An integrated Artificial Intelligence and GIS spatial analyst tools for Delineation of Groundwater Potential Zones in complex terrain: Fincha Catchment, Abay Basi, Ethiopia

In this paper, the performance of Artificial Intelligence (AI) in Geospatial analysis and GIS platforms for the prospecting of potential groundwater zones was evaluated in Fincha catchment, Abay, Ethiopia. Components of geospatial data under morphometric, hydrologic, permeability, and surface dynamic change were confirmed as the criteria for prospecting groundwater potential zones. The influence of the individual criterion was ranked and weighted in Artificial Neural Networks (ANN) training model and Analytical Hierarchy Process (AHP). The correctness of the weights fixed in the ANN and AHP was evaluated with target data assigned to the networks and consistency index (CI) respectively. The weighted overlay analysis in the GIS environment was implemented to generate the promising zones in both approaches (ANN and GIS). The results obtained in the ANN model and GIS were evaluated based on pumping rate and ground-truthing points. Groundwater potential zones of five and four classes were delineated in AI and GIS techniques respectively, and this is an indicator for the effectiveness of AI in geospatial analysis for prospecting of potential zones than the traditional GIS technique. The percentage of accuracy in both methods was measured from the ROC curve and AUC. Therefore, it was found that the delineated groundwater potential zones and the ground-truthing points were agreed with 96% and 91% in the AI and GIS platforms respectively. Finally, it is concluded that the ANN model is an effective tool for the delineation of groundwater prospective zones.