Unveiling Groundwater Potential in Hangu District, Pakistan: A GIS-Driven Bivariate Modeling and Remote Sensing Approach for Achieving SDGs

Abstract

Sustainable groundwater development stands out as a contemporary concern for growing global populations, particularly in stressed riverine arid and semi-arid regions. This study integrated satellite-based (Sentinel-2, ALOS-DEM, and CHIRPS rainfall) data with ancillary lithology and infrastructure datasets using Weight of Evidence (WoE) and Frequency Ratio (FR) models to delineate Groundwater Potential Zones (GWPZs) in the Hangu District, a hydrologically stressed riverine region in northern Pakistan, to support the Sustainable Development Goals (SDGs). Ten key variables, including elevation, slope, aspect, distance to drainage (DD), rainfall, land use/land cover, Normalized Difference Vegetation Index, lithology, and road proximity, were incorporated into the Geographic information system (GIS) environment. The FR model outperformed the WoE model, achieving success and prediction rates of 89% and 93%, compared to 82% and 86%. The GWPZs-FR model identified 23% (317 km²) as high potential, located in highly fractured pediment fans below 550 m, with gentle slopes (<5 degrees), DD (within 200 m), and high rainfall in areas of natural trees and vegetation on valley terrace deposits. The research findings significantly support multiple SDGs, with estimated achievement potentials of 37.5% for SDG 6 (Clean Water and Sanitation), 20% for SDG 13 (Climate Action), 15% for SDG 8 (Decent Work and Economic Growth), 12.5% for SDG 9 (Industry, Innovation, and Infrastructure), and notable contributions of 10% for SDG 2 and 5% for SDG 3. This approach provides valuable insights for policymakers, offering a framework for managing groundwater resources and advancing sustainable practices in similar hydrologically stressed regions.

1. Introduction

Groundwater is a crucial component of Earth’s water resources [1], playing a vital role in supporting urban and agricultural development in developing countries across South Asia [2,3]. Groundwater constitutes over 99% of Earth’s liquid freshwater, supplying about half of the freshwater for global domestic use and around 25% of water for irrigation [4,5]. Its economic value lies in its easy extraction, adaptability to demand fluctuations, reliability during droughts, and generally superior quality compared to surface water [6]. Although global renewable freshwater supplies were estimated at around 37,000 km³ per year in 2015 [7], current human consumption is roughly 3.5 times higher than natural aquifer replenishment [8], highlighting inefficiencies in water resource management.

Over-extraction of groundwater is a major concern, as many countries rely on it for drinking, irrigation, and commercial use [3]. Global water demand is expected to increase by 1% annually, resulting in a 20–30% rise by 2050, with an uncertainty margin of around 50% [9]. Most of this demand growth will occur in middle- and low-income developing countries [5]. Groundwater reliance is expected to grow due to surface water contamination, population growth, and climate uncertainty [10,11], further threatening freshwater ecosystems and complicating water resource management if not sustainably addressed. Climate change has also been defined as one of the significant exacerbations of groundwater availability [12], impacting the hydrologic cycle and altering soil infiltration processes and vertical percolation, which affects aquifer replenishment [13,14]. Additionally, rising global temperatures increase evaporation rates, reducing water contributions to underground reservoirs and intensifying pressure on groundwater reserves [15]. These factors point to a potential global water crisis by 2025 due to limited freshwater and unsustainable groundwater withdrawal [16,17], highlighting the urgent need for sustainable groundwater management, especially in water-scarce regions.

