Introduction

A significant natural resource crucial for the survival of humans and the growth of aquatic and terrestrial ecosystems is GW. The earth’s crust’s water bearings (interconnected fractures, lineaments, etc.) serve as reservoirs as well as transmission conduits for water storage. Recently, the soil and water supply in India, along with certain other countries, has been diminishing daily due to inadequate replenishment. Consequently, it is imperative to effectively assess the supply to manage systems in the water-deficient area.

India is an agriculturally developing region, and of the total population, sixty to seventy percent relies on it. Rapid industrial and agricultural production, which has contributed to over-exploitation, is a significant factor in the depletion of the country’s GW supply. Surface water (SW) in hard rock aquifers is often restricted to fractured and weathered horizons. Because of the hidden nature beneath the ground, it is extremely important to identify the factors that influence groundwater in a hilly terrain. LULC, rainfall, slope, elevation, aspect, distance to drainage, and lineament are all included in a number of GIS integrated models1,2,3,4,5. However, depending on geographical, geological, and hydrological characteristics, the overall number of thematic layers for groundwater potentiality may differ from location to location6. The primary regulating factor for the incidence and flow of GW in a hard rock area is secondary porosity, which is initiated by fractures in the underlying rocks. GW is the primary source of water storage and is key in addressing water scarcity challenges7. It is an essential resource in many countries, providing drinking water, supporting irrigation, and supplying “businesses8,9,10,11. Due” to the limited availability of freshwater and the overuse of global GW reserves, a significant water crisis is projected by 202512,13,14. GW holds greater economic value than SW because it is more accessible, can respond flexibly to consumption patterns, remains reliable during droughts, and offers high-quality water15,16,17. Water scarcity is particularly pronounced in arid as well as semi-arid regions, which have experienced recurrent droughts in recent years due to inconsistent and insufficient RF6,18,19,20,21. The"GWPZ mapping method has been utilized in field investigations for decades, despite its potential for being both time-consuming as well as expensive13,22. However, advancements in technology merged GIS and Remote Sensing (RS) to simplify the collection, processing, and visualization of a variety of data types, thereby enabling the development of specialized maps13,23,24,25. Availability of GW differs across regions based on factors including hydrodynamics, rain, topography, GL, soil texture, as well as slope26. These parameters were thus incorporated into a GIS framework to generate GWPZ maps.

In this investigation, the variability in monsoon patterns, along with diverse physiographic conditions, leads to an uneven distribution of water, resulting in significant water scarcity in certain areas. Each summer, many SW sources dry up, leaving most agricultural lands reliant on GW from dug wells as well as shallow tube wells. The unregulated and excessive extraction of GW has caused many of these wells to run dry, severely impacting farmers’ livelihoods. So far, various conventional methods like geological, hydrogeological, geophysical, as well as photogeological techniques have been used to identify GWPZs27,28. Several workers, including29,30,31,32,33 and others have been” utilizing GIS methods to delineate GWPZ. Currently, the widespread use of satellite data, combined with traditional maps as well as accurate ground truth data, has significantly simplified the process of establishing baseline information for “ GWPZs34. The” advancement of powerful computers along with digital technology has enabled the integration of conventional methods with RS as well as GIS techniques, enhancing the accuracy of hydrogeological research as well as the identification of GWPZs in various regions. Additionally, numerous researchers, “including28,35,36,37,38,39,40,41, have utilized” both RS techniques as well as GIS applications for GW exploration, the delineation of GWPZs, along with the identification of artificial recharge sites.

A popular knowledge-based semi-quantitative technique for managing natural resources is the analytical hierarchy process (AHP)42. The weighting for both factors and domains obtained through a pairwise comparison matrix under a significant consistency ratio is the main benefit of the AHP method4,43. Several researchers have previously employed the AHP approach for groundwater potentiality delineation worldwide3,44,45,46 and Das 2021). According to the expert opinion and weight for the factors and domains, their outcome demonstrates a good prediction accuracy as measured by AHP.

