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. The solutions produced by the AHP and ANN were more accurate than the UNICEF model in determining the productivity of deep wells in the study data, whilst the LR model was less accurate than the UNICEF model. The groundwater productivity maps produced by the AHP and ANNs showed clear correlation with the maps produced by the UNICEF model, despite moderate (AHP) and severe (ANN) parameter perturbation, demonstrating the robustness of the UNICEF model. Whilst the AHP and ANN models demonstrated higher accuracy than the UNICEF model, this must be considered against the well data used to assess accuracy, which were drawn from a small sample of non-ideal distribution. Although this study focuses on case studies in Ethiopia the key findings are applicable internationally, namely, that the use of the AHP in data-scarce environments provides robust models, and that with the addition of easily obtainable well data the accuracy of modelling can be significantly increased through the application of ANNs.
Prediction of transportation costs using trapezoidal neutrosophic fuzzy analytic hierarchy process and artificial neural networks