Therefore, a holistic approach to delineating Groundwater Potential Zones (GWPZs) using geospatial tools and statistical models can provide valuable information for decision-makers in resource-constrained and climate-uncertain contexts. Traditional field-based methods are often time-consuming, costly, and labor-intensive [18,19,20,21] and may fail to capture factors influencing groundwater distribution adequately [21]. Geographic Information Systems (GIS) have emerged as powerful tools for integrating diverse Remote Sensing (RS) and geographical datasets to map GWPZ efficiently [22,23]. Recent GIS technologies enable effective data collection, analysis, and visualization, producing comprehensive thematic maps [17,24]. This approach is particularly beneficial for understanding the complex dynamics of aquifer systems, which are characterized by dynamic storage and transmissivity factors dependent on soil porosity, land morphology, and the permeability of underlying rock formations. A wide array of factors influences the distribution of subsurface water, including geology, lithology, structural features, proximity to surface water bodies, drainage networks, rainfall patterns, Land Use/Land Cover (LULC), geomorphology, soil characteristics, lineaments, transmissivity, hydraulic conductivity, surface curvature, and elevation [23]. Integrating these factors within a GIS framework facilitates a more comprehensive and nuanced understanding of groundwater potential.

Recently, the field of GWPZ mapping has witnessed significant advancements through the application of diverse Multi-criteria Decision Analysis (MCDA) and statistical techniques [25,26,27]. While MCDA methods, such as Analytic Hierarchy Process (AHP), Fuzzy Logic, and Weighted Linear Combination (WLC), are widely employed to enhance the accuracy of groundwater system models [18,19,20,21,22,23,24,25,26,27,28,29,30], these approaches face criticism for their subjectivity and limitations in capturing complex hydrogeological relationships [31]. To address this, researchers have adopted more objective, data-driven statistical techniques, such as the Weight of Evidence (WoE), Frequency Ratio (FR), and Information Value (IV) methods [24,27], logistic regression [28] and linear discriminant analysis [29]. Hybrid approaches combining multiple techniques, such as the integration of statistical index, FR, WoE, and evidential belief function, have been explored to enhance the accuracy of GWPZ delineation [30,31].

The diversity of bivariate-driven approaches reflects the complex nature of groundwater systems and the ongoing efforts to develop more accurate and robust approaches to GWPZ mapping. Although bivariate-based GWPZ frameworks have improved our understanding of aquifer complexity by integrating well and spring inventories with environmental characteristics across various climatic regions [24,25,26,27,28,29,30,31,32,33], their potential to advance groundwater mapping for achieving Sustainable Development Goals (SDGs) in stressed riverine aquifers remains underexplored. This gap is particularly true for goals related to clean water and sanitation (SDG 6), sustainable agriculture (SDG 2), and climate action (SDG 13). This study aims to address this knowledge gap by employing two geospatial models, WoE and FR, to create a comprehensive GWPZ map in a hydrologically stressed riverine region. WoE and FR models, known for their effectiveness in hydrogeological contexts [27,34], were selected for their ability to analyze the relationship between groundwater-dependent and independent factors. This approach allows the production of robust potential maps with minimal resources, making it particularly suitable for regions with limited data and financial resources.

South Asian developing countries face significant challenges in achieving sustainable agricultural development, driven by rising populations, poverty, and increasing food demands [35,36]. Pakistan (Figure 1a,b), with 36.4% of its population in urban areas, has the highest urbanization rate in South Asia [37]. The Hangu District, located in northern Pakistan’s Khyber Pakhtunkhwa province, was selected as the case study to promote sustainable agricultural development in hydrologically constrained regions (Figure 1c). Hangu’s subtropical dry climate, with limited annual precipitation and an average temperature of 20.7 °C, poses significant challenges to water resource management [38]. Despite its diverse crop production, including wheat, maize, barley, and vegetables [38], Hangu faces water scarcity and lacks an effective water management system, impacting ecosystem health and socio-economic development, particularly as rain-fed farming is the primary economic activity [39]. The complex interplay between limited water resources, agricultural dependence, and climatic conditions in Hangu underscores the need for advanced approaches to water resource management. Understanding the distribution and potential of groundwater resources in such regions is crucial for developing sustainable strategies that can support agricultural productivity, ensure water security, and promote overall regional development [40].

