ayouboihi/wildfirerisk-rif

Wildfire Risk Prediction — Rif Mountains, Morocco using Sentinel-2 + Random Forest ML # WildfireRisk-Rif readme_content = """# 🔥 WildfireRisk-Rif — Forest Fire Risk Prediction A geospatial machine learning project that predicts **wildfire risk zones** in the **Rif Mountains, Morocco** using real Sentinel-2 satellite imagery and a Random Forest classifier. --- ## 📌 Project Overview | Item | Details | |---|---| | **Satellite** | Sentinel-2 L2A (ESA / Copernicus) | | **Bands used** | B04 (Red), B8A (NIR), B11 (SWIR-1), B12 (SWIR-2) | | **Study area** | Rif Mountains — Tétouan / Chefchaouen, Morocco | | **Date** | September 23, 2023 (peak fire season) | | **Resolution** | 20 metres per pixel | | **ML Model** | Random Forest Classifier (scikit-learn) | --- ## 🗺️ Outputs | File | Description | |---|---| | `rif_indices.png` | NDVI, NBR, NDWI spectral indices maps | | `rif_fires_ndvi.png` | NDVI map with documented fire locations | | `rif_fire_risk_map.png` | Static fire risk map (4 classes) | | `wildfire_risk_interactive.html` | Interactive Folium web map | | `morocco-wildfire-prediction.ipynb` | Full analysis notebook | --- ## 🔥 Fire Risk Classes | Class | Color | Description | |---|---|---| | Low | 🟢 Green | Water bodies, wet zones, dense forest | | Moderate | 🟡 Yellow | Sparse vegetation, some moisture | | High | 🟠 Orange | Dry vegetation, low moisture | | Extreme | 🔴 Red | Very dry, stressed vegetation — highest risk | --- ## 📊 Key Results - **Study area:** ~1,000 km² — Rif Mountains, Morocco - **Total pixels analyzed:** ~30 million (20m resolution) - **Fire risk distribution:** - 🟢 Low: 65.3% - 🟡 Moderate: 10.2% - 🟠 High: 8.4% - 🔴 Extreme: 16.1% --- ## 🧠 Spectral Indices Used | Index | Formula | Fire Risk Relevance | |---|---|---| | NDVI | (NIR - Red) / (NIR + Red) | Low NDVI = dry vegetation = high risk | | NBR | (NIR - SWIR2) / (NIR + SWIR2) | Detects burned/dry areas | | NDWI | (NIR - SWIR1) / (NIR + SWIR1) | Low moisture = high risk | --- ## 🔧 Installation ```bash conda create -n geoenv python=3.11 conda activate geoenv conda ins …