Geospatial technique for the delineation of groundwater potential zones using multi-criteria-based AHP and MIF methods

Groundwater is a vital resource for communities and ecosystems in the tropical wet and dry climate zone of the Raipur urban area, where temperatures remain moderate throughout the year, groundwater withdrawal for drinking water, agriculture, industry, and other purposes has increased in the last 10 years (Galkate et al. 2012, 2015). The problems with our water demand are not going to diminish and how we find and utilize water has become one of the most significant questions to the future of any new urbanization. Groundwater is drastically needed due to the enormous pressure on population and urbanization (Sinha et al. 2016, 2019, 2022). Groundwater issues appear slowly and incrementally as the cumulative result of several individual impacts of abstraction and contamination sources manifests themselves (CGWB 2014).

An advance geospatial technique has an enormous capability for accession, regulation, and conserving groundwater resources. A review of the literature suggests that researchers are using various geospatial methods to map groundwater potential areas (Magesh et al. 2012; Kumar et al. 2016; Rahmati et al. 2016; Yeh et al. 2016; Falah et al. 2017; Thomas & Duraisamy 2018; Nguyen et al. 2022). For scientific planning, the current work uses geospatial techniques to create a groundwater potential zonation Map (GWPZ) based on multiple-criteria decision analysis (MCDA). MCDA has been acknowledged by several scholars as an efficient tool for water resources management (Nampak et al. 2014; Ahmed & Sajjad 2018; Arulbalaji et al. 2019; Mallick et al. 2019). To identify GWPZ, 12 thematic layers, lithology, geomorphology, LULC, soil, lineament, drainage, slope, rainfall, topographic wetness index (TW), roughness, topographic positional index, and curvature, were used previously (Pourtaghi & Pourghasemi 2014; Yeh et al. 2016; Arulbalaji et al. 2019; Abijith et al. 2020). The potential of each thematic layer on groundwater availability needs to be analyzed.

MCDA provides a rich collection of techniques and procedures for structuring decision problems, and designing, evaluating, and prioritizing alternative decisions regarding the most potential layer of different thematic layers to create GWPZ (Malczewski 2006). MCDA has been applied through various techniques including the analytic hierarchy process (AHP), and multi-influencing factors (MIF), commonly used in the groundwater potential zone analysis (Mohammady et al. 2012; Mas et al. 2013; Tahmassebipoor et al. 2015; Kumar & Krishna 2018; Razavi-Termeh et al. 2019; Jhariya et al. 2021; Raj et al. 2022; Uc Castillo et al. 2022; Jaiswal et al. 2023; Shelar et al. 2023) In 1980, Thomas Saaty provided the 1–9 scale for every thematic layer in AHP, a useful statistical tool for dealing with complex decision-making in different disciplines of system engineering (Saaty 1977; Wind & Saaty 1980). The MIF approach is an easy, reliable, and efficient method to identify statistically determined groundwater potential areas using each thematic layer's rank and weight (Magesh et al. 2012; Mohammady et al. 2012; Mas et al. 2013; Tahmassebipoor et al. 2015; Thapa et al. 2017; Kumar & Krishna 2018; Anbarasu et al. 2019; Razavi-Termeh et al. 2019; Abijith et al. 2020; Raj et al. 2022).

In the current work, 12 thematic layers were used for identifying GWPZ and these thematic layers have individual characteristics that influence the availability and sustainability of groundwater. From the 12 layers, it is complex to analyze their influence on groundwater. The advantage of AHP and MIF in groundwater potential zone analysis is its ability to deal with complex problems and make suitable decisions (Jhariya et al. 2021; Uc Castillo et al. 2022; Jaiswal et al. 2023; Shelar et al. 2023). Individual analysis with AHP and MIF for identifying GWPZ with various thematic layers may make inappropriate results for developing urban areas like Nava Raipur. Statistical analysis approaches, such as AHP and MIF, were integrated with GIS to determine the GWPZ in the current study.