The present research demonstrates the daily drop in groundwater levels as well as the effects of climate change on groundwater supplies. The rapid expansion of industrialization and population growth causes a rise in the supply and demand for freshwater. Because of its distinct geography, land use patterns, and climate, Ooty, which is in Tamil Nadu’s Nilgiri district, confronts many groundwater potential concerns. Low natural groundwater recharge is the result of the hilly terrain’s steep slopes and sparse soil cover, which hinder rainwater infiltration. Deforestation, uncontrolled development, and rapid urbanization have further hampered natural drainage systems and decreased the land’s ability to hold water. Furthermore, excessive groundwater extraction for residential, agricultural, and tourism-related uses has caused some regions’ water tables to drop. Sustainable water resource management is a critical issue for the area since seasonal variations in rainfall, especially during the monsoon months, can lead to irregular groundwater supplies.

In various areas of the Nilgris District, the long-term variations in water levels range from G.L. to 14.0 m. According to (National Water Mission and District Groundwater Brochure Nilgiri District) the analysis, the water level has decreased in the Nilgris District’s central, west, and north regions. The annual fluctuation leads to the conclusion that the phreatic aquifer’s groundwater levels are impacted by the absence of rainfall. According to the seasonal fluctuation study, the water level has decreased as a result of the monsoon’s failure and the requirement to develop ground water for various sectored purposes.

Delineating GWPZs utilizing RS and GIS is an efficient approach for evaluating, exploiting, and managing this valuable resource to ensure sustainable development in the region. To achieve this, it is crucial to understand the various rock types, hydrogeomorphic units, geological structures, and their characteristics and how hydrological properties evolve in different geomorphic settings. This knowledge is essential for applying an integrated RS and GIS-based methodology to assess the geohydrological conditions and identify GWP in Nilgri, Tamil Nadu (TN), India. The use of the Weightage Index Overlay method to combine RS data with GIS has advanced research, particularly in the areas of GW resource assessment, monitoring, and management.

Description of the study area

Nilgiri Hill stands among the smallest districts in TN. From an etymological perspective, the term“Nilgiri”refers to the Blue Mountains. Bordering Kerala and Karnataka, the Nilgiris are located in the northwestern region of TN. It is bordered to the west by Kerala’s Malabar region, Karnataka to the north, as well as Coimbatore to the east and south. The district covers the latitudes of 11° and 11°55″ north and the longitudes of 76°13″ and 77°02″ east. The district measures a total of 2719sqkm Fig. 1. Among the districts of TN, the district Nilgiris is ranked 27th regarding the population. The Nilgiris Plateau and the South East Land of Wayanad were the 2 distinct natural divisions of the district. Hills are the predominant feature of the plateau, with only a limited number of valleys. In addition, the plateau is further classified into 4: Peranganad (Far East), Merkunad (Northwest), Todanad (North), and Kundahnad (South East). The district’s region is situated at a lower elevation than the plateau and is characterized by expansive undulating forests, paddy fields, and numerous bamboo thickets, particularly in the Gudalur sector.

Fig. 1
figure 1

Location map of the study area. This figure was prepared using ArcGIS Desktop 10.7 (https://www.esri.com/en-us/arcgis/products/arcgis-desktop/overview).

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Materials and methods

Given erratic GW nature occurrences, it is both economical as well as crucial to use GIS along with AHP to rapidly characterize GW influencing factors along with potential zone delineation to identify the best borehole locations for the most practical GWP. Base maps, contour maps, drainage maps, along slope maps were generated using the Survey of India (SOI) topographical maps (58 A/15 and 58 A/11, 1:50,000 scale). Using ArcGIS’s geostatistical analyst tool, thematic layers, which include lineaments, geomorphic units, as well as LULC were delineated using satellite imagery from Indian RS LISS-III at “a 1:50,000 scale (geo-coded, with UTM projection, spheroid, as well as datum WGS 84, Zone 45 North). The geological map was developed with the aid of the District Resource Map of TN along with the quadrangle map 73 M (GSI 1985). The LUP and NBSS (Land Use Planning and National Bureau of Soil Survey 1992”) data was employed to generate a soil texture map. Through the utilization of spatial analysis tools in ArcGIS 10.8, GWPZs “are identified by overlaying all thematic maps using weighted overlay analysis (WAO). Throughout this procedure, each parameter from the thematic maps was ranked, as well as weights were assigned based on the MIF (Multi Influencing Factor) of each feature’s impact on the hydrogeological environment of the area under study33,47. The WAO of 10 influencing factors for GW occurrence along with distribution using GIS-based AHP methodology. Using a WIO technique, the weighted values of every thematic layer were added to construct the GW modeling map. Seven productive boreholes were added to the ArcGIS platform to validate it. The following is a clear illustration of the general processes used in this work. The methods of a flow chart are shown in Fig. 2

Fig. 2
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Methodological flowchart for the study area.

