Comparison of ANN model and GIS tools for delineation of groundwater potential zones, Fincha Catchment, Abay Basin, Ethiopia

Abstract

In this article, the novelty of Artificial Neural Networks (ANN) model and GIS platform for the delineation of groundwater potential zones were compared in Fincha Catchment, Abay Basin, Ethiopia. LULC, rainfall, soil, geology, drainage density, lineament density and geomorphologic units were used as key factors in both models. Weights were generated in ANN and Analytical Hierarchy Process (AHP) to delineate the groundwater potential zones. Groundwater potential zones with five and four categories have been delineated in the ANN and GIS tools, respectively. The potential zones were validated by overlapping the existing well locations with an overall accuracy of 85% and 82.5% in ANN and GIS tools, respectively. The ANN model revealed better performance in the delineation of groundwater potential zones in this catchment when compared with GIS tools. Therefore, the delineated groundwater potential zones will be valuable in solving the problem of drinking water in the catchment.