GIS-based multi-criteria approach to delineate groundwater prospect zone and its sensitivity analysis

Abstract

This study was aimed at delineating groundwater potential zones (GWPZ) using geo-spatial techniques for Ranchi district, Jharkhand (India). Data including Cartosat-1 digital elevation model (DEM), Landsat 8 satellite images, lithology, geology, soil, and water yield data were utilised in this study. The relative importance of multiple parameters including lithology, soil, slope, geology, rainfall, drainage density, and land use/land cover (LULC) that influence the availability of groundwater was determined subjectively. Analytical hierarchy process (AHP) along with pair-wise comparison decision theory was utilized to calculate the weights for each aforementioned parameter. The delineated GWPZ were categorized into four classes viz., very good zone (31.57%), good zone (45.43%), moderate zone (13.09%), and poor zone (8.53%). The sensitivity analysis indicated lithology and soil type as the most and least sensitive parameters, respectively influencing the presence of groundwater in the study area. Comparison between well discharge data and delineated GWPZ yielded a coefficient of determination (R2) of 0.59. This study contributes to identifying priority areas where appropriate water conservation programs as well as strategies for sustainable groundwater development can be implemented.

Delineation of Potential Groundwater Zones and Assessment of Their Vulnerability to Pollution from Cemeteries Using GIS and AHP Approaches Based on the DRASTIC Index and Specific DRASTIC

“…To minimise the related subjectivity, the normalised weights were computed in the second step of this procedure. Equation (3) [114] was also used to calculate the sum of the values in each column, which is shown in Table 5.…”

“…where C ij represents the variable used in the analysis and L ij represents the PCM's total column value. To create the Normalised Pairwise Comparison Matrix (NPCM), each column value was divided by the sum of the column values [87,114]. Each variable's normalised weight (NWt) was calculated by averaging all the values in the associated row of the NPCM (Table 5) [83,93].…”

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

“…The second step of this method is to calculate the normalized weights to reduce the associated subjectivity. A sum of the values in each column was carried out by Equation (3) [11] and is also shown in Table 2.…”

“…where L ij is the total column value of the PCM and C ij is the variable employed for the analysis. Subsequently, all the column values were divided by the sum of the column to create the Normalized Pairwise Comparison Matrix (NPCM) [4,11]. The normalized weight (N Wt ) of each variable was obtained by averaging all the values of the corresponding row in the NPCM (Table 3).…”

“…According to [10], most of the groundwater used in dry regions will be fossil water and not sustainable for the future. Excessive pumping has decreased groundwater levels [11].…”