In the recent past, the growing climate change and transformation of the green cover into urban areas have posed a threat to natural water supply, which will have a direct impact on water demand for emerging cities such as Nava Raipur. As a result, the increasing demand coupled with the reduced availability of surface water prompts scientific investigation into groundwater availability and its sustainable management as an alternative. The study attempted to determine groundwater potential zones using the analytic hierarchy process (AHP) and multi-influencing factor (MIF) techniques. Twelve contextually significant regulating environmental factors were selected, and their significance and influences on decision-making approaches models have been attempted to determine through the sensitivity analysis. The final GWPZ map obtained, from a combination of thematic layers, was verified using the receiver operating curve (ROC) and the area under curve (AUC) with discharge (yield) records taken from 21 bore wells. According to the ROC curve's AUC estimation, MIF can explain 82.9% of the actual groundwater situation in the region, and for AHP, an AUC value of 0.751 is relatively low. This indicates that the MIF model is the most appropriate to accurately define potential groundwater zones for emerging cities like Nava Raipur.

  • AHP and MIF methods of the MCDA approach have been applied for groundwater potential zone mapping.

  • The GWPZ maps are validated with field observed yield data of 21 wells.

  • The conventional validation approach has been changed using ROC and AUC approaches.

  • To understand the weightage or sensitivity of each thematic layer, sensitivity analysis was performed.

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.

Study area

Nava Raipur (Atal Nagar) lies approximately 21°04′00″ to 21°14′50″ north latitude and 81°41′35″ to 81°52′50″ east longitude as illustrated in Figure 1. Atal Nagar is at an altitude of 298.15 m above sea level and the development plan covers a total area of 237.032 km2, out of which the core area alone spreads over 80.13 km2. The targeted population for the city will be 5.6 lakhs by 2031. The plan for 2031, prepared in 2008, includes 41 revenue villages (Jhariya et al. 2021; Jaiswal et al. 2023; Sinha et al. 2023).
Figure 1

Location map of the study area.

Figure 1

Location map of the study area.

Close modal

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.

List of data used, their sources and utility

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.

Table 1

Description of data used in this study

SNo.DataDescriptionUsedSource
Lithology Extracted from DRM To analyze the rock characteristics of the study Geological Survey of India 
Soil types Soil data prepared through soil map published by the Indian Council of Agricultural Research To determine the texture class National Bureau of Soil Survey-Land Use Planning, Nagpur 
Geomorphology Extracted from CARTOSAT 1 Imagery of 2.5 m resolution To analyze the geological features of the study area Chhattisgarh Council of Science and Technology, Raipur 
Land use/land cover 
Drainage Network 
Lineaments 
Rainfall Annual Average rainfall data for 2018 is used To understand the behavior of infiltration and runoff India Meteorological Department, Raipur 
Digital elevation model CartoDEM of 30 m resolution To prepare the following layers of the study area: slope, curvature, roughness index, topographic position index, and topographic wetness index. Bhuvan 
SNo.DataDescriptionUsedSource
Lithology Extracted from DRM To analyze the rock characteristics of the study Geological Survey of India 
Soil types Soil data prepared through soil map published by the Indian Council of Agricultural Research To determine the texture class National Bureau of Soil Survey-Land Use Planning, Nagpur 
Geomorphology Extracted from CARTOSAT 1 Imagery of 2.5 m resolution To analyze the geological features of the study area Chhattisgarh Council of Science and Technology, Raipur 
Land use/land cover 
Drainage Network 
Lineaments 
Rainfall Annual Average rainfall data for 2018 is used To understand the behavior of infiltration and runoff India Meteorological Department, Raipur 
Digital elevation model CartoDEM of 30 m resolution To prepare the following layers of the study area: slope, curvature, roughness index, topographic position index, and topographic wetness index. Bhuvan 

Preparation of the GWPZ map using AHP and MIF with geospatial technology involved five major steps: (1) building a spatial database (12 thematic data, including lithology, geomorphology, LULC, soil, lineament, drainage, slope, rainfall, TW, roughness, topographic positional index, and curvature, were gathered, interpreted, delineated, and input into a spatial model); (2) the integration of thematic layers for the control factor and these contributing elements' relationship is weighted as to how they influence the occurrence of groundwater based on AHP and MIF analyses as per the previous opinions of experts. (3) Geospatial application of weights to generate a GWPZ map; (4) sensitivity analysis of all parameters to analyse their influence. (5) Validation of the GWPZ map using existing groundwater well inventory. All five major steps are shown in Figure 2.
Figure 2

Flow chart illustrating technical process incorporated in this study.

