scikit-learn 1.9.0 supports only Python >=3.11, so uv could not resolve
the >=3.9 range. The deployment runs 3.11. Also pins numpy back to
1.26.4 in the lockfile (pandas 2.0.3 ABI).
Add src/ml/ package predicting, per station and per 6/12/24 h horizon,
the probability of exceeding warning (3.0 m) and danger (4.5 m) levels
plus expected peak level, trained on the 592k-row PostgreSQL history:
- features.py: hourly grid with coverage gating and no future leakage;
upstream stations enter at empirically measured travel-time lags
(P.20 +17h ... P.103 +1h vs P.1); hour-of-day deliberately excluded
(it encodes the scrape schedule, not hydrology)
- train.py: HistGradientBoosting regression + warn/danger classifier
heads per station x horizon, >=30-positives gate with calibrated
sigmoid-on-regression fallback, strict temporal splits, per-event
lead-time evaluation; guards against sklearn 1.9.0 crash on
degenerate feature columns
- predict.py: bundle loading with feature-name checks, heuristic
fallback tier, get_latest_forecasts() for the API; raises when no
models are trained so the endpoint 503s instead of serving
persistence output as forecasts
- data.py: Postgres-first loader (FLOOD_ML_DB_URL override), HTTP API
fallback (flagged: that path backfills synthetic discharge), csv.gz
cache
- /forecast endpoint (15-min TTL cache) + dashboard flood-risk panel
(hidden until models exist)
- docs/FLOOD_FORECASTING.md: full system doc with measured deployment
numbers (~335 MB RSS, CPU negligible, ~6 min full retrain) and
retraining policy
Validation: out-of-sample backtest of the record 2024 flood season
(train <= Aug 2024) alerted 24-48 h ahead of the Oct 5 peak; 2025-26
test split: P.1 6h PR-AUC 0.974, recall 98.3% at 1% false-alarm rate.
Also: fix P.81 station coordinates (was Ban Pong/Ratchaburi, 493 km
out of basin; now 18.6936 N 99.0819 E per RID station page), pin
scikit-learn==1.9.0 and numpy<2, gitignore model artifacts (~100 MB,
train on the server via scripts/train_flood_model.py).
- Replaced deprecated tool.uv.dev-dependencies with dependency-groups.dev
- Follows new uv standard for dependency group declaration
Co-Authored-By: Claude <noreply@anthropic.com>
- **Migration to uv package manager**: Replace pip/requirements with modern pyproject.toml
- Add pyproject.toml with complete dependency management
- Update all scripts and Makefile to use uv commands
- Maintain backward compatibility with existing workflows
- **PostgreSQL integration and migration tools**:
- Enhanced config.py with automatic password URL encoding
- Complete PostgreSQL setup scripts and documentation
- High-performance SQLite to PostgreSQL migration tool (91x speed improvement)
- Support for both connection strings and individual components
- **Executable distribution system**:
- PyInstaller integration for standalone .exe creation
- Automated build scripts with batch file generation
- Complete packaging system for end-user distribution
- **Enhanced data management**:
- Fix --fill-gaps command with proper method implementation
- Add gap detection and historical data backfill capabilities
- Implement data update functionality for existing records
- Add comprehensive database adapter methods
- **Developer experience improvements**:
- Password encoding tools for special characters
- Interactive setup wizards for PostgreSQL configuration
- Comprehensive documentation and migration guides
- Automated testing and validation tools
🤖 Generated with [Claude Code](https://claude.ai/code)
Co-Authored-By: Claude <noreply@anthropic.com>
grabowski
