Momahmoses/water-quality-mapping

||WHO water quality mapping that identifies contaminated communities and prioritises WASH interventions across rural Nigeria. # Water Quality & Borehole Access Mapping Maps water sources (boreholes, hand pumps, rivers, wells) + population data + WHO quality test results to identify contaminated or waterless communities across rural Nigeria, supporting WASH programmes and RUWASSA investment decisions. --- ## Problem Statement 60+ million Nigerians lack access to safe drinking water. Waterborne diseases kill thousands annually. Without spatial mapping of water source quality, interventions miss the most vulnerable communities. This platform identifies contaminated sources and prioritises remediation by affected population. --- ## Features | Feature | Description | |---------|-------------| | WHO Compliance Checking | pH, turbidity, nitrate, coliform, arsenic, fluoride threshold testing | | Contamination Prediction | Random Forest classifier on water chemistry features | | Population-Weighted Priority Scoring | Investment priority rank by contamination × population exposed | | Waterless Community Detection | Communities >2 km from any functional water source | | Interactive Map | Folium map with water source markers, quality layers, and community overlays | --- ## Tech Stack | Layer | Technology | |-------|-----------| | Machine Learning | scikit-learn (Random Forest) | | Geospatial | GeoPandas, Folium, Shapely | | Data | pandas, NumPy | | Visualisation | Matplotlib, Seaborn | --- ## Quick Start ```bash git clone github.com cd water-quality-mapping pip install -r requirements.txt python main.py ``` --- ## Data Sources - RUWASSA/RWSSP borehole and water point registry - WHO water quality guidelines (2022) - UNICEF MICS water access survey data - WaterAid Nigeria field quality test results --- ## Author **Momah Moses**, Geospatial AI Engineer & Data Scientist GitHub · Portfolio