Figure 2

Flow chart illustrating technical process incorporated in this study.

Close modal

Selection and preparation of groundwater influencing factors

Lithology

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

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).

Geomorphology

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.

Landuse/landcover

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).

Soil types

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.

Drainage density

In assessing recharge levels, drainage networks are helpful. To properly comprehend the environment of the drainage system, we have prepared a drainage map and then computed the drainage density (Shahid et al. 2010; Khanday & Javed 2017). The drainage density in the area has been calculated after the digitization of the entire drainage pattern. The drainage density is calculated from the following equation where the total stream length divided by the total basin area was calculated as:
formula
(1)

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

Slope

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).

Rainfall

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).

Topographic wetness index

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).

In this study, the TWI map was prepared by CartoDEM data in the GIS platform by the raster calculator tool. TWI is calculated from the following equation:
formula
(2)
where As is the cumulative up slope area draining through a point (per unit contour length) and tanβ is the slope angle at the point.

Roughness index

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.

Topographic position index

The topographic position index (TPI) is a commonly used algorithm for creating a multi-scale undulation index to measure slope positions and classify landforms by GRID (Weiss 2001; Mokarram et al. 2015; Arulbalaji et al. 2019; Benjmel et al. 2020). TPI assesses each cell of surface model elevation in relation to the average height of the neighboring elevation (Equation (3)). The elevation value at center is deducted from the mean elevation value.
formula
(3)
where M0 is the elevation of the model point under evaluation, Mn is the elevation of the grid, and n is the total number of surrounding points employed in the evaluation.

Curvature

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.

Multi-influencing factor

To assign ranks to each of the criterion subclasses, the multi-influencing factor (MIF)-based statistical method was used to establish the weights of each criterion and appraised by previous research methodologies. Previous research suggests that MIF is viable for understanding factors affecting groundwater potential (Magesh et al. 2012; Dinesan et al. 2015; Razandi et al. 2015; Thapa et al. 2017; Thomas & Duraisamy 2018; Abijith et al. 2020). The MIF is an effective and rapid method wherein the weights for each criterion, according to its strength, can be determined effectively with high precision. Based on empirical knowledge and practices, relationships were established between the ranking of criteria classes and subclasses. Criteria with highest influence as a major effect were assigned a weight of 1.0 whereas slight influence was marked as a minor effect with a weight of 0.5. The cumulative sum of all major and minor effects is added to determine the proportional rates and relative rates for each criterion. Equation (4) has been used to determine the recommended score for each influencing factor, considering the total weight of all major and minor effects.
formula
(4)

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

Analytical hierarchical process

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).

The degree of departure from pure inconsistency produced randomly is measured by the consistency ratio (CR) for the matrix from the following equation:
formula
(5)
where RI is the random index (values depend on the order of the matrix), whose values were obtained from Saaty's standard and CI is a consistency index. The CI is obtained using the following equation:
formula
(6)

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).

Sensitivity analysis

In this study, 12 thematic layers have been used for extracting the GWPZ using MIF and AHP technique. To understand the weightage or sensitivity of each thematic layer, sensitivity analysis was performed in this study. Sensitivity analysis determines the effective weights to summarize priority order (Pichery 2014; Chomba et al. 2022). It is a technique for figuring out the sensitivity of each thematic layer, for AHP and MIF analysis. Selected 12 layers are arranged in 1–12 order based on literature review for overlay analysis, for that to check and verify the arranged order, sensitivity analysis was performed also the impact on each layer on obtained GWPZ individually on the verge of analysis. The map removal technique (removed one by one parameter to extract GWPZ) is applied for sensitivity analysis. In this analysis, the effective weights, after removing parameters one by one, were calculated from Equation (7), which makes understanding of each layer according to the arranged order for the AHP and MIF analysis.
formula
(7)
where EW is the effective weight of each parameter; R is the rating of the parameter; W is the weightage of the parameter; V is the index aggregate value.
To estimate the impact of each parameter on the individual GWP Zone after the exclusion of parameter, Equation (8) is used by Ajay Kumar et al. (2020). This gives the area percentage of specific parameters in a specific zone of GWPZ.
formula
(8)
where Δ is the area change in % of the parameter; x represents the selected zone; y represents the removed parameter; represents the area of the x-zone after the removal of the y parameter; represents the area of the x-zone without the removal of any parameter.

