11 Commits

Include renames

Author	SHA1	Message	Date
grabowski	27fa292e09	docs: September 2025 flood render - deployed config, 24 h warning, peak within 7 cm ... Hour-by-hour chart of the 25-28 Sep 2025 event as forecast by the exact deployed configuration (trained through 2024, event unseen): first alert 26 Sep 18:00, flooding began 27 Sep 18:00 (24 h lead), predicted peak 4.00 m vs actual 3.93 m. The near-miss 3.51 m crest on 26 Sep correctly never alerted.	2026-08-10 18:35:55 +07:00
grabowski	f0183faa62	feat: discharge-driven river animation speed, replay stat tiles, hourly doc render ... - river dash animation now flows at a speed continuously derived from each segment's discharge (period 260/(Q+45) s, clamped 0.5-5.5 s) via inline per-path animation-duration, so it updates live and per-frame during the replay (CSS speed classes removed - setStyle cannot change classes) - the replay drives the Combined discharge and Strongest flow stat tiles each frame (marked '2024 replay'), restored on finish - docs: hour-by-hour detection detail render (22-28 Sep 2024) showing the model alert at 24 Sep 01:00, flooding at 25 Sep 01:00, and the 24 h warning between them; embedded with commentary - Matrix alerts now link to https://water.buildfor.life/ (override via ALERT_DASHBOARD_URL), replacing the Grafana public dashboard link	2026-08-10 18:29:01 +07:00
grabowski	6112c681a7	docs: render of actual vs predicted through the 2024 flood; adaptive replay speed ... docs/img/backtest-2024-p1.png: observed P.1 level vs the 24h-ahead predicted peak issued at each hour by a model trained only on pre-flood data, with the warning-probability panel below (first alert 24 Sep 01:00, 24 h before flooding began). Embedded in FLOOD_FORECASTING.md's headline-validation section with an honest reading, including the ~0.4 m peak under-prediction. Replay pacing is now adaptive: 4 h/frame through quiet days, 2 h when risk is elevated, 1 h (hour-by-hour) while the model is alerting or the river is near/above flood stage - so viewers can watch the detection sequence unfold.	2026-08-10 18:10:48 +07:00
grabowski	e4d5d274f0	feat: per-station flood thresholds and Chiang Mai inundation stages for P.1 ... Replace the network-wide (3.0, 4.5) m thresholds with per-station values calibrated from the DB's discharge_percent (RID % of channel capacity): warning = median level at 75-85% capacity, danger = median at 95-105%. Fixes P.103 over-alerting (bank-full ~6.75 m, not 4.5) and P.67 under-alerting (overflow ~2.9 m). Requires a retrain to take effect in the classifier heads. P.1 uses the official Chiang Mai municipal inundation map instead: warning 3.70 m (stage 1, city flooding begins), danger 4.20 m (stage 5), with the full 7-stage table (3.70-4.60 m + discharge) in features.P1_FLOOD_STAGES. Forecast rows for P.1 now include per-stage exceedance probabilities computed from the regression head + calibration sigma - available immediately without retraining. Dashboard: "Chiang Mai city flood outlook" block above the forecast grid (predicted peak + 7 stage-probability chips) and a toggleable georeferenced overlay of the official flood-zone map (static/flood-zones-p1.jpg, bounds tunable in FLOOD_ZONE_BOUNDS).	2026-08-10 15:35:00 +07:00
grabowski	4358d52d55	feat: ML flood-event forecasting from 8 years of gauge history ... 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).	2026-08-10 12:49:47 +07:00
grabowskiandClaude	ca730e484b	Add comprehensive Matrix alerting system with Grafana integration ... - Implement custom Python alerting system (src/alerting.py) with water level monitoring, data freshness checks, and Matrix notifications - Add complete Grafana Matrix alerting setup guide (docs/GRAFANA_MATRIX_SETUP.md) with webhook configuration, alert rules, and notification policies - Create Matrix quick start guide (docs/MATRIX_QUICK_START.md) for rapid deployment - Integrate alerting commands into main application (--alert-check, --alert-test) - Add Matrix configuration to environment variables (.env.example) - Update Makefile with alerting targets (alert-check, alert-test) - Enhance status command to show Matrix notification status - Support station-specific water level thresholds and escalation rules - Provide dual alerting approach: native Grafana alerts and custom Python system 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>	2025-09-26 16:18:02 +07:00
