Geospatial and multi-criteria decision approach of groundwater potential zone identification in Cuma sub-basin, Southern Ethiopia

Abstract

Groundwater is an essential component of our country's freshwater supplies. It plays a critical role in satisfying the water demands of the nation's many user sectors. The groundwater resource cannot be exploited and sustained optimally unless prospective zones are identified prior to the drilling of wells. The current study intended to analyze groundwater potential possibilities in the Cuma sub-basin, Omo Gibe basin, southern Ethiopia, utilizing geospatial and multi-criteria decision analysis approaches. For this purpose, a range of thematic layers like geology, Rainfall, drainage density, slope, land use and land cover (LULC), Soil type, faults density, saturated hydraulic conductivity, Available water capacity, and soil depth were organized for the study area. The different sub-criteria of each theme layer were rated according to their effect on groundwater recharge, and a weightage was assigned to each thematic layer based on the analytical hierarchy method (AHP). The identification of groundwater-potential regions of the sub-basin was one of the study's main results. The resulting groundwater potential zone map is further classified into five groundwater potential classes: very good (7.01%), good (19.49%), moderate (17.48%), poor (29.51%), and very poor (26.51%). The study's findings have important significance for developing sustainable groundwater strategies in the area.

Groundwater quality and potential analysis using geospatial techniques: The case of Ashanti Region in Ghana

“…The majority of these soil types are aerated, with loamy sand and coarse sandy loam textures allowing for infiltration and percolation, which raises the potential and recharge of the groundwater. They also have a high filtration rate to allow water to flow through very easily [ 14 , 41 , 42 ]. Also, the Obosum and Oti beds geology groups were found within the excellent groundwater potential zone.…”

“…Utilizing the weights generated by the AHP technique, groundwater occurrence and movement controlling criteria were combined in ArcGIS to determine the groundwater potential zones of the Ashanti Region. To assign weights to specific layers and determine their relative weight, decision matrices were created using the AHP technique utilizing Satty's scale of 1–9 [ 14 , 32 , 33 ]. Satty's Measure of Relative Importance is displayed in Table 4 .…”

“…Most of the research done in Ghana on water quality assessment used a statistical and multivariate approach in assessing the quality of the groundwater source for drinking and irrigation purposes. Research done by Arnold et al [ 2 ], Boateng et al [ 6 ], Hayford & Appiah-Adjei [ 7 ], and Sander et al [ 13 ] failed to determine spatially the groundwater zones and quality similar to works by Hagos & Andualem [ 14 ], Kabeto et al [ 15 ], Rahmati et al [ 16 ], and Saranya & Saravanan [ 17 ]. The purpose of this study is to create continuous surface and maps of groundwater quality data, which will help to identify areas where groundwater quality parameters exceed the acceptable limits.…”

Identification of Potential Farm Pond Sites for Spring Surface Runoff Harvesting Using an Integrated Analytical Hierarchy Process in a GIS Environment in Northern Kazakhstan

“…The slope gradient, which depicts the steepness of the Earth's surface, is a crucial relief component. According to Melese and Belay [25,51], a moderate slope has the maximum potential for infiltration, whereas a steeper slope produces more runoff. The slope is one of the more important parameters when choosing zones for reserving melt and flood waters, the average value of which in the Akkayin district is about 2%.…”

Mapping groundwater potential zone in the subarnarekha basin, India, using a novel hybrid multi-criteria approach in Google earth Engine

“…They then performed a sensitivity analysis to determine the impact of hydrologic and geological factors on their variations. The widely used multi-criteria-based decision support techniques for GWP mapping include AHP [ 22 , [26] , [27] , [28] ], Frequency ratio (FR) [ 29 ], Logistic regression ( ) [ 30 ], Fuzzy set [ 31 ], Quick unbiased efficient statistical tree (QUEST) [ 32 ], Weighted linear combination (WLC) [ 33 ], Evidential belief function (EBF) [ 34 ], Multi-influencing factor ( ) [ 35 ], Shannon's entropy [ 36 ], TOPSIS [ 37 ], Dempster-Shafer model [ 38 ], Bayesian network model [ 39 ] etc. Causal relationships based on Fuzzy decision-making trial and evaluation laboratory (FDEMATEL) approaches have also been applied for soil erosion, flood, and landslide susceptibility mapping [ 40 , 41 ].…”