Analysis of Hydrogeological Parameters of the Nairobi Aquifer Suite Using GIS-Based Spatial Interpolation Methods

Groundwater resources in the Nairobi Aquifer Suite (NAS), Kenya, face significant problems largely due to rapid urbanization and the rising water demand. The depletion of groundwater resources at the local level could potentially extend to regional extents, and hence affect natural water flows. This therefore calls for the prediction of aquifer hydrogeological parameters for sustainable groundwater management. This study aims to utilize GIS-based spatial interpolation methods for the in-depth analysis of NAS hydrogeological parameters. Classical geostatistical tools are employed to develop models that can be used to accurately predict hydrogeological parameters of the NAS. Field-measurable predictors, that is, geographic position, elevation, depths and first water struck level, are used to demonstrate the efficacy of the predictive models. Data from hydrogeological measurements, geological surveys and satellite imagery are integrated during the development of the predictive models for key hydrogeological parameters, including, groundwater level, discharge, drawdown, electrical conductivity, and transmissivity. Classical geostatistical tools such as kriging and natural neighbour interpolation are applied to develop spatially explicit maps of the NAS hydrogeological parameters. The distribution of borehole data is analyzed using geostatistical tools such as trend analysis and semi variogram. Cross-validation has been performed to identify the most suitable spatial interpolation model. While, in general, the prediction worked well based on model evaluation metrics such as mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE) and coefficient of determination (R²), during the testing we observed characteristic deviations from the measured values at some locations. These differences could be due to the geological setting; however, a few outliers may appear due to yet unknown reasons. Further studies utilizing machine learning techniques are expected to develop accurate predictive models that can help in sustainable groundwater management in the NAS. The generated spatial maps provided insightful information on the spatial distribution of hydrogeological parameters in the NAS, facilitating the accurate identification of prospective locations for ideal groundwater extraction.

Keywords: GIS; hydrogeological parameters; Nairobi Aquifer Suite; machine learning; predictive modelling; spatial mapping