Assessing the performance and robustness of the UNICEF model for groundwater exploration in Ethiopia through application of the analytic hierarchy process, logistic regression and artificial neural networks

Abstract

This study assesses the performance and robustness of the groundwater potential (GWP) maps produced by the UNICEF model for deep groundwater exploration in Ethiopia. The UNICEF model is a weighted linear combination of hydrogeological parameters including permeability, slope, recharge, and lineament density, which has been calibrated using the expert judgements of local hydrogeologists. In order to assess the performance and robustness of the model, three techniques were employed: the analytic hierarchy process (AHP), logistic regression (LR), and artificial neural networks (ANNs). Three study areas (Dallol, Halaba and Shinelle) were selected on the basis of climatic and geological variation, in addition to the availability of well data pertaining to depth and yield. The performance of the UNICEF model in predicting outcomes of the well data included in the study was assessed by computing the receiver operating characteristic (ROC) curve.

Authors

Categories