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Thematic map preparation

Lithology and groundwater potential

GIS databases were generated individually for various natural parameters, including Lithology, LD, DD, GM, Soil, Slope, as well as LULC, following the generation of the GIS database on the GWP. These databases generally assign varying GWP grades in mountainous regions.

Rainfall

The Southwest as well as the Northeast Monsoon are the 2 seasons of RF that the Nilgiris District experiences. The taluks of Gudalur, Pandalaur, and Kundah receive RF during the Southwest Monsoon, while the Northeast Monsoon impacts Udhagamandalam taluk, Coonoor, and Kotagiri. The region’s average annual RF varies between 1,500–3,000 mm. As shown in the map in Fig. 3a, the northeastern, southern, as well as eastern areas receive lower RF, whereas the highest RF occurs in the southern regions along with some parts of the west.

Fig. 3
figure 3figure 3

a) Rainfall, b) Geology, c) Geomorphology, d) Lineament density, e) Drainage density, f) Soil map, g) Slope, h) Land/Use land cover (Maps are created by using ArcGIS 10.7 software, https://desktop.arcgis.com/en/quick-start-guides/10.7/arcgis.desktop.quick.start.guide.htm).

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Geology

Lithologically, the Nilgiri Mountains are mostly formed by Charnockites and hornblend biotite gneiss, as shown in Fig. 3b. The Charnockites experienced a range of degrees of weathering. Charnockites classified the GL of the sampling area as moderately, relatively, as well as heavily weathered. These zones protected by 3 types of Charnockite are digitized using the ArcGIS Screen Digitalization technique as well as a geological map has been created.

In the Nilgiri district, the Charnockite group of rocks is on display, which “includes enclaves of the Satyamangalam Schist Complex. It is located in the southern region of the province, which is referred to as the"Nilgiri Massif."The Bhavani Group (Peninsular Gneissic Complex) is a collection of fissile hornblende biotite gneiss that is located in the northern region of the district. Quartzsericite/mica schist, ultramafics, along banded magnetite quartzite comprise the Satyamangalam Schist Complex. The Nilgiri Massif is capped by an aluminous laterite at several locations, which suggests a deep zone weathering (GSI, 2000). Most areas of the district rocks are heavily weathered as well as the soil thickness is estimated to be up to 40 m, with lithomage being a typical characteristic of the area. The low slope gradient observed in Ootacamund encourages retention of SW” such as bogs as well as swamps (GSI, 2000). Geologically, the majority of the area under study is composed of extensive deposits of weathered materials along with fractured rock. These formations have given rise to a significant aquifer unit, creating a GWPZ as a result of the weathering along fracturing processes.

Geomorphology

According to Swain (2015), geomorphology is the study of earth structures and aids in illustrating hereditary processes pertaining to groundwater potential zones and structural features. Identification and classification of diverse forms and structural features, water movement, freezing, and thawing are all part of geomorphological mapping48.

Furthermore, the data IRS P6 LISS-III utilized to map the lineaments were subjected to a variety of image processing methods, including false color composites, color composites, and contrast stretching of the key image component. The GIS database was based on the GM map Fig. 3c and a variety of geomorphic features were interpreted along vectorized as individual polygon classes. In the area under study, the geomorphic features that were interpreted in this manner include denudation hills, peneplain, piedmont zone, structural hills, and valley filling. The denudation of hill areas is evident in the presence of dusty red-to-red skeletal soil, which is followed by massive laterites. Additionally, the hill areas are dissected by rills, gullies, and erosion processes. Lateritic upland consisting mainly of brown latosol with iron nodules (disintegrated duricrust) underlain by a lateral hard pan. The peneplain consists mainly of unconsolidated, weathered material overburdened with thick alluvium or gravel larger than the shallow peneplain, which, when saturated, forms the reservoir of the aquifer. Valley filling is an aggregation region of colluvial materials originating from the nearby uplands, thin loamy to clay soils with a mild to thick soil layer. The soil is largely hydromorphic in nature and has very strong porosity as well as permeability, although often, the involvement of clay will render it impermeable.