Validation of the resulting GWPZ using ROC and AUC approaches

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.

Discussion on prepared thematic layers

Lithology

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.

Laterite in the area is given a moderate weight, whereas limestone and shale are given low weights. Laterite groundwater-bearing zones are around 5–10 m thick, limestone is about 15 m thick, and shale is about 5–15 m thick. The calculated AHP weight is 13 and the MIF weight is also 13 from three major and one minor influencing factors. Figure 3 shows the lithological setup of the area.
Figure 3

Thematic maps derived from visual interpretation.

Figure 3

Thematic maps derived from visual interpretation.

Close modal

Lineaments

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.

Geomorphology

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.

Landuse/landcover

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.

Soil type

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.

Drainage density

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.

Slope

The gradient of the slope is almost flat throughout the region. Three classifications of slope maps have been established: moderate, genitalia, and flat (Figure 4). Compared to regions with moderate slopes, areas with flat and gentle slopes are more conducive to recharge because they limit runoff, which, in turn, aids in the process of recharging. Due to higher recharge capacities, higher weights have been ascribed to somewhat lower slopes, vice versa. With one major and two minor influencing factors, the MIF weight is 8 and the resultant AHP weight% is 7.
Figure 4

Thematic maps derived from mathematical modeling.

Figure 4

Thematic maps derived from mathematical modeling.

Close modal

Rainfall

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.

Topographic wetness index

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.

Roughness index

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.

Topographic position index

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.

Curvature

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.

Results of MIF

According to the major and minor effects of parameters, resulting in multi-influencing groundwater parameters with their weighted scores are illustrated in Table 2. Each connection's weights are established and weighted by the efficiency of the various influence characteristics. After assigning the weighted score, the parameters are overlaid on each other in a geographical information system (GIS) environment. The result of the overlay analysis is shown in Figure 5. The result shows the four ranges or zones of GWPZ, i.e. very low, low, moderate, and high. Range and area of zones are shown in Table 3. Area of low zone observed maximum area and the high zone observed minimum area.
Table 2

Multi-influencing factor (MIF) matrix table of 12 thematic layers

ThemeMajor effect (X)Minor effect (Xi)Relative rates (X + Xi)Weight score
Lithology 1, 1, 1 0.5 3.5 13 
Lineament 1, 1 0.5 2.5 
Geomorphology 1, 1  
LULC 0.5, 0.5 2.5 
Soil 1, 1  
Drainage density  0.5, 0.5, 0.5, 0.5, 0.5, 0.5 2.5 
Slope 0.5, 0.5 
Rainfall 0.5 1.5 
TWI 0.5, 0.5 
Roughness 0.5, 0.5 
TPI 0.5, 0.5 
Curvature 
Total 16 10.5 26.5 100 
ThemeMajor effect (X)Minor effect (Xi)Relative rates (X + Xi)Weight score
Lithology 1, 1, 1 0.5 3.5 13 
Lineament 1, 1 0.5 2.5 
Geomorphology 1, 1  
LULC 0.5, 0.5 2.5 
Soil 1, 1  
Drainage density  0.5, 0.5, 0.5, 0.5, 0.5, 0.5 2.5 
Slope 0.5, 0.5 
Rainfall 0.5 1.5 
TWI 0.5, 0.5 
Roughness 0.5, 0.5 
TPI 0.5, 0.5 
Curvature 
Total 16 10.5 26.5 100 
Table 3

GWPZ description obtained from MIF

RangeClassArea (km2)Percentage area MIF (%)
292–433 Very low 59.50 25.30 
434–575 Low 86.77 36.90 
576–717 Moderate 63.60 27.05 
718–859 High 25.28 10.75 
RangeClassArea (km2)Percentage area MIF (%)
292–433 Very low 59.50 25.30 
434–575 Low 86.77 36.90 
576–717 Moderate 63.60 27.05 
718–859 High 25.28 10.75 
Figure 5

Resulting groundwater potential zones map using the MIF method.

