Nelvinebi/Groundwater-Contamination-Risk-Mapping-in-the-Niger-Delta-Using-GIS-and-Machine-Learning

Machine learning–driven groundwater contamination risk mapping for the Niger Delta using GIS and synthetic hydro-environmental data, producing spatial risk zones and GeoTIFF outputs to support groundwater protection, environmental monitoring, and evidence-based decision-making in data-scarce coastal regions. Groundwater Contamination Risk Mapping in the Niger Delta Using GIS and Machine Learning 📌 Project Overview This project develops a spatially explicit groundwater contamination risk map for the Niger Delta using GIS and machine learning. Synthetic hydro-environmental datasets are integrated to model contamination susceptibility and generate raster and vector risk outputs. 🎯 Objectives Model groundwater contamination risk using ML classifiers Integrate hydrogeological and anthropogenic factors in GIS Produce GeoTIFF and shapefile risk maps for decision support 🗂️ Project Structure ├── data/ │ └── groundwater_contamination_dataset.xlsx ├── scripts/ │ └── groundwater_contamination_risk_ml.py ├── outputs/ │ ├── groundwater_contamination_risk_niger_delta.tif │ └── groundwater_contamination_risk_zones.shp ├── README.md 🧪 Dataset Description Synthetic but realistic variables include: Depth to groundwater Nitrate concentration Electrical conductivity Land use intensity Distance to pollution sources Soil permeability Target variable: Groundwater contamination risk (Low, Moderate, High) 🧠 Methodology Summary Data preprocessing and normalization Supervised ML classification (Random Forest) Rasterization and spatial prediction Risk zoning and GIS visualization 🗺️ GIS Outputs GeoTIFF: Continuous groundwater contamination risk surface Shapefile: Classified contamination risk zones 🛠️ Tools & Libraries Python, NumPy, Pandas Scikit-learn Rasterio, GeoPandas, Shapely QGIS / ArcGIS for visualization 📍 Study Area Niger Delta region, Nigeria (WGS84 – EPSG:4326) 👤 Author AGBOZU EBINGIYE NELVIN LinkedIn: *linkedin.com