The “Coimbatore plains(southeast), Bhavani plains(northeast), Moyar valley(north), as well as the Gudalur Plateau (northwest), surround the Nilgiri Hills, which rise from the nearby plains to an altitude of 1370 msl. Ooty Hills, Kodaibetta Hills, Bhavani Betta Hills, Devabetta Hills, along with Dodabetta Hills, are the most well-known hills. The highest point in TN is Dodabetta (GSI, 2000). Moyar is a famous river in the district and flows east along the northern border of the district. The drainage is dendritic to radial in areas with popular rapids, cascades, along” waterfalls.

Lineament and lineament density

Lineaments are circular defects, including cracks, joints, and other defects, that can be directly observed from satellite images. The research region’s lineaments are gathered and mapped by visually interpreting the satellite pictures. This is done based on tonal, textural, soil tonal, landscape, and topographic, along with drainage linearity, curvilinear links, as well as rectilinear connections49,50. Lineaments are indicative of faulting along with fractures in the rocks of the bed, which leads to an increase in secondary porosity along with permeability. These factors are of significant hydro-geological importance, as they can be anticipated to enhance well yields as well as provide pathways for the movement of GW51”. The distribution of LD in the investigation reveals linear features formed by tectonic activity, which reflect subsurface discontinuities52. These lineaments serve as major conduits for GW flow in impermeable formations. In rocky terrains, the movement and accumulation of GW largely depend on secondary porosity.

The tectonic “base of the fracture-controlled lineaments observed in the IRS-P6 LISS III Raw, along with FCC data, along with the DEM-packed FCC data, was mapped as well as field-tested based on tonal, textural, topographical, drainage, vegetation linearity, along curvilinearity. The LD diagram was generated from these lines 15 by superimposing a grid map with 4687 grids of 250 × 250 m2 each over the lineament map, measuring the total length of lineaments for each grid as well as plotting them in the corresponding grid centers. Based on the plotted values, linear density contours were formed. The variance in density from 0-400 m was grouped into 4 classes: 0–100(Low), 100–200(Moderate), 200–300(High), and > 300(Very High). The LD maps are illustrated in Fig. 3d.

Drainage and drainage density

The drainage pattern is indicative of both subsurface along surface formations. DD (measured in km/km2) indicates the spacing of channels along with the nature of surface materials53. Higher DD corresponds to increased runoff, which implies less rainwater infiltration. Conversely, areas with low DD are more likely to have higher infiltration rates, making them potential recharge zones54. The upper Nilgiri Hills exhibit the highest DD in the research area, while the lower part of the area under study is characterized by a lower DD, as illustrated in the map in Fig. 3e.

Soils

The recharge of GW is significantly influenced by the soil texture, which is a crucial factor in the quantity of water that infiltrates the soil. The rate of infiltration is significantly influenced by the soil texture along with its corresponding hydraulic characteristics. The major soil types in the Nilgiri district are ALFI SOLS, ENTI SOLS, HILL SOIL, and INCEPTISOLS. ALFI SOLS and ENTI SOLS cover the majority of the district. The HILL SOIL as well as INCEPTISOLS are present in small patches. Fig. 3f illustrates the Nilgiris district’s soil map with the names of local places.

Slope

The identification of GWPZ in any region is significantly influenced by the slope. Due to the rapid runoff that occurs during RF, steeper slopes result in a reduced amount of recharge, as the surface is not given enough time to infiltrate as well as recharge the saturated area33,55,56,57. The slopes of the area under study were classified into 4 categories, such as very steep (> 30°), steep (30–20°), moderate (20–10°) shallow (10–5°), along with rolling slopes (< 5 degrees) map shown in Fig. 3g by increasing the scale of the topographic sheets from 1:50,000 to 1:12,500. The distances between the two contours of 20 m were measured at intervals to prepare these 4 classes of slopes from the top sheet of 1:12,500 scales. The vector GIS layer was similarly generated to display these 4 polygon classes of slopes.

Land use/land cover

The raw as well as digitally processed IRS P6 LISS-III data, along with LULC features of the area under study, are interpreted to determine the LULC. Using ArcGIS, the following features were interpreted: cultivation land, forest land, grassland, settlements, water bodies, as well as wasteland and rock surface areas. A LULC map was generated, displaying all the classes featured in Fig. 3h.