Figure 5

Resulting groundwater potential zones map using the MIF method.

Close modal

Results of AHP

The pairwise comparison matrix shows the weights of all 12 parameters in Table 4, which decide the influence factor for the overlay analysis. The obtained weightage of all parameters is validated according to Saaty's CI and CR. The criteria for acceptability of pairwise comparison matrix CR value are less than 0.1 for each parameter as shown in Tables 4 and 5, which is acceptable. After the validation of assigned weightage to parameters from AHP ranking was assigned through a pairwise comparison matrix, the parameters were overlaid on the GIS environment that gives the GWPZ Map (Figure 6). The results in Table 6 illustrate the four GWP zones and their range. Figure 6 gives the pictorial view of zones in different colors. The result shows that the low zone covers the maximum area and the high zone covers the minimum area which is the same as a result of the MIF analysis but the range of zones is different and their covering area is also.
Table 4

Pairwise comparison matrix table of 12 thematic layers

Theme
LithologyLineamentGeomorphologyLULCSoilDrainage densitySlopeRainfallTWIRoughnessTPICurvatureGeometric meanNormalized weight
Assigned weight898676433222
Lithology 8 1.000 0.889 1.000 1.333 1.143 1.333 2.000 2.667 2.667 4.000 4.000 4.000 1.853 0.133 
Lineament 9 1.125 1.000 1.125 1.500 1.286 1.500 2.250 3.000 3.000 4.500 4.500 4.500 2.085 0.129 
Geomorphic 8 1.000 1.125 1.000 1.333 1.143 1.333 2.000 2.667 2.667 4.000 4.000 4.000 1.890 0.133 
LULC 6 0.750 1.500 0.750 1.000 0.857 1.000 1.500 2.000 2.000 3.000 3.000 3.000 1.487 0.100 
Soil 7 0.875 0.778 0.875 1.167 1.000 1.167 1.750 2.333 2.333 2.333 3.500 3.500 1.568 0.119 
Drainage density 6 0.750 0.667 0.750 1.000 0.667 1.000 1.500 2.000 2.000 3.000 3.000 3.000 1.361 0.100 
Slope 4 0.500 0.444 0.500 0.667 0.571 0.667 1.000 1.333 1.333 2.000 2.000 2.000 0.927 0.067 
Rainfall 3 0.375 0.333 0.375 0.500 0.429 0.500 0.750 1.000 1.000 1.500 1.500 1.500 0.695 0.050 
TWI 3 0.375 0.333 0.375 0.500 0.429 0.500 0.750 1.000 1.000 1.500 1.500 1.500 0.695 0.050 
Roughness 2 0.250 0.222 0.250 0.333 0.286 0.333 0.500 0.667 0.667 1.000 1.000 1.000 0.463 0.035 
TPI 2 0.250 0.222 0.250 0.333 0.286 0.333 0.500 0.667 0.667 1.000 1.000 1.000 0.463 0.033 
Curvature 2 0.250 0.222 0.250 0.333 0.286 0.333 0.500 0.667 0.667 1.000 1.000 1.000 0.463 0.033 
Total  7.50 7.74 7.50 10.00 8.381 10.00 15.00 20.00 20.00 28.83 30.00 30.00   
Theme
LithologyLineamentGeomorphologyLULCSoilDrainage densitySlopeRainfallTWIRoughnessTPICurvatureGeometric meanNormalized weight
Assigned weight898676433222
Lithology 8 1.000 0.889 1.000 1.333 1.143 1.333 2.000 2.667 2.667 4.000 4.000 4.000 1.853 0.133 
Lineament 9 1.125 1.000 1.125 1.500 1.286 1.500 2.250 3.000 3.000 4.500 4.500 4.500 2.085 0.129 
Geomorphic 8 1.000 1.125 1.000 1.333 1.143 1.333 2.000 2.667 2.667 4.000 4.000 4.000 1.890 0.133 
LULC 6 0.750 1.500 0.750 1.000 0.857 1.000 1.500 2.000 2.000 3.000 3.000 3.000 1.487 0.100 
Soil 7 0.875 0.778 0.875 1.167 1.000 1.167 1.750 2.333 2.333 2.333 3.500 3.500 1.568 0.119 
Drainage density 6 0.750 0.667 0.750 1.000 0.667 1.000 1.500 2.000 2.000 3.000 3.000 3.000 1.361 0.100 
Slope 4 0.500 0.444 0.500 0.667 0.571 0.667 1.000 1.333 1.333 2.000 2.000 2.000 0.927 0.067 
Rainfall 3 0.375 0.333 0.375 0.500 0.429 0.500 0.750 1.000 1.000 1.500 1.500 1.500 0.695 0.050 
TWI 3 0.375 0.333 0.375 0.500 0.429 0.500 0.750 1.000 1.000 1.500 1.500 1.500 0.695 0.050 
Roughness 2 0.250 0.222 0.250 0.333 0.286 0.333 0.500 0.667 0.667 1.000 1.000 1.000 0.463 0.035 
TPI 2 0.250 0.222 0.250 0.333 0.286 0.333 0.500 0.667 0.667 1.000 1.000 1.000 0.463 0.033 
Curvature 2 0.250 0.222 0.250 0.333 0.286 0.333 0.500 0.667 0.667 1.000 1.000 1.000 0.463 0.033 
Total  7.50 7.74 7.50 10.00 8.381 10.00 15.00 20.00 20.00 28.83 30.00 30.00   
Table 5

