Comparison of ANN model and GIS tools for Delineation of Groundwater Potential Zones, Fincha Catchment, Abay Basin, Ethiopia
Abstract
The novelty of ANN model and GIS tools for delineation of groundwater potential zones were compared in Fincha Catchment, Abay Basin, Ethiopia. The reclassified slope, LULC, rainfall, soil, geology, drainage density, lineament density, and geomorphologic units were used as inputs in both models. Weights were generated in Artificial Neural Networks (ANN) and Analytical Hierarchy Process (AHP) to delineate the groundwater potential zones. The result in the ANN model showed five qualitative-based classifications, whereas four classifications were identified in the result obtained in GIS tools. The delineated potential zones were evaluated with the existing water sources and overlapped percentages of 85% and 82.5% was reached in the ANN model and GIS tools respectively. Therefore, it was concluded that the ANN model is more powerful and accurate in the delineation of groundwater potential zones than GIS tools in the region where skilled manpower and financial capacity are not affordable