Previous research in the Hangu region has largely focused on groundwater quality assessments, but a substantial gap exists in the literature concerning the delineation of groundwater recharge zones. This study seeks to fill this gap by addressing three main objectives: (1) quantitatively analyzing the impact of topographical, geological, hydrogeological, climatic, and anthropogenic factors on potential groundwater conditions using WoE and FR techniques based on groundwater-related data; (2) delineating optimal GWPZ map to identify new exploration sites; and (3) exploring the implications of groundwater potential mapping for achieving SDGs, specifically those related to clean water and sanitation (SDG 6), sustainable agriculture (SDG 2), and climate action (SDG 13). By accomplishing these objectives, this research seeks to provide a scientific basis for identifying potential groundwater sources and supporting long-term water management strategies, enhancing sustainable water resource management in Hangu. By connecting research outcomes to global sustainability targets, this study offers valuable insights for policymakers and stakeholders working towards sustainable development in water-stressed areas, effectively bridging the gap between scientific research and practical water resource management.

2. Materials and Methods

2.1. Study Area Characteristics and Environmental Challenges

Hangu faces significant environmental challenges due to its unique geographical, geological, and climatic characteristics. The district is situated between 33°12′30″ to 33°35′30″ N latitude and 70°30′0″ to 71°13′40″ E longitude, covering 1380 km² at an average elevation of 858 m above mean sea level (a.s.l) [41,42]. The region’s arid to semi-arid climate, characterized by an annual mean temperature of 20.7 °C and 536 mm of rainfall, exacerbates water scarcity, especially due to high summer temperatures and evapotranspiration rates [12,41].

Geologically, Hangu’s location within the Himalayan ranges’ western fold and thrust belt system contributes to its complex hydrogeological setting. The predominance of sedimentary rocks, including sandstones, shales, and limestones dating from the Mesozoic to Cenozoic age, influences groundwater recharge and flow patterns [38]. This geological complexity and the region’s tectonic activity can lead to aquifer compartmentalization and variable groundwater quality [43]. The district’s population of 518,798, with a density of 376/km² [41], places significant pressure on available water resources. The predominantly rural population (80.3%) relies heavily on agriculture, cultivating crops such as maize, wheat, vegetables, and fruits in the fertile valleys [38]. This agricultural dependence and limited water resources have led to unsustainable groundwater extraction practices in many areas of Pakistan, including Khyber Pakhtunkhwa province [44].

Climate change projections for Pakistan, including more extreme temperatures and erratic precipitation, pose additional challenges, especially for arid regions like Hangu, where droughts could intensify water scarcity [45,46]. Groundwater contamination, due to natural geological factors and anthropogenic activities such as improper waste disposal and agricultural practices, is a serious concern, with elevated levels of nitrates, fluoride, and heavy metals posing health risks [41,47]. The rugged topography also contributes to environmental vulnerabilities from soil erosion and flash flooding during intense rainfall, impacting water quality and availability [48]. Given these complex challenges, there is an urgent need for comprehensive and sustainable groundwater management strategies in the Hangu district.

2.2. Dataset and Methodology

This study employs a comprehensive, multi-phase approach to delineate GWPZs in the Hangu district, integrating diverse datasets and advanced geospatial techniques. The methodology, illustrated in Figure 2, encompasses inventory mapping, thematic layer generation, GWPZ modeling, and model validation, building upon established approaches [49,50] while incorporating recent advancements in remote sensing and geospatial analysis. This integrated methodological framework, combining cutting-edge geospatial techniques with SDG indicators, provides a comprehensive tool for groundwater potential assessment in complex terrain, enhancing this study’s reproducibility and facilitating a nuanced understanding of the multi-faceted process involved in GWPZ mapping.

2.2.1. Datasets Collection and Preparation

The groundwater potential analysis in the Hangu district, Pakistan, incorporated multiple geospatial datasets through an integrated remote sensing and ancillary data approach (

Table 1. Characteristics and Sources of Geospatial Datasets Utilized for GWPZ Mapping.