Consistent judgment matrix of 12 thematic layers

Theme
LithologyLineamentGeomorphologyLULCSoilDrainage densitySlopeRainfallTWIRoughnessTPICurvatureWeighted sumCriteria weightλ
Assigned Weight898676433222
Lithology 8 0.132 0.132 0.135 0.144 0.129 0.129 0.132 0.133 0.133 0.132 0.132 0.132 1.597 0.132 12.050 
Lineament 9 0.149 0.149 0.152 0.162 0.145 0.146 0.149 0.150 0.150 0.149 0.149 0.149 1.796 0.149 12.050 
Geomorphic 8 0.132 0.168 0.135 0.144 0.129 0.129 0.132 0.133 0.133 0.132 0.132 0.132 1.632 0.135 12.084 
LULC 6 0.099 0.224 0.101 0.108 0.097 0.097 0.099 0.100 0.100 0.099 0.099 0.099 1.322 0.108 12.198 
Soil 7 0.116 0.116 0.118 0.126 0.113 0.113 0.116 0.117 0.117 0.077 0.116 0.116 1.359 0.113 12.069 
Drainage density 6 0.099 0.099 0.101 0.108 0.075 0.097 0.099 0.100 0.100 0.099 0.099 0.099 1.176 0.097 12.063 
Slope 4 0.066 0.066 0.068 0.072 0.065 0.065 0.066 0.067 0.067 0.066 0.066 0.066 0.798 0.066 12.050 
Rainfall 3 0.050 0.050 0.051 0.054 0.048 0.049 0.050 0.050 0.050 0.050 0.050 0.050 0.599 0.050 12.050 
TWI 3 0.050 0.050 0.051 0.054 0.048 0.049 0.050 0.050 0.050 0.050 0.050 0.050 0.599 0.050 12.050 
Roughness 2 0.033 0.033 0.034 0.036 0.032 0.032 0.033 0.033 0.033 0.033 0.033 0.033 0.399 0.033 12.050 
TPI 2 0.033 0.033 0.034 0.036 0.032 0.032 0.033 0.033 0.033 0.033 0.033 0.033 0.399 0.033 12.050 
Curvature 2 0.033 0.033 0.034 0.036 0.032 0.032 0.033 0.033 0.033 0.033 0.033 0.033 0.399 0.033 12.050 
Theme
LithologyLineamentGeomorphologyLULCSoilDrainage densitySlopeRainfallTWIRoughnessTPICurvatureWeighted sumCriteria weightλ
Assigned Weight898676433222
Lithology 8 0.132 0.132 0.135 0.144 0.129 0.129 0.132 0.133 0.133 0.132 0.132 0.132 1.597 0.132 12.050 
Lineament 9 0.149 0.149 0.152 0.162 0.145 0.146 0.149 0.150 0.150 0.149 0.149 0.149 1.796 0.149 12.050 
Geomorphic 8 0.132 0.168 0.135 0.144 0.129 0.129 0.132 0.133 0.133 0.132 0.132 0.132 1.632 0.135 12.084 
LULC 6 0.099 0.224 0.101 0.108 0.097 0.097 0.099 0.100 0.100 0.099 0.099 0.099 1.322 0.108 12.198 
Soil 7 0.116 0.116 0.118 0.126 0.113 0.113 0.116 0.117 0.117 0.077 0.116 0.116 1.359 0.113 12.069 
Drainage density 6 0.099 0.099 0.101 0.108 0.075 0.097 0.099 0.100 0.100 0.099 0.099 0.099 1.176 0.097 12.063 
Slope 4 0.066 0.066 0.068 0.072 0.065 0.065 0.066 0.067 0.067 0.066 0.066 0.066 0.798 0.066 12.050 
Rainfall 3 0.050 0.050 0.051 0.054 0.048 0.049 0.050 0.050 0.050 0.050 0.050 0.050 0.599 0.050 12.050 
TWI 3 0.050 0.050 0.051 0.054 0.048 0.049 0.050 0.050 0.050 0.050 0.050 0.050 0.599 0.050 12.050 
Roughness 2 0.033 0.033 0.034 0.036 0.032 0.032 0.033 0.033 0.033 0.033 0.033 0.033 0.399 0.033 12.050 
TPI 2 0.033 0.033 0.034 0.036 0.032 0.032 0.033 0.033 0.033 0.033 0.033 0.033 0.399 0.033 12.050 
Curvature 2 0.033 0.033 0.034 0.036 0.032 0.032 0.033 0.033 0.033 0.033 0.033 0.033 0.399 0.033 12.050 
Table 6

