Abstract
This study used remote sensing (RS) and geographic information system (GIS) techniques to assess groundwater potential areas by applying two multi-criteria decision-making analyses tools in the Arghandab river basin. Twelve influencing parameters summarizing basin characteristics were gathered and generated using geospatial RS and GIS tools. The analytical hierarchy process (AHP) and analytical network process (ANP) were examined to weigh, ranking, and reclassify raster to produce groundwater potential maps. Two multi-criteria decision models were applied to compare results and suitability in the study area. The results of the AHP analysis delineate five groundwater potential zones (GWPZs) classified as very poor (29%), poor (22%), moderate (17%), high (19%), and very high (14%). On the other hand, the results of the ANP analysis classified GWPZs as very poor (25%), poor (9%), moderate (25%), high (30%), and very high (11%). To validate generated GWPZs maps, a total of 270 well locations data were utilized in the receiver operating characteristic (ROC) curve analysis. ROC model accuracy in training and validation stages is marginally higher for the ANP model (0.810 and 0.823) as compared to the AHP model (0.749 and 0.742). The groundwater potential map delineated in this study offers a preliminary assessment to scientists, public authorities, and policy makers for sustainable management of groundwater resources in the study area.
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performend by all authors. The first draft of the manuscript was written by Assaduallah Farahmand and Hussian Ali Jawadi. All other authors revised and commented on the first version of the manuscript. All authors read several times the manuscript and approved the final version of the manuscript.
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Farahmand, A., Hussaini, M.S., Jawadi, H.A. et al. Multi-criteria decision process to identify groundwater potential zones using geospatial tools in the Arghandab river basin, Afghanistan. Environ Earth Sci 82, 362 (2023). https://doi.org/10.1007/s12665-023-11046-3
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DOI: https://doi.org/10.1007/s12665-023-11046-3