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. 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.

Comparative performance of rural water supplies during drought

“…The most appropriate technical intervention in a given area is determined by hydrological, hydrogeological and demographic constraints. For example in some areas of the Kolla groundwater levels can be up to 250 m deep [36][37][38] , well beyond the maximum capacity of a hand-pump (typically 45 m). In these areas motorised boreholes are the only appropriate technological solution.…”

Improving Monitoring and Water Point Functionality in Rural Ethiopia

“…The appropriateness of deep groundwater investigation and development during periods of increased drought in Ethiopia has been demonstrated before. Investments in deeper groundwater prove to reduce the unit costs and improve the sustainability of water points [24][25][26][27]. Similarly, mapping of water points, year-round availability of water, and vulnerability towards droughts provide opportunities for monitoring and water service delivery in rural areas [28,29].…”

Assessment of Groundwater Potential Zones of Upper Blue Nile River Basin Using Multi-Influencing Factors under GIS and RS Environment: A Case Study on Guder Watersheds, Abay Basin, Oromia Region, Ethiopia

“…The application of geographical information systems in groundwater assessing, monitoring, and management such as delineation of groundwater potential zones has been reported by many scholars [3,4,11,[31][32][33][34][35][36][37][38][39][40][41][42]. Several researchers in current days perform several advanced methodological approaches for groundwater potential investigation, amongst which frequency ratio [43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61], logistic regression [50][51][52][53][54][55][56], fuzzy logic [22][23][24][57][58][59][60][61]…”