GWPZ description obtained from AHP

RangeClassArea (km2)Percentage area AHP (%)
326–456 Very Low 42.48 18.06 
457–587 Low 78.46 33.36 
588–718 Moderate 75.02 31.90 
719–850 High 39.21 16.67 
RangeClassArea (km2)Percentage area AHP (%)
326–456 Very Low 42.48 18.06 
457–587 Low 78.46 33.36 
588–718 Moderate 75.02 31.90 
719–850 High 39.21 16.67 
Figure 6

Resulting groundwater potential zones map using the AHP method.

Figure 6

Resulting groundwater potential zones map using the AHP method.

Close modal

Comparison of results of MIF and AHP

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%.

Table 7

Summary of AHP and MIF weights for various thematic layers

ThemeDomain of effectRankMIF weightAHP weight
Lithology (LG) Laterite 13 13 
Limestone ad Dolomite 
Purple calcareous Shale 
Lineament (LM) Very low 15 
Low 
Moderate 
High 
Geomorphology (GM) Buried Pediplain Shallow 14 
Laterite plain Shallow 
Pediplain shallow 
Water bodies 
LULC (LL) Agriculture 11 
Built-up 
Forest 
Scrub land 
Water bodies 
Soil (SL) Clay 11 
Clay loam 
Gravelly sandy clay Loam 
Sandy loam 
Sandy clay loam 
Drainage density (DD) Very low 10 
Low 
Moderate 
High 
Very high 
Slope (SP) Very low 
Low 
Moderate 
High 
Very high 
Rainfall (RF) 900 
1,000 
1,200 
Topographic wetness index (TW) Very low 
Low 
Moderate 
High 
Roughness index (RN) Very low 
Low 
Moderate 
High 
Topographic position index (TP) Very low 
Low 
Moderate 
High 
Curvature (CV) Very low 
Low 
Moderate 
High 
ThemeDomain of effectRankMIF weightAHP weight
Lithology (LG) Laterite 13 13 
Limestone ad Dolomite 
Purple calcareous Shale 
Lineament (LM) Very low 15 
Low 
Moderate 
High 
Geomorphology (GM) Buried Pediplain Shallow 14 
Laterite plain Shallow 
Pediplain shallow 
Water bodies 
LULC (LL) Agriculture 11 
Built-up 
Forest 
Scrub land 
Water bodies 
Soil (SL) Clay 11 
Clay loam 
Gravelly sandy clay Loam 
Sandy loam 
Sandy clay loam 
Drainage density (DD) Very low 10 
Low 
Moderate 
High 
Very high 
Slope (SP) Very low 
Low 
Moderate 
High 
Very high 
Rainfall (RF) 900 
1,000 
1,200 
Topographic wetness index (TW) Very low 
Low 
Moderate 
High 
Roughness index (RN) Very low 
Low 
Moderate 
High 
Topographic position index (TP) Very low 
Low 
Moderate 
High 
Curvature (CV) Very low 
Low 
Moderate 
High 
Table 8

