Comparative analysis of machine learning and analytical hierarchy analysis for artificial groundwater recharge map development

A proactive policy for tackling the global water crisis involves the application of artificial groundwater recharge (AGR). AGR site selection is a complex challenge, particularly in large study areas. Considerable research attempted to locate AGR sites using field data collection or conventional decision modeling techniques, such as analytic hierarchy process (AHP). However, the present study utilizes machine learning (ML) techniques with geographic information system (GIS) and remote sensing images to develop a high-efficiency AGR map for the United Arab Emirates. In this study, nine thematic layers were considered: precipitation, drainage density, total dissolved solids, groundwater level, geology, geomorphology, lineament density, elevation and distance from residences. The study applied three ML models, namely support vector machine, multilayer perceptron and random forest (RF), to estimate the relative importance of each thematic layer through feature importance analysis. The AHP approach was also used for comparison. The weights for each thematic layer were determined through a literature review and expert opinions. Results showed that the RF model performed best, with an overall prediction accuracy of 99%. The developed AGR maps were categorized according to their suitability for AGR potential, with approximately 10% of the study area categorized as high. The results of the AHP and RF approaches were relatively similar, indicating that the qualitative approach of AHP was validated by the data-driven approach of RF. The present study presents a framework that can be applied in other climate regions with data availability. This framework can also help environmental agencies and practitioners understand the role of ML in AGR site selection. The results also demonstrate the effectiveness of combining GIS, remote sensing and ML techniques to produce high-efficiency AGR maps.