Abstract

In this paper, 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.

摘要

本文比较了埃塞俄比亚阿拜盆地芬查集水区人工神经网络(ANN)模型和GIS平台划定地下水潜在区的新颖性。LULC、降雨量、土壤、地质、排水密度、线状密度和地貌单位被用作两个模型的关键因素。在人工神经网络和层次分析过程（AHP）中生成权重以划定地下水潜力区。ANN 和 GIS 工具分别描绘了五类和四类地下水潜力区。通过以 ANN 和 GIS 工具分别以 85% 和 82.5% 的整体精度重叠现有井位来验证潜在区域。与 GIS 工具相比，ANN 模型在划定该流域的地下水潜在区方面具有更好的性能。