Comparison of results of MIF and AHP

ClassArea (km2) MIFPercentage area MIF (%)Area (km2) AHPPercentage area AHP (%) Change in area (km2)Change in percentage area (%)
Very low 59.50 25.30 42.48 18.06 17.03 7.24 
Low 86.77 36.90 78.46 33.36 8.31 3.54 
Moderate 63.60 27.05 75.02 31.90 −11.42 −4.85 
High 25.28 10.75 39.21 16.67 −13.92 −5.92 
ClassArea (km2) MIFPercentage area MIF (%)Area (km2) AHPPercentage area AHP (%) Change in area (km2)Change in percentage area (%)
Very low 59.50 25.30 42.48 18.06 17.03 7.24 
Low 86.77 36.90 78.46 33.36 8.31 3.54 
Moderate 63.60 27.05 75.02 31.90 −11.42 −4.85 
High 25.28 10.75 39.21 16.67 −13.92 −5.92 

Sensitivity analysis

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.

Table 9

Map elimination sensitivity analysis in % (one factor is eliminated at a time)

MIF (effective weight %)
AHP (effective weight %)
Parameter removedMinMaxMeanSDMinMaxMeanSD
Lithology 0.71 0.85 0.80 0.032 0.70 0.85 0.80 0.032 
Lineament 0.72 0.87 0.81 0.028 0.72 0.87 0.81 0.028 
Geomorphology 0.69 0.83 0.79 0.016 0.57 0.79 0.72 0.024 
LULC 0.68 0.87 0.79 0.023 0.68 0.87 0.79 0.023 
Soil 0.71 0.86 0.82 0.023 0.71 0.86 0.82 0.023 
Drainage density 0.71 0.87 0.79 0.027 0.71 0.87 0.79 0.027 
Slope 0.73 0.88 0.82 0.034 0.73 0.88 0.82 0.034 
Rainfall 0.72 0.85 0.80 0.013 0.72 0.85 0.80 0.013 
TWI 0.74 0.88 0.84 0.023 0.74 0.88 0.84 0.023 
Roughness 0.73 0.88 0.83 0.031 0.73 0.88 0.83 0.031 
TPI 0.74 0.88 0.85 0.016 0.74 0.88 0.85 0.016 
Curvature 0.70 0.87 0.79 0.021 0.70 0.87 0.79 0.021 
MIF (effective weight %)
AHP (effective weight %)
Parameter removedMinMaxMeanSDMinMaxMeanSD
Lithology 0.71 0.85 0.80 0.032 0.70 0.85 0.80 0.032 
Lineament 0.72 0.87 0.81 0.028 0.72 0.87 0.81 0.028 
Geomorphology 0.69 0.83 0.79 0.016 0.57 0.79 0.72 0.024 
LULC 0.68 0.87 0.79 0.023 0.68 0.87 0.79 0.023 
Soil 0.71 0.86 0.82 0.023 0.71 0.86 0.82 0.023 
Drainage density 0.71 0.87 0.79 0.027 0.71 0.87 0.79 0.027 
Slope 0.73 0.88 0.82 0.034 0.73 0.88 0.82 0.034 
Rainfall 0.72 0.85 0.80 0.013 0.72 0.85 0.80 0.013 
TWI 0.74 0.88 0.84 0.023 0.74 0.88 0.84 0.023 
Roughness 0.73 0.88 0.83 0.031 0.73 0.88 0.83 0.031 
TPI 0.74 0.88 0.85 0.016 0.74 0.88 0.85 0.016 
Curvature 0.70 0.87 0.79 0.021 0.70 0.87 0.79 0.021 

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.