Delineating the groundwater potential zone

RS and GIS techniques were employed to generate the GWPZs in the research by integrating a variety of thematic maps, such as DD, slope, GL, GM, LULC, LD, and soil. The WIO method is applied, where maps are analyzed based on a weighted combination, with respective thematic layers assigned a weight according to their influence. The resulting groundwater potential map (GWPM) (Fig. 4) shows that the best GWPZs are concentrated in valley fills, where lineament intersections along with medium to high LD are prominent. In the hard rock terrain of the southern region, lineaments act as direct conduits for GW movement, resulting in shallow water levels in wells near these features. However, in the lateritic upland region in the north, where lineaments are sparse, GWP is very low, making this area unsuitable for GW targeting Table 1.

Fig. 4
figure 4

Groundwater potential map (Maps are created by using ArcGIS 10.7 software https://desktop.arcgis.com/en/quick-start-guides/10.7/arcgis.desktop.quick.start.guide.htm).

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Table 1 Weightage of different classes for identification of groundwater prospect zone.
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Analytical hierarchical process

Analytical hierarchies are organized decision-making processes that use the expertise of experts to determine the rankings and weights by building an eigenvalue pairwise comparison matrix. This approach is most appropriate for decision-making in problems with multiple parameters influencing the outcome. This procedure entails building a pairwise matrix in which the relative relevance of each parameter was taken into account while determining the weights of each parameter58. The method’s key benefit is its ability to reduce inconsistencies and estimate the priority vector through pairwise comparison and decomposition44. The AHP method’s main need, a pairwise comparison matrix, was created as Eq. (1). Eight parameters (rainfall, geology, geomorphology, slope, lineament density, drainage density, LU/LC, and soil group) are considered when planning a town for groundwater potentiality zonation. Each of these thematic layers was given a weight based on the AHP analysis utilizing Satty’s mathematical technique after being compared pairwise. The thematic ranks.

Expert knowledge was used to assign layers according to Satty’s scale (Table 2), taking into account the layer’s influence on groundwater occurrence. Using the AHP technique, all of the theme layer elements were simultaneously ranked according to their impact on groundwater occurrence. Sub-features were given normalized weights, and the final weightages for features in each theme layer were calculated by multiplying the actual weights determined for the thematic layers by the corresponding normalized feature weight. The resulting matrix was tested for consistency if the consistency ratio was less than 0.1, and the resulting criteria weights were then employed for analysis. This approach gives the decision-maker a vast array of options. By looking at the consistency ratio, this method also enables checking for the subjectivity of the determined weights59. If the criterion is not satisfied, the matrix can be rebuilt using several options60.

Table 2 Fundamental scales of AHP (Saaty & Vargas, 1980).
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The AHP is the most prevalent GIS-based method utilized in identifying GWPZ. The AHP methodology was employed to construct the PCM. This process involved the comparison of criteria in pairs, which resulted in evaluations recorded in a comparison matrix by the recommendations of61. The significance of each criterion was evaluated about its peers on a scale of 1 to 9 (refer to Table 3). Consequently, the subsequent procedures were implemented to determine all thematic layers’ final weight62.

Table 3 RI and Saaty’s relative importance scale61.
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First, using Eq. 1. the values in all columns of the PCM are added.:

$$Lj={\sum }_{ij=1}^{n}Cij$$
(1)

In “the pair-wise matrix, Lj denotes the sum of the values in each column, while Cij denotes the number assigned to each factor in the ith row and jth column.

Secondly, employ Eq. 2 to generate a normalized pair-wise matrix by” dividing each element in the matrix by “its column total:

$$Xij=\frac{Cij}{Lj}$$
(2)

In the normalized pairwise matrix, Xij denotes the value in the ith row and jth column.

Finally, to produce standard weights, apply Eq. 3 to divide the sum of the normalized row of the matrix by the number of factors employed (8 in the current investigation):

$$wi =\frac{{\sum } Xij}{N}$$
(3)

Here Wi denotes the standard weight, and N denotes” the number of factors.