The current work applied AHP and MIF for the same region so that each result was validated using a new trending approach, receiver operating characteristics (ROC) and area under the curve (AUC). AUC is a commonly used metric to assess how accurate diagnostic tests are. The test's accuracy increases with the ROC curve's proximity to the upper left corner of the graph, where the sensitivity and false-positive rate are one (specificity = 1). AUC = 1.0 is hence the perfect ROC curve. The ROC and AUC have been applied in several studies to determine the accuracy of groundwater potential maps (Mohammady et al. 2012; Mas et al. 2013; Bui et al. 2014; Tahmassebipoor et al. 2015; Thapa et al. 2017; Kumar & Krishna 2018; Razavi-Termeh et al. 2019; Raj et al. 2022). The research specifically looked at how various groundwater recharge and storage control interdependent factors, such as lithology, geomorphology, LULC, soil, lineament, drainage, slope, rainfall, TW, roughness, topographic positional index, and curvature, were used previously (Pourtaghi & Pourghasemi 2014; Yeh et al. 2016; Arulbalaji et al. 2019; Abijith et al. 2020) and ultimately affect the groundwater potentiality of the study area through groundwater storage and movement. Policymakers, economists, and government planners will greatly benefit from the study's effective and suitable groundwater planning.

The planning area of the new capital region is distributed into three layers in Figure 1, whereas layer 1 indicates the first phase settlement of Nava Raipur and layer 2 shows the future extension region of layer 1 as per the population demand and land requirement. Layer 3 represents the airport (Swami Vivekananda International Airport) area in Nava Raipur. The study area is the region of the smart city that is in the developing stage and after the settlement of the proposed population, it is likely to face water scarcity in the future due to land-use restrictions and demands for sustainable human settlement. Groundwater resources must now be scientifically investigated as an alternative due to the rising necessity for reliable and sustainable water sources to lessen reliance on surface water.

Five data sources, including the district resource map (DRM) of the Geological Survey of India (GSI), the National Bureau of Soil Survey and Land Use Planning (NBSS-LUP), the Indian Metrological Department (IMD), the CARTOSAT 1 Imagery, and the CartoDEM 30 m, were used in this study to create thematic layers. The CARTO product was obtained from the Chhattisgarh Council of Science & Technology (CCOST). From each data source, different thematic layers were prepared as listed in Table 1.

Any terrain's lithological setting has a major impact on the presence and distribution of groundwater (Yeh et al. 2016; Guru et al. 2017). Groundwater storage and flow are more favored by unconsolidated sedimentary and fractured rocks. On the basis of the published DRM of the GSI, the various lithological units were delineated and classified. The hydrogeological importance of lithology has been considered in determining the weightage as far as the rock characteristic of the study area is considered. While estimating the weight of a rock, due importance is given to its characteristics based on the process of formation and its disintegration, etc. (Arulbalaji et al. 2019).

Lineaments indicate the regions of faulting and fracturing resulting in increased secondary porosity and permeability (Yeh et al. 2016). Lineaments control the groundwater percolation and act as a permeable medium, especially in hard and massive rock-fractured aquifer terrain (Anbazhagan et al. 2011). The intersection of well-developed lineaments may retain a significant volume of groundwater. The hydrogeologist can easily locate cracks using remote sensing data, such as satellite images, low-altitude radar images, and aerial photography by their comparatively linear alignments (Dinger et al. 2002; Anbazhagan et al. 2011; Nampak et al. 2014). The lineaments were delineated from an integrated DRM with a Cartosat-1 satellite image by the visual interpretation method. Line Density tool in GIS environment was used to prepare lineament density. The primary function of fractured lineaments in terms of groundwater is that they serve as channels for the infiltration of surface water into aquifers and serve as more effective conduits for groundwater to pass through any structure or crevice in the rocks (Nagarajan & Singh 2009).

One of the key elements employed frequently for the definition of GWPZ is geomorphology, which signifies the landform and topography of an area. It provides a detailed distribution of different landforms and the processes of water flow movement.(De Reu et al. 2013; Rajaveni et al. 2015; Kumar et al. 2016; Ralph Schöpke & Zahn 2017; Das & Pal 2019). It can be extracted from the satellite imagery. Different geomorphological units viz. pediplains, lateritic plains, and water bodies are identified from the Cartosat-1 image through visual interpretation.

Infiltration, evapotranspiration, soil erosion, runoff, and other factors are all influenced by land use and land cover. It also indicates the groundwater requirements (Arulbalaji et al. 2019; Abijith et al. 2020). LULC was interpreted using a Cartosat-1 and LISS IV merged false color composite (FCC) satellite imagery. The LULC layer was classified using the visual interpretation technique according to Anderson's Level 1 categorization. (Pradhan 2009) such as agriculture land, built-up, forests, water bodies, etc., from the FCC (Pradhan 2009).

