An integrated geospatial multi-influencing factor approach to delineate and identify groundwater potential zones in Kabul Province, Afghanistan

Abstract

This study was an attempt to evaluate the groundwater potentiality in Kabul province, Afghanistan using geospatial multi in uencing factor (MIF) approach. The in uencing parameters employed for the assessment of groundwater potential zones (GWPZ) includes slope, geology, soil, land use/land cover, lineament density, rainfall and drainage density. The sub-classes within each in uencing parameter were sub-divided, based on their effectiveness in groundwater potentiality as major, minor and no effect, and subsequently assigned a score value. The combined score value of these parameters was used for calculating the relative weight. The delineated GWPZ were classi ed in four groups, i.e. poor, moderate, good and very good GWPZ. The study results revealed that very good GWPZ covered an area of 354.87km 2 (2% of the total area), good 1523.86 km 2 (20%), moderate 2250.99 km 2 (73%) and poor 477.19 km 2 (5%). The study concluded that geospatial assisted MIF approach was very useful and e cient techniques for the assessment of GWPZ and can be effectively employed to enhance the conceptual understanding of groundwater resources of Kabul Basin, Afghanistan. HighlightsMulti in uencing factor approach was used to evaluate groundwater potential zones.Seven in uencing parameters were used for groundwater potential zones' evaluation.Satellite images and secondary data was used for the in uencing parameters layers.The generated map revealed 73% of Kabul province area covered by moderate GWPZ.The validation suggested multi in uencing factor approach as an effective tool.

Delineation of Groundwater Potential Zones (GWPZs) in a Semi-Arid Basin through Remote Sensing, GIS, and AHP Approaches

“…The drainage density is defined as the ratio between the total length of the watercourses in a basin and the surface area of the drained basin [51,68,69]. Typically, groundwater occurrence is inversely proportional to drainage density; a high drainage density leads to lower infiltration and minor GWPZs, whereas a low density leads to major GWPZs [58,[70][71][72][73][74]. However, the drainage system depends on several variables such as slope gradient, the absorption capacity of soils, rainfall, vegetation cover, climate, topography, and subsurface characteristics [65,75,76].…”

“…(3) Other Multi-Criteria Decision Making techniques could be applied for better results. Among the most employed for identifying GWPZs are Multi Influencing Factor Analysis (MIF) [14,72,98,143], Analytical Networking Process (ANP) [103,144,145], and Fuzzy-Analytic Hierarchy Process [113,131,146,147]. (4) Thematic maps often generalize the nature of the site, so a variation on a local scale could be found.…”

An Approach to Delineate Potential Groundwater Zones in Kilinochchi District, Sri Lanka, Using GIS Techniques

“…The use of remote sensing data in hydrological investigations can give information on spatial and temporal scales, which is necessary for effective analysis, prediction, and validation of hydrological models [30]. Satellite imagery's capacity to span vast spatial scales is vital for depicting physiographic and structural characteristics, which are essential requirements for delineating potential groundwater zones [30] and have been used by various researchers [35][36][37][38][39][40][41].…”

“…Integrated GIS and RS with AHP prove that it is a handy tool for delineating potential groundwater zones by reducing time and cost [4,17,32,37,41,44,45,[48][49][50]. Researchers adopt various techniques for delineation potential groundwater zones: statistical method [51], influence factor (IMF) [36,38,40,52], groundwater modelling, the combination of GIS, RS with AHP [37,41,45,49], and GIS-based machine learning [39,53,54]. Many researchers have adopted these methods since they have been demonstrated to be reliable and effective.…”

“…As groundwater is dynamic, RS data integration into the GIS is convenient for identifying the potential groundwater zones (GWPZ). [41] x x x x x x x x Karimi-Rizvandi et al, [39] x x x x x x x x x x Aslan and Çelik, [17] x x x x x x x x x Jhariya et al, [40] x x x x x x x x Sarwar et al, [52] x x x x x x x Nasir et al, [36] x x x x x x x Kumar et al, [37] x x x x x x x x x x Dilekoglu and Aslan, [49] x x x x x x x x x Khan et al, [38] x x x x x x x x x Abijith et al, [44] x x x x x x x x Achu et al, [32] x x x x x x x Ajay Kumar et al, [50] x x x x x x x Ahmad et al, [47] x x x x x x x x Arefin, [31] x x x x x x x x x x Arya et al, [56] x x x x x x x x x x Bhunia, [57] x x x x x x x Qadir et al, [58] x x x x x x Lentswe and Molwalefhe, [48] x x x x x Sl-Slope, Lu-Land use, Ge-Geology, Li-Lithology, Gm-Geomorphology, Rf-Rainfall, SPi-Stream Power Index, El-Elevation, So-Soil, Ld-lineament density, Rd-River density, If-Infiltration TWi-Topographic wetness index, STi-Sediment transport index, Cu-Curvature, Dd-Drainage density, Al-Altitude, Dr-Distance from the river, Aq-Aquifer, Dg-Depth to groundwater.…”