A Hybrid Spatial–Analytical Network Process Model for Groundwater Inventory in a Semi-Arid Hard Rock Aquifer System—A Case Study

Abstract

Growing agricultural, industrial, and residential needs have increased the demand for groundwater resources. Targeting groundwater has become a challenging endeavour because of the complex interplay between varying climatic, geological, hydrological, and physiographic elements. This study proposes a hybrid RS, GIS, and ANP method to delineate groundwater zones. The resource was evaluated using seven surface hydrological and six subsurface aquifer parameters. The analytic network process model was used to determine the global priority vectors of each subclass. Surface and subsurface groundwater potential maps were created by assigning the resulting weights and spatially integrating them. Later, an integrated potential map was created by combining them. The validation of the obtained results using water level data demonstrates that the integrated map accurately predicted the zones. The area under study has 172.94 km2 of good groundwater potential. An area of 393.01 km2 is classified as having a moderate potential, and an area of 410 km2 is classified as having low potential. These findings will be beneficial to regional policymaking and long-term groundwater management. The results show that an integrated approach using ANP can better determine the groundwater potential zones in semi-arid zones.

Multi-criteria decision process to identify groundwater potential zones using geospatial tools in the Arghandab river basin, Afghanistan

“…ANP offers a broad framework for handling decisions without making any presumptions. According to Saaty (1990Saaty ( , 1988, Sujatha (2020) and Radhakrishnan et al (2022), ANP reduces a multicriteria complicated problem to a non-linear network. More crucially, because most real-life events are non-linear, the interdependence of network pieces enables better modeling of complicated problems.…”

Prediction of gold mineralization zones using spatial techniques and geophysical data: A case study of the Josephine prospecting licence, NW Ghana

“…Knowledge-driven approaches that are mostly applied in the predictive modeling of a particular natural resource such as gold, copper, iron, hydrocarbons, groundwater etc. Comprise the analytical hierarchy process [ 34 , 76 , 84 ], analytical network process [ 68 ], ELECTRE [ 2 , 3 ], fuzzy analytical hierarchy process [ 14 , 20 , 45 , 51 ], best-worst method [ 42 ], PROMETHEE [ 4 ] and TOPSIS [ 59 ]. It is worthy to note that, knowledge-driven data integration methods are best suited in areas with little or no known occurrence of the sought-after mineral [ 45 ].…”