The amount of water that may permeate into subterranean formations is significantly influenced by the soil type, and affect the groundwater recharge (Dinesan et al. 2015; Das 2017; Arulbalaji et al. 2019). The ranking of different soil types is based on their composition and water-holding capacity, with soil parameters being a key component in assessing groundwater potential because they regulate water-holding capacity (Kresnanto & Sriyono 2019; Abijith et al. 2020). Maps published by the Indian Council of Agricultural Research, NBSS-LUP were used to generate the soil data and classify them based on texture.

D_d is the drainage density, D_i is the total length of all streams, and A is the area in kilometers squared.

Slope provides important geodynamic details about the nature of regional geology and formation processes. The slope is one of the major factors that directly influence the recharge rate. Generally, slope and infiltration rate have an inverse relation (Zecharias & Brutsaert 1988; De Reu et al. 2013). The slope is an important topographical feature that describes the steepness of the ground surface. Since the water from precipitation flows downhill faster, steeper slopes produce less recharge due to less time to infiltrate and recharge. A longer period for water to dwell in relatively flat surface areas with gentle slopes makes them ideal for recharging (De Reu et al. 2013; Arulbalaji et al. 2019; Das & Pal 2019). The slope is derived from the Digital Elevation Model using mathematical modeling in the geographical information system (GIS).

The primary source of water for the hydrological cycle is rainfall and the most dominant influencing factor in groundwater development. Annual rainfall data for 2022 are collected from the Indian Meteorological station (IMD) for the present study. The spatial distribution map of rainfall was prepared using the inverse distance weighting (IDW) interpolation method in the GIS platform. The annual rainfall ranges from 1,100 to 1,276 mm (Bhelawe et al. 2014; Sinha et al. 2023).

TWI is commonly considered to calculate the control of forms and features of land surfaces on hydrogeological processes and reflects the possibility of groundwater derived from rain percolating downward from the surface brought on by topographical impacts. (Moore et al. 1991; Sörensen et al. 2006). The TWI, as a secondary topographic indicator, has been frequently used to show how morphological conditions affect the position and extent of saturated zones where surface runoff generation occurs (Moore et al. 1991; Pourtaghi & Pourghasemi 2014; Razandi et al. 2015).

The degree of surface undulation is expressed by the roughness index (Riley et al. 1999). Over time, the undulating topography gradually transforms the rugged surface into a smooth and flat surface due to weathering and erosion processes. (Arulbalaji et al. 2019). Roughness is derived from CartDEM using mathematical modeling in the GIS platform.

To describe the quantitative nature of the surface profile, acceleration and deceleration, as well as flow convergence and divergence, are predominantly controlled by the curvature of the region. (Lee et al. 2020). Convexity causes the water flow to spread and concavity causes it to assemble and curvature value can be used to find soil erosion patterns as well. (Arulbalaji et al. 2019). The curvature tool in GIS is designed to compute the second derivative value of each pixel of the input surface raster.

In this scenario, X_i stands for the minor effect of factors, whereas X represents the major effect of factors.

About 40 years ago, Saaty tested the other 20 scales to choose a suitable ratio scale for the pairwise comparisons in the analytical hierarchical process (AHP) (Wind & Saaty 1980; Saaty 1994; Saaty 1996). Based on their testing results, the 1–9 scale has become the most widely used ratio scale in the MCDA (Saaty 2008; Zhang et al. 2009). The AHP methodology for MCDA has been extensively used and efficiently implemented in various areas of development planning, resource management, and environmental impact analysis. (Malczewski 2006; Saaty 2008)

According to Hosseinali & Alesheikh (2008), an AHP typically involves six phases:

• i.

  Identifying relationships between problem decision criteria and alternatives;

• ii.

  Pairwise comparison between criteria to identify the priority of the decision maker;

• iii.

  Pairwise comparison between alternatives based on their performance within each parameter;

• iv.

  Using the eigen-value method to determine the relative weights of the decision-making criteria;

• v.

  Study of the frameworks' dependability;

• vi.

  Ranking the alternatives by adding up the weighted choice variables.

A high-weight criterion indicates a layer with a substantial impact, whereas a low-weight criterion indicates a layer with less influence on groundwater potential. Accordingly, a pairwise comparison matrix has been used to evaluate the relationships between all thematic levels. To assign a weight, the subcategory of thematic layers was ranked using the natural breaks classification method in a GIS on a scale from 0 to 9 according to their respective influence on the development of groundwater (Saaty 2008; Kumar et al. 2016; Arulbalaji et al. 2019; Abijith et al. 2020; Benjmel et al. 2020).

