grabowski
4358d52d55
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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).
35 lines
586 B
Plaintext
35 lines
586 B
Plaintext
# Core dependencies
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requests==2.31.0
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schedule==1.2.0
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pandas==2.0.3
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numpy>=1.24,<2 # pandas 2.0.3 wheels are ABI-incompatible with numpy 2.x
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# Flood forecasting (ML)
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scikit-learn==1.9.0
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# Web API framework
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fastapi==0.104.1
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uvicorn[standard]==0.24.0
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pydantic==2.5.0
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# Database adapters
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sqlalchemy==2.0.23
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influxdb==5.3.1
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pymysql==1.1.0
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psycopg2-binary==2.9.9
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# Monitoring and metrics
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psutil==5.9.6
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# Development dependencies (optional)
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pytest==7.4.3
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pytest-cov==4.1.0
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black==23.11.0
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flake8==6.1.0
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mypy==1.7.1
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pre-commit==3.5.0
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# Documentation
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sphinx==7.2.6
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sphinx-rtd-theme==1.3.0
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