grabowski	17a716fcd0	Version bump: 3.1.2 3.1.3 (Force new build) ... Version Updates: - Core application: src/__init__.py, src/main.py, src/web_api.py - Package configuration: setup.py - Documentation: README.md, docs/GITEA_WORKFLOWS.md - Workflows: .gitea/workflows/docs.yml, .gitea/workflows/release.yml - Scripts: generate_badges.py, init_git scripts - Tests: test_integration.py - Deployment docs: GITEA_SETUP_SUMMARY.md, DEPLOYMENT_CHECKLIST.md Purpose: - Force new build process after workflow fixes - Test updated security.yml without YAML errors - Verify setup.py robustness improvements - Trigger clean CI/CD pipeline execution All version references synchronized at v3.1.3 Ready for new build and deployment testing	2025-08-12 17:47:26 +07:00
grabowski	40aef686af	Fix: Replace GitHub checkout with Gitea checkout + Version bump ... Checkout Action Migration: - Replace all 'actions/checkout@v4' with 'https://gitea.com/actions/checkout' - Fixes 'Bad credentials' errors when workflows try to access GitHub API - Native Gitea checkout action eliminates authentication issues - Applied across all 4 workflow files (CI, Security, Release, Docs) Version Increment: 3.1.1 3.1.2 - Core application version updates - Web API version synchronization - Documentation version alignment - Badge and release example updates Problem Solved: - Workflows no longer attempt GitHub API calls - Gitea-native checkout action handles repository access properly - Eliminates 'Retrieving the default branch name' failures - Cleaner workflow execution without authentication errors Files Updated: - 4 workflow files: checkout action replacement - 13 files: version number updates - Consistent v3.1.2 across all components Benefits: - Workflows will now run successfully in Gitea - No more GitHub API authentication failures - Native Gitea action compatibility - Ready for successful CI/CD pipeline execution	2025-08-12 17:06:20 +07:00
grabowski	19e182c53b	Version bump: 3.1.0 3.1.1 ... Version Updates: - Core application version (src/__init__.py) - Web API version (src/web_api.py) - Main application logging (src/main.py) - Package setup version (setup.py) - Documentation generation (docs workflow) - Release workflow example version - Badge generation script - Integration test version display - README.md badge version - Setup and deployment documentation - Git initialization scripts Patch Release (3.1.1): - Workflow token migration fixes (GITHUB_TOKEN GH_TOKEN) - Pip installation warning elimination - Improved workflow reliability and logging - Better Gitea compatibility - Enhanced error handling and validation Files Updated: - 13 files with version references updated - Consistent versioning across all components - Ready for release tagging and deployment	2025-08-12 16:52:39 +07:00
grabowski	71dc2b33a8	Improve workflows: Add GitHub token support for Trivy scans ... Enhancements: - Add GITHUB_TOKEN environment variable to security and release workflows - Enable better rate limits for Trivy vulnerability scanner - Add continue-on-error for resilient security scans - Add GitHub token availability check with helpful messages - Update documentation with GitHub token setup instructions Security Benefits: - Reduced API rate limiting issues - More reliable vulnerability scanning - Better error handling and user feedback - Improved workflow stability Documentation: - Added GitHub token setup guide in GITEA_WORKFLOWS.md - Clear instructions for repository secret configuration	2025-08-12 16:09:06 +07:00
grabowski	af62cfef0b	Initial commit: Northern Thailand Ping River Monitor v3.1.0 ... Features: - Real-time water level monitoring for Ping River Basin (16 stations) - Coverage from Chiang Dao to Nakhon Sawan in Northern Thailand - FastAPI web interface with interactive dashboard and station management - Multi-database support (SQLite, MySQL, PostgreSQL, InfluxDB, VictoriaMetrics) - Comprehensive monitoring with health checks and metrics collection - Docker deployment with Grafana integration - Production-ready architecture with enterprise-grade observability CI/CD & Automation: - Complete Gitea Actions workflows for CI/CD, security, and releases - Multi-Python version testing (3.9-3.12) - Multi-architecture Docker builds (amd64, arm64) - Daily security scanning and dependency monitoring - Automated documentation generation - Performance testing and validation Production Ready: - Type safety with Pydantic models and comprehensive type hints - Data validation layer with range checking and error handling - Rate limiting and request tracking for API protection - Enhanced logging with rotation, colors, and performance metrics - Station management API for dynamic CRUD operations - Comprehensive documentation and deployment guides Technical Stack: - Python 3.9+ with FastAPI and Pydantic - Multi-database architecture with adapter pattern - Docker containerization with multi-stage builds - Grafana dashboards for visualization - Gitea Actions for CI/CD automation - Enterprise monitoring and alerting Ready for deployment to B4L infrastructure!	2025-08-12 15:40:24 +07:00