Matthias-Ab/africa-shield-ai

Africa Shield AI - Last-Mile Alert AI flood early-warning prototype # Africa Shield AI **Big vision:** Africa Shield AI predicts natural disasters (floods, droughts, heatwaves, wildfires, cyclones, earthquakes) across Africa and sends early warnings to at-risk communities before disasters happen, prioritizing accessibility for people without smartphones or reliable internet. **Scoped hackathon demo:** "Last-Mile Alert AI" — flooding only, working end-to-end: a rules-based flood risk score shown alongside a genuine trained ML model as a second opinion, an API that turns the result into a human-readable alert, translation into a local African language, real SMS/voice-call alerts (with USSD self-service to check risk or subscribe) via Africa's Talking — voice covering people a text-only channel doesn't reach — and a live IoT sensor ingestion endpoint, demoed via a Wokwi ESP32 simulation, all shown on a simple web dashboard. **Future:** expand to the other hazards, train the ML model on real historical data instead of synthetic data, real ESP32 hardware instead of the Wokwi simulation, and an offline-first mobile app (see Future Improvements below). Built for the **"AI for All Hackathon: Building Inclusive Solutions for Early Warning and Disaster Resilience,"** organized by the African Youth Advisory Board on Disaster Risk Reduction (AYAB-DRR) under the African Union. ## Status: backend + frontend both real, alerts send for real, IoT ingestion live (updated 2026-08-17) - **Backend risk scoring and translation are real**, not stubbed. `POST /api/risk-check` and `GET /api/regions` compute live from the rules-based model in `backend/app/models/risk_model.py` and the hardcoded translation dictionary in `backend/app/models/translations.py`. See `docs/progress-log.md` for thresholds, assumptions, and what's still unverified. - **A second, genuinely trained ML model now runs alongside the rules-based one** (`ml_risk_level`/`ml_risk_score`, additive fields on both endpoints) — a `scikit-learn` logistic regression, trained on synthetic data …