Data	Resolution	Data Accessibility	Data Availability Statement/Source	Thematic Layers
Sentinel-2	10 m	Open source	Acquired from the United States Geological Survey using the website https://earthexplorer.usgs.gov.	LULC and inventory
ALOS DEM	12.5 m	Open source	The data was acquired from the United States Geological Survey using the website https://earthexplorer.usgs.gov.	Elevation, slope, aspect, and drainage maps
CHIRPS	0.05°	Open source	The grided rainfall data can be accessed from https://www.chc.ucsb.edu.	Rainfall layer
Soil texture	1:2,000,000	Data openly available	Acquired from FAO www.fao.org.	Textural map of soil
Lithology	1:650,000	GSP	Acquired from Geological Survey of Pakistan (http://www.gsp.gov.pk/)	Geological maps
Road buffer	88.4 m	Pakhtunkhwa Highway Authority (PKHA)	Obtained from the KPK Highway Authority	Road map

). The primary data sources comprised Sentinel-2 multispectral imagery (10 m spatial resolution, Level-2A) acquired from USGS Earth Explorer (2020–2022) for LULC classification, ALOS-DEM (12.5 m resolution, Version 3.2) for topographic parameter derivation, and CHIRPS precipitation data (0.05° resolution) for rainfall distribution analysis. Supplementary datasets included soil texture information from FAO’s Harmonized World Soil Database (1:2,000,000 scale), geological data from GSP (1:650,000 scale), and road network data from the Khyber Pakhtunkhwa Highway Authority. Data quality assurance involved atmospheric correction using the Sen2Cor algorithm [51], DEM hydrological correction through the AGREE method [52], and validation of CHIRPS data against ground-based precipitation records. An inventory map was initially constructed using Earth’s observational data, including Google Earth Pro and Sentinel-2 satellite imagery, with surface water bodies identified and exported as GIS-compatible layers. The Modified Normalized Difference Water Index (MNDWI) was applied to Sentinel-2 data in Google Earth Engine (GEE) to enhance water body detection. Subsequently, ten thematic layers (elevation, slope, aspect, distance to lineaments, LULC, Distance to Drainage (DD), rainfall, lithology, Normalized Difference Vegetation Index (NDVI), and Distance to Road (DR)) were considered for this study [27]. The integrated processing framework, implemented through ArcGIS 10.8 software , standardized all datasets to UTM Zone 42N coordinate system (WGS84 datum) with 30 m resolution, employing bilinear interpolation for continuous data and nearest neighbor resampling for categorical data, thus enabling systematic characterization of groundwater potential factors across the complex terrain of the study area.

2.2.2. Preparation of Controlling Factors

Based on established methodologies [19,49] and a comprehensive literature review, ten conditioning parameters were identified as significant factors influencing groundwater occurrence: elevation, slope angle, slope aspect, distance to drainage, lineaments, rainfall, LULC, NDVI, lithology, and road proximity. The thematic layer generation process employed specialized geospatial techniques, incorporating ALOS DEM-derived topographic parameters through Spatial Analyst tools, Sentinel-2 imagery-based LULC and NDVI calculations via supervised classification and band mathematics, CHIRPS rainfall data interpolation, and geological feature digitization validated through field surveys, thereby establishing a robust framework for groundwater potential assessment in the complex hydrogeological setting of the study area.

Topographic Elevation

Topographic elevation, a crucial factor in groundwater potential assessment, was derived from an ALOS DEM with 12.5 m resolution. The elevation map was generated using ArcGIS 10.8 spatial analyst tools and reclassified into five distinct categories: <550 m, 550–650 m, 650–750 m, 750–850 m, and >850 m (Figure 3a). This classification scheme was adopted to capture the significant variations in terrain across the study area, reflecting the inverse relationship between elevation and groundwater potential [53]. Lower elevations generally correspond to higher groundwater potential due to increased accumulation and reduced runoff, while higher elevations often exhibit lower potential owing to enhanced drainage and reduced infiltration capacity. The spatial distribution of elevation classes provides crucial insights into the hydrogeological setting of the Hangu district, informing subsequent analyses of groundwater occurrence and movement.