Table 10

Zone category change (%) under the elimination of each factor

MIF (zone)
AHP (zone)
Parameter removedVery lowLowModerateHighVery lowLowModerateHigh
Lithology −1.9 0.7 0.1 0.4 9.8 2.7 −6.5 −3.7 
Lineament 6.3 16.3 −2.9 31.9 2.8 6.6 4.7 25.3 
Geomorphology 9.9 7.5 −3.2 −17.8 13.3 2.9 5.6 9.6 
LULC 47.8 11.6 −14.6 39.7 8.9 2.1 −4.8 −4.6 
Soil 28.1 3.4 −8.1 −17.6 0.5 2.9 −3.5 0.3 
Drainage density 44.4 19.0 −15.4 49.3 4.0 0.9 1.8 −9.5 
Slope 4.1 14.0 1.3 33.2 4.6 −2.6 0.5 9.2 
Rainfall 58.5 9.6 −17.9 39.8 0.2 −1.1 0.4 1.6 
TWI 30.6 16.9 −8.8 44.7 1.1 −1.2 −0.6 4.8 
Roughness 2.7 10.1 2.0 −25.6 −2.3 −2.1 1.7 3.6 
TPI 21.0 8.0 −5.1 −25.5 −4.1 −0.2 1.7 1.6 
Curvature 24.4 2.7 −6.9 −15.1 1.7 −0.5 −0.6 0.3 
MIF (zone)
AHP (zone)
Parameter removedVery lowLowModerateHighVery lowLowModerateHigh
Lithology −1.9 0.7 0.1 0.4 9.8 2.7 −6.5 −3.7 
Lineament 6.3 16.3 −2.9 31.9 2.8 6.6 4.7 25.3 
Geomorphology 9.9 7.5 −3.2 −17.8 13.3 2.9 5.6 9.6 
LULC 47.8 11.6 −14.6 39.7 8.9 2.1 −4.8 −4.6 
Soil 28.1 3.4 −8.1 −17.6 0.5 2.9 −3.5 0.3 
Drainage density 44.4 19.0 −15.4 49.3 4.0 0.9 1.8 −9.5 
Slope 4.1 14.0 1.3 33.2 4.6 −2.6 0.5 9.2 
Rainfall 58.5 9.6 −17.9 39.8 0.2 −1.1 0.4 1.6 
TWI 30.6 16.9 −8.8 44.7 1.1 −1.2 −0.6 4.8 
Roughness 2.7 10.1 2.0 −25.6 −2.3 −2.1 1.7 3.6 
TPI 21.0 8.0 −5.1 −25.5 −4.1 −0.2 1.7 1.6 
Curvature 24.4 2.7 −6.9 −15.1 1.7 −0.5 −0.6 0.3 

Validation

The available well yield data from RGNDWP were finally used to confirm the delineated groundwater potential map. On the final GWPZ map, the well yield data were overlaid. The well yield data and the results were very congruent. It was discovered that the high-well yield (100–200 LPM) and low-well yield (50 LPM to 50–100 LPM) zones correspond with the excellent to very good and bad to very poor GWPZ established in this study, respectively. The ROC curve is considered a graphical representation of the trade-off between the false-negative (X-axis) and false-positive (Y-axis) rates for every possible cut-off value. The quantitative–qualitative relationship between the AUC and prediction accuracy can be classified into poor (0.5–0.6), average (0.6–0.7), good (0.7–0.8), very good (0.8–0.9), and excellent (0.9–1). The ROC prediction curve indicates a trade-off between the two rates, as shown in Figure 7. The AUC values of ROC of MIF and AHP illustrate the explanation of the percentage area of the study area for GWPZ. Figure 7 shows the validation of the obtained GWPZ using the AUC value of ROC. The ROC curve's area under the curve (AUC) value for the AHP method was 0.751, which equates to a prediction accuracy of 75.10% (Figure 7), and for the MIF method, it was 0.829, which equates to an accuracy of 82.90% (Figure 7). As a result, AHP and MIF methods produced maps that accurately produced the groundwater potential zone. However, the MIF method results are better than the results obtained using the AHP method. The AHP method was more suitable than the MIF methods along with basin characteristics (Das 2017; Pande et al. 2021; Senapati & Das 2022) but this study demonstrates MIF is better than the AHP method for the urban region. The findings of this study demonstrate the usefulness of the MCDA method for mapping groundwater potential zones. The methods can also be used to identify acceptable well sites at a low cost, plan effectively for the exploitation of scarce groundwater, and ensure the growth of groundwater resources sustainably.
Figure 7

Validation result of observed groundwater yield vs GWPZ results.

Figure 7

Validation result of observed groundwater yield vs GWPZ results.

Close modal

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.

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