATIONS.md](docs/NOTIFICATIONS.md) — public push alerts: topics, semantics, ntfy deployment
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- [docs/MATRIX_QUICK_START.md](docs/MATRIX_QUICK_START.md) — the same alerts in a Matrix room
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- [rust/README.md](rust/README.md) — the service: commands, configuration, develop, deploy
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- [docs/RUST_REWRITE.md](docs/RUST_REWRITE.md) — why and how it moved from Python to Rust
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- [docs/DASHBOARD_REDESIGN.md](docs/DASHBOARD_REDESIGN.md) — the dashboard: layout, rules, how it replaced the old page
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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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## Contributing
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`make format` before committing, `make check` must stay green; 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 and reservoir reports;
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Hydro-Informatics Institute (HII) / ThaiWater open API; Open-Meteo; OpenStreetMap
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contributors for the river geometry; the CMU Water Center (Chiang Mai University)
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for the flood hazard map of the 2024 flood that the P.1 stages and the city's flood
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zones come from. All instruments are theirs; we aggregate, store, fill
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gaps and forecast.
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## License
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MIT — see [LICENSE](LICENSE).
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