CR (consistency ratio) is calculated to find discrepancies as well as to determine the best weights after the PCM has been finished, the weights of its components are determined. Only when the CR was 0.1 or less did the AHP analysis move forward. However, inconsistencies in the review process could result in erroneous results if the consistency ratio was greater “than 0.1. To assess the dependability of the relative weights, a CR was calculated for the matrix by comparing the CI as well as the RI. Equation 4 was employed to calculate the Consistency Index (CI). This research’s randomized index (RI) is 1.41, as reported in Table 361.

$$CI=\frac{\uplambda -\text{n}}{n-1}$$
(4)

Here,

λ represents” the consistency vector.

n indicates “the number of factors used.

The consistency ratio was calculated using Eq. 5

$$CR\, = \,CI/RI$$
(5)

Here: CI and CR stand for consistency index and consistency ratio, respectively.

Identification of groundwater potential zones

The main thematic layers that contribute to GW were superimposed to create the “GWPM. The Groundwater Potential Index (GWPI), which was computed by combining all of the thematic layers that affect GW, was used to evaluate GW prospect zones. The ‘weighted overlay analysis’ tool was utilized to compute the GWPI values based on the equation as shown in Eq. 6:

$$GWPI={\sum }_{w=1}^{m} {\sum }_{j=1}^{n}Wj\times Xi$$
(6)

where,

n:total classes of thematic layers.

Xi:rank associated with classes of ith thematic layer.

m:Number of total thematic layers used.

Wj:normalized weight of the jth thematic layer.

64,65 employed the GWPI values to classify regions into 5 categories: very high, high, moderate, low, and very low” GWP.

Results and discussion

Analytical hierarchy process result

The AHP approach was used in this work to determine the relative weighting of ten (10) groundwater-influencing conditioning parameters. A subjective experiences and literature survey was conducted to rate the paired decision matrix66,67. The PCM of all GW controlling factors pairwise comparison is depicted in Table 4. The AHP outcome normalized weight of factors indicates that RF is the most significant factor (0.304), with GL as well as GM also contributing 0.222 & 0.177, respectively (see Table 5). In contrast, the soil type and DD are assigned the lowest weights. The slope, DD, soil group (SG), and LULC weights are 0.090, 0.077, 0.037, and 0.037, respectively. The model’s consistency was assessed, yielding a CI of 0.09, λmax of 9.34, and a CR of 0.01. A reasonable level of consistency is indicated by a CR value of less than 0.161.

Table 4 Pairwise comparison for factors.
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Table 5 Normalized weight of factors.
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Groundwater potential zone of the study area

Finding regions with high GWP helps manage water resources sustainably by ensuring that withdrawals are supported by accurate data, avoiding over-extraction as well as GW resource damage. By combining the weight values of several thematic layers, such as slope, DD, RF, LD, lithology, LULC, and GM, along with soil type, the GWPM was created using WAO. To identify GWPZ, high-weighted areas were selected using the methodology “described by68. Using the Jenk (natural break) classification approach”69, the GWPM was then divided into 5 classes according to GW potentiality, ranging from very low to extremely high (see Fig. 4). These classes encompass 10.22% (very high), 31.51% (high), 21.11% (moderate), 25.53% (low), and 11.62% (very low) of area in total (refer to Table 6 and Fig. 4).

Table 6 Areal coverage of GWPZ classes.
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The results of the GWPZ model demonstrate how lithology, RF, & LD have a major impact on the occurrence of GW. According to Hussein et.al. (2016), GL was given more weight than other aspects because of its substantial impact on GW storage as well as transport. The research area’s southern, south-central, southwest, central, and tiny areas of the northwest region all have high to extremely high GWP, according to the GWPM. The higher RF, gentle slopes, and high LD, along with lithological units, include biotite-hornblende gneiss, gneiss, basalt, biotite, and schists that promote GW infiltration are some of the characteristics that are identified as defining these regions’ ample70 As a result, these elements work together to produce advantageous circumstances for GW availability in this section of the research region.

On the other hand, the lithologies found in the eastern, southern, south-central, as well as northwest regions of the area under study include epidote-hornblende gneiss, garnet-sillimanite gneiss, quartz vein, syenite/nepheline syenite, corundum syenite, steep slopes, lower LD, low RF, and elevated ridges. These lithologies have little effect on the availability of GW71,72,73”. Approximately 41.73% of the whole land comprises very high to high GWPZ, whilst 58.26% comprises moderate and very low to low potential zones. Therefore, it is clear that lower GPWZs are more common in the area under study. Regions with high GWP can be prioritized for water supply development, ensuring access to clean water in areas where SW resources may be scarce.