In this scenario, λ is the biggest eigen value of the framework that can be determined with reasonable accuracy from the given matrix and n is the number of groundwater elements.

Saaty has opined that a CR of 0.10 or less is adequate to continue the analysis. If the consistency value is greater than 0.10, then it is necessary to amend the decision to locate the causes of inconsistency and correct it accordingly (Malczewski 2006; Saaty 2008; Arulbalaji et al. 2019). All the factors were analyzed against each other in a pairwise comparison matrix. With the aid of field-based expertise, the rank of each class of thematic layer is determined, normalized, and applied in AHP.

The pairwise comparison has perfect consistency if the CR value is 0. The decision matrix is moderately reliable because the threshold value does not rise above 0.1. To prepare the groundwater potential area map, all the layers were assigned appropriate weights from AHP according to Saaty's scale (1–9) of relative importance assessment and integrated with the weighted overlay analysis method in a GIS environment. (Saaty 2008; Zhang et al. 2009; Jothibasu & Anbazhagan 2016; Arulbalaji et al. 2019).

The criteria were incorporated into the geospatial environment to decide the range of categories as very high, high, medium, low, and very low regions of the potential zone using normalized weight of AHP and MIF data. The resulting GWPZ map is validated with yields of 21 observed borewells obtained from the Rajiv Gandhi National Drinking Water Project (RGNDWP). Yield data were first validated with AHP and MIF, GWPZ categories using linear regression analysis and then using receiver operating characteristics (ROC), and area under the curve (AUC). ROC and AUC validation is based on the interpretation of the obtained class from GWPZ to the reach values of yield data of wells with their chances of true or false positivity, whereas regression analysis is based on the relationship between two or more variables. The trade-off between the false-positive (Y-axis) and false-negative (X-axis) rates for each potential cut-off value is depicted graphically by the ROC curve. When analyzing ROC curves, the AUC measures how well a prediction system can predict the accurate occurrence or non-occurrence of pre-defined ‘events’.

As mentioned in the methodology, the selected 12 thematic layers for GWPZ have been created in the GIS platform and each parameter has been ranked based on the AHP and MIF. Using the methodology proposal, the twelve thematic maps were prepared and the hydrogeological setting of the study area was made. The rating and weight system have been calculated based on the influence of groundwater availability.

The studied region is a part of the Chandi and Gunderdehi formation of the Raipur group. Shale makes up a sizable portion of the studied area lithologically. Additionally, Laterite is also found in the vicinity. Unconsolidated alluvium-containing Quaternary deposits are only occasionally observed. Shale and limestone make up the majority of the region's aquifers from a hydrogeological standpoint. High weight is given to alluvium and unconsolidated sediments based on the properties of the rock.

Using the natural breaks approach, subcategorization of the density map was done into four groups: extremely low, low, moderate, and high (Chen et al. 2013). High weight and low weight are given to classes with high and low densities, respectively. Two major elements and one minor influencing element are present in the resulting MIF weight% is 09, and the AHP-derived weight% is 15. The lineament density of the area overlaid with a lineament feature is shown in Figure 3.

The plains typically consist of gentle sloping terrain. The primary geomorphic features in the region are Pediplains and Lateritic plains. This portion shows a greenish red tone and regular texture in the satellite imagery and good potential zones for groundwater exploration and development. The lateritic upland region is a very tough and undulating terrain. Darkish red to crimson skeletal soil is present in the parts of lateritic upland that have been divided by rills, gullies, and other erosional processes (Das 2017). The groundwater recharge capacity of the upland plain is comparatively low, but localized low-lying areas are favorable (Das & Pal 2019). Figure 3 illustrates the geomorphology of the Nava Raipur area. The high weight is assigned for buried pediplains and water bodies; the moderate weight is assigned for pediplain the low weight for lateritic upland. The derived AHP weight % is 14 and the MIF weight is 8, which has two major influencing factors.

A water body, forest with scrub land, and agricultural land are present in the region. Figure 3 illustrates the LULC of the study area. Water percolation is less affected by built-up areas, whereas groundwater recharge is influenced by vegetation cover due to water absorption. Compared to agricultural land, forest plantations have excellent groundwater potential while scrub land and built-up areas have poor potential. Water bodies had the highest ranking, followed by agricultural land, forests, and built-up areas, which received the lowest rankings. With one major and two minor influencing factors, the calculated MIF weight is 9, and the calculated AHP weight is 11.