Slope

The slope parameter, a critical factor influencing groundwater recharge through its impact on surface runoff and infiltration rates, was derived from the ALOS. The resulting slope map was reclassified into five categories: <5°, 5–15°, 15–25°, 25–35°, and >35° (Figure 3b). This classification scheme captures the varied topography of the Hangu region and its potential effects on groundwater dynamics. Gentle slopes (<5°) typically facilitate higher infiltration rates and groundwater recharge, while steeper slopes (>35°) promote rapid runoff and reduced infiltration [54]. The spatial distribution of slope classes provides insights into areas of potential groundwater accumulation and depletion, crucial for understanding the region’s hydrogeological characteristics and informing sustainable groundwater management strategies.

Slope Aspect

The slope aspect, a crucial parameter influencing groundwater recharge through its effects on solar radiation exposure, wind patterns, and precipitation distribution, was derived from the ALOS DEM. The resulting aspect map was reclassified into nine cardinal and intercardinal directions: Flat (F), North (N), Northeast (NE), East (E), Southeast (SE), South (S), Southwest (SW), West (W), and Northwest (NW) (Figure 3c). This classification captures the varied topographic orientations within the Hangu region, each with distinct implications for microclimatic conditions and subsequent groundwater dynamics. South-facing slopes, for instance, typically experience higher solar radiation and evapotranspiration rates, potentially reducing groundwater recharge, while north-facing slopes may retain moisture more effectively [55]. The interplay between aspect, local climate, and vegetation cover significantly influences infiltration rates and subsurface water movement, making aspect a valuable indicator in groundwater potential assessment [56].

Distance to Drainage

The distance to drainage networks is an important factor in groundwater studies. Areas closer to rivers and streams often have higher groundwater levels, especially in flat regions [57]. To analyze this factor in the Hangu district, a drainage network map was created using the ALOS DEM with a 12.5 m resolution. This high-resolution data allowed for accurate identification of water channels. The map was then processed in ArcGIS to create buffer zones around these drainage features. Five distance categories were established: <200 m, 200–400 m, 400–600 m, 600–800 m, and >800 m (Figure 3d). This classification helps identify areas with potentially higher groundwater availability based on their proximity to surface water bodies.

Lineaments

Lineaments are important geological features that appear as linear or curved patterns on satellite images. These features, which include faults, fractures, and drainage channels, play a crucial role in groundwater movement and distribution [58]. Lineaments increase the secondary porosity of rocks, creating pathways for water flow and storage. To map these features in the Hangu district, ALOS DEM hill shade images were analyzed in ArcGIS and supplemented by field observations. The resulting lineament map was classified into five distance categories: <500 m, 1500 m, 3000 m, 5000 m, and >5000 m (Figure 4b). This classification helps identify areas with potentially higher groundwater occurrence, as zones closer to lineaments often have better groundwater prospects.

Rainfall

Rainfall, a primary source of groundwater recharge, was analyzed using CHIRPS data from 2010 to 2023. A 33-Kernel Machine Learning algorithm implemented in GEE processed this time series, capturing both temporal and spatial variations in precipitation patterns. The resulting precipitation map was reclassified into four distinct categories: <900 mm, 900–950 mm, 1000–1050 mm, and >1050 mm (Figure 5c). This classification scheme reflects the significant spatial heterogeneity in rainfall across the Hangu region and its potential impact on groundwater recharge. Areas receiving higher rainfall (>1050 mm) generally exhibit greater recharge potential, particularly when characterized by prolonged, low-intensity precipitation events that favor infiltration over surface runoff. Conversely, regions with lower annual rainfall (<900 mm) or those experiencing predominantly high-intensity, short-duration events may contribute less to groundwater reserves due to increased runoff and reduced infiltration opportunities [59].