This research contains several limitations that should be noted, even though it uses a methodical scientific analysis of many environmental elements. AHP is a heuristic approach, which means that expert opinion is used to evaluate the criteria. This is one of its main drawbacks. Although the accuracy of this research is good, reordering the ranks using the same method might result in a more accurate map. Furthermore, public maps from different authorities constitute a large portion of the qualitative data used in this investigation. These maps are frequently very generic, though, which could result in notable differences in the factors at the local level. Because of this, when applied to extremely localized locations, the produced GWPM could not be accurate.

Validation of groundwater potential zones

Validation is the most critical procedure for verifying the reliability and accuracy of the data utilized in these models. The common techniques used to evaluate model performance in inventory locations like wells, reservoirs, and springs using Google Earth Pro and a GIS validation map. The data from 37 available wells were used to establish and cross-check the GWPM in this research. The validation results Box A has Moderate potential, Box B has low to very low potential, and Box C has good to very good potential, as shown in Fig. 5. To validate GWPZ mapping, the model had to be validated74. To statistically validate models with strong predictive values, the ROC curve was widely used in research75. The model’s diagnostic capacity to differentiate between groundwater potential zones is demonstrated by the ROC (Receiver Operating Characteristic) curve displayed for the groundwater potential zonation (GWPZ) map. The dashed black line shows how well a random guess model performs, while the red line shows the true positive rate (sensitivity) versus the false positive rate (1-specificity). The relationship between prediction assessment and AUC can be summarized as weak (0.5–0.6), medium (0.6–0.7), good (0.7–0.8), very good (0.8–0.9), and excellent (0.9–1). The Area Under the Curve (AUC) value of 0.703 suggests that the model has an acceptable discriminative ability, with a 70.3% chance of correctly distinguishing a randomly chosen true positive from a false positive shown in Fig. 6. This validation confirms the reliability of the AHP-based GIS model used for delineating groundwater potential zones and supports its applicability in regional-scale hydrogeological studies.

Fig. 5
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Groundwater potential validation map. (Maps are created by using ArcGIS 10.7 software https://desktop.arcgis.com/en/quick-start-guides/10.7/arcgis.desktop.quick.start.guide.htm).

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Fig. 6
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ROC curve for validation of GWPZ.

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Conclusions

Delineating GWPZs in Nilgiri District, TN, India, using RS as well as GIS techniques, facilitates rapid decision-making for sustainable water resource management. Various thematic maps, including GL, GM, lineaments, slope, and soil, were prepared using geological maps, RS data, topographic maps, and conventional sources within the ArcGIS software to assess GW availability in Nilgiri. Each thematic layer was assigned appropriate weightage as well as integrated within the GIS environment to generate a GWPZ map for the area under study. The investigation integrated the impact of a variety of factors on the occurrence and movement of GW, including RF, GL, GM, Slope level (SL), LD, DD, LULC as well as SG, with weights of 0.304, 0.222, 0.177, 0.090, 0.061, 0.077, 0.033, and 0.037, respectively. These parameters have been combined using WAO in the ArcGIS environment to generate the GWPZs for the area under study. Based on the GWPZ map, the area under study is classified into 5 zones: very good (10%), good (32%), moderate (21%), low (26%), and very low (11%) potentials. Overall analysis reveals that areas where lineaments follow drainage lines and intersect, particularly in valleys, fills, are prime locations for GW prospecting. Lineaments play a key role in enhancing GWP in hard rock regions, with water levels in wells near these features rising significantly during the monsoon. Geomorphological units like valley fill and pediments are also promising zones for GW exploration and development. According to the validation results, the AUC of 0.703 on the validation ROC curve indicates a strong model performance, and good groundwater potential zone prediction accuracy suggests that the model has an acceptable discriminative ability, with a 70.3% chance of correctly distinguishing a randomly chosen true positive from a false positive. This validation confirms the reliability of the AHP-based GIS model used for delineating groundwater potential zones and supports its applicability in regional-scale hydrogeological studies.

The results of this investigation can support the sustainable management of resources, enabling more efficient and faster GW exploration. This method is broadly applied to similar regions with hard rock terrain and comparable climates to identify suitable sites for GW exploration and development.