The duration of the wetness of soil and its permeability are evaluated for estimating the rate of infiltration and are determined by the soil texture and hydraulic parameters. The study region includes areas that are unevenly, moderately, well, and slightly excessively drained. The majority of the territory is made up of agricultural land regions with moderately drained soil. Well-drained soils are given more weight, whereas inadequately drained soils are given less weight. Figure 3 depicts the soil map of the new capital region. Two minor impacting elements are seen in the resulting MIF weight of 8 and the resultant AHP weightis 11.

Most of the drainage derived from the lateritic uplands in the central part of the study area is in a dendritic pattern and developed locally. Four categories – very low, low, medium, and high – are used to categorize the drainage density (Figure 3). In areas with high relief and slope, where there is more runoff and less recharge, first-order and second -order streams are associated with low to moderate suitability. High weights are allocated for low densities in groundwater potential zonation, whereas low weights are allotted for high densities. With five minor affecting factors, the MIF weight is 9 and the resulting weight by AHP is 10.

The prepared rainfall layer has been subcategorized into low, moderate, and high rainfall, based on the intensity values (Figure 4). The amount of infiltration varies with rainfall quantity and duration. Low-intensity rain over a long period affects higher infiltration than runoff, while high-intensity rain over a short period influences less infiltration and greater surface runoff (Ibrahim-Bathis & Ahmed 2016; Arulbalaji et al. 2019). High rainfall receives higher weights vice versa. AHP weight% is 5, and MIF weight is 6, with one major and one minor influencing element.

Four categories, including very low, low, moderate, and high, were created using the TWI values. Aquifers are more likely to be found where the TWI is higher. For high TWI, high weights have been assigned vice versa. With one major and two minor influencing factors, the MIF weight is 8 and the calculated AHP weight is 5. The TWI map of the study region is depicted in Figure 4.

The higher the roughness, the more the undulation, vice versa. Figure 4 illustrates the roughness index of the region and the values were reclassified into very low, low, moderate, and high. The high weights are assigned for low roughness values vice versa. The derived AHP weightis 3 and the MIF weight is 8, which has one major and two minor influencing factors.

Using the TPI to characterize the study area into very low, low, moderate, and high. The high weights are allotted for low TPI values vice versa. Low TPI values are given high weights vice versa. AHP weight is 3, while the MIF weight is 8, with one major and two minor influencing elements. The classified TPI map of the study area is exemplified in Figure 4.

The four classifications of curvature – very low, low, moderate, and high – represent the morphology (Figure 4). High curvature values are given more weight vice versa. There are one major and two minor contributing elements in the MIF weight of 8, which is much higher than the resulting AHP weight of 3.

Table 7 shows the results of the MIF and AHP weight scores of all parameters, in the AHP section weighted scores are assigned from 3 to 15, having a high range but in the MIF section scores are assigned from 6 to 13 where 6 is assigned to rainfall and 13 is assigned to lithology. In AHP lineaments were assigned a maximum score of 15, and Roughness, TPI, and Curvature were assigned a minimum score of 3. AHP and MIF were performed with different influence scores but the same ranking of parameters. The results of both analyses are compared in Table 8. The AHP method classified the GWPZs as very low (18.06%), low (33.36%), moderate (31.90%), and high (16.67%). According to the classification made using the MIF approach, 25.30% of the research region has extremely low recharge potential, and 36.90% is categorized as low while the remaining Raipur area has moderate (27.05%), and high potential (10.75%). The comparison reflects that the change in area in a particular zone, in AHP very low, and the low range covers more area than the area obtained from MIF. From the MIF analysis, moderate and high ranges lay on comparatively larger areas than the AHP analysis. The very low range of GWPZ has maximum change from MIF to AHP. This study exhibits that in the MIF technique, the weights of the 11 elements, with the exception of lithology, are about similar; however, in the AHP technique, the weights of lithology, geomorphology, LULC, soil, and drainage density are greater than those of the other 7 factors. The precision of both approaches ultimately depends on the classification criteria, mean rating score, and weights assigned to the thematic layers. While the precision of the GWPZ maps produced by both the MCDM approaches was satisfactory, the validation phase indicates that the results of MIF with an accuracy of 82.9% outperform AHP which has an accuracy of 75.1%.

Sensitivity analysis performed in this study using Equation (7) by removing all parameters one by one to identify the sensitivity of every parameter influences the GWP Zone. The effective weightage of each parameter was then estimated accordingly at their minimum (Min), maximum (Max), mean, and standard division (SD) and represented in Table 9. The results show that the EW of every parameter was in near range with each other and that the influences of each parameter are important and affect the GWPZ map. Also, map removal sensitivity analysis reflects that the EW of Lithology and Geomorphology is different and all other parameters are the same in MIF and AHP (values in bold in Table 9). Thus, based on the map removal sensitivity analysis method, it is not possible to identify the most influential parameter for GWPZ.

To understand the most influential parameter for GWPZ in this study, using Equation (8) impact of the parameter on GWPZ is estimated. The results show the negative and positive values where the negative value illustrates a decrease in the influence of the parameter after removal and the positive value illustrates an increase in the influence of the parameter after removal in different Zones. In the MIF zone, the removal of the parameter reflects an indirectly proportional relation, i.e. increase in area in a very low zone shows a decrease in area in the high zone that can be shown most predominantly in parameters, such as lineament, LULC, drainage density, rainfall, TWI (values in bold in Table 10). After the removal of lithology, very low zone decreases and the high zone increases. The results reflect that the removal of lineament and slope increases very low and high zone. The results of this analysis in the AHP method reflect the decrease in the high zone after the removal of the lineament and the decrease in the very low zone after the removal of geomorphology. The analysis of the percentage change in the zone with respect to parameters shows the most influential parameters such as lineament and slope for the MIF zone and also lineament and geomorphology for the AHP zone.

In the recent past, the growing climate change and transformation of green cover into urban areas have posed a threat to natural water supply, which will have a direct impact on uncontrolled water demand from the large population for emerging cities such as Nava Raipur. Under the development of sustainable groundwater management, there were several practical, accessible, and sustainable methods to determine groundwater capacity and depletion in any area. However, the advent of geospatial and survey technologies has made it possible to quickly and affordably locate groundwater occurrences and delineate their prospect areas. By integrating various spatial data of variables that regulate groundwater (e.g. geology, topography, weather conditions, human intervention, etc.) using advanced geospatial techniques attractive real earth information can be created showing locations with scarce and potential groundwater. Therefore, many studies and research have been done on groundwater assessment and its vulnerability last decade, using different statistical techniques. The current study attempted to determine groundwater potential zones using an integrated approach consisting of MCDA based on geospatial technique with AHP and multi-influencing factor (MIF) techniques. Twelve contextually significant regulating environmental factors, i.e. lithology, geomorphology, LULC, soil, lineament, drainage, slope, rainfall, TWTW, roughness, topographic positional index, and curvature, were considered. In this study, an attempt was made to determine the importance of the decision-making approach model and their impact through sensitivity analysis for the suitability of the factors, which may be significant in the decision of giving correct weightage to the factors and their subclasses.

The GWPZs are categorized as extremely low (18.06%), low (33.36%), moderate (31.90%), and high (16.67%) using the AHP technique. The research region is classified as having extremely low recharge potential in 25.30% of the area, and low potential in 36.90% of the area, based on the MIF approach. The remainder Raipur area is classified as having moderate potential in 27.05% of the area, and high potential in 10.75% of the area. The final GWPZ map obtained from a combination of thematic layers was verified using ROC and AUC with discharge (yield) records taken during field investigation from 21 bore wells. The cross-validation results indicated that MIF was a more effective technique (accuracy = 0.829) for identifying potential groundwater zones for the Nava Raipur. In contrast, the AHP method that extracts groundwater potential mapping in the area with a reasonable level of effectiveness (accuracy = 0.751). Nonetheless, the results of GWPZ suggest that there is ample opportunity for groundwater management and planning perspective for artificial recharge to mitigate groundwater availability in the region as the study area has a high percentage of the low potential zone, and the low percentage of the high potential zone. To develop the outcome, this study also suggests enclosing additional topographical components to determine the occurrence of groundwater. The overall result concludes that for GWPZ creation, using two MCDA approaches is more suitable for helping in the process of decision-making. The overall results conclude that the use of different MCDM approaches and their integration for sustainable management of water resources is more suitable to help in the decision-making process.

The authors are thankful to the Director General, CCoST, Raipur, India for providing data support to carry out this study. They also thank the Geoscience division of the National Remote Sensing Center, Hyderabad, India for making available their useful data related to this study.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.