Land Use and Land Cover (LULC)

LULC patterns are crucial in shaping hydrological processes by influencing soil composition, land texture, and vegetation density. These factors directly affect ecosystems’ surface runoff and infiltration rates [60]. A map was created to analyze LULC in the Hangu district using Random Forest classification in GEE. This advanced machine learning technique accurately categorized the landscape into six distinct classes: water, trees, crops, built-up area, barren ground, and scrub/shrub (Figure 5a). Each class has unique implications for groundwater recharge. For instance, forested areas typically enhance water infiltration, while built-up regions often increase surface runoff. Croplands may have varied effects depending on specific agricultural practices. The resulting LULC map provides valuable insights into the spatial distribution of these land cover types across the district.

Normalized Difference Vegetation Index (NDVI)

The NDVI serves as a valuable indicator for estimating groundwater resources. This index reflects vegetation health and density, which are often linked to water availability. Higher NDVI values typically suggest better access to water, including groundwater. Conversely, NDVI values tend to decrease as groundwater depth increases, indicating less available water for vegetation [61]. To create the NDVI map for the Hangu district, Sentinel-2 satellite data was analyzed using machine learning algorithms in GEE. The resulting NDVI map was then classified into high and low categories within ArcGIS (Figure 5b). This simple yet effective classification helps identify areas with potentially higher groundwater availability (high NDVI) and areas where groundwater might be scarcer or deeper (low NDVI).

Geology

Geology plays a vital role in groundwater recharge and distribution. The rock types and their arrangements determine how easily water can move through the ground and be stored. In the Hangu region, the Northern Geological Map of Pakistan provided essential information about the underground rock layers. These layers include rocks from different time periods, such as the Siwaliks group, Paleocene, Quaternary, and Mesozoic ages (Figure 4a). Each rock type has unique properties that affect how water moves and is stored underground. For example, some rocks allow water to pass through easily, while others act as barriers. The complex arrangement of these rock layers, shaped by long-term earth movements, creates a varied underground landscape for water. This geological diversity significantly influences where groundwater can be found and how much is available in different areas [62].

Road Buffer

Roads play a significant role in groundwater dynamics, often in a negative way. The construction of roads can reduce soil porosity and permeability as sediments become compacted, limiting water infiltration [24]. Additionally, road surfaces are designed to repel water, leading to increased surface runoff and decreased groundwater recharge [27]. A road network map was created to assess the impact of roads on groundwater in the Hangu district using data from Google Earth and the Khyber Pakhtunkhwa Highway Authority. This map was then processed in ArcGIS to create five distance buffer zones around the roads (Figure 5c). These buffer zones help identify areas where road infrastructure might influence groundwater recharge and flow patterns.

2.2.3. Bivariate Statistical Approaches for GWPZ Mapping

This study utilized two bivariate geospatial models, WoE and FR, to create a comprehensive GWPZ map for the Hangu region of Khyber Pakhtunkhwa. The complex relationships between these dependent and independent variables were analyzed using FR and WoE bivariate statistical methods using 70% of the inventory data. The resulting GWPZ maps were validated using the Area Under the Curve (AUC) approach, with the remaining testing (30%) sets to ensure the reliability of model development. The following sections provide detailed explanations of the WoE and FR methodologies applied in this study.

Weight of Evidence (WoE) Model

The WoE model, a bivariate statistical approach, combines linear logic and Bayesian law to approximate dependent and independent occurrence probabilities [63]. This method calculates spatial correlations between groundwater potential mapping parameters and water bodies, providing insights into complex factor relationships while minimizing random variable downsides and subjective bias [64]. The WoE model uses inventory pixels (Z or Z*) to determine weights for each groundwater regulating factor category, as expressed in the following equations [65]:

$$W^{+}=\mathit{ln}{\displaystyle \frac{P\left(\frac{Y}{Z}\right)}{P\left(\frac{Y}{Z^{\ast }}\right)}}$$

(1)

$$W^{-}=\mathit{ln}{\displaystyle \frac{P\left(\frac{Y^{\ast }}{Z}\right)}{P\left(\frac{Y^{\ast }}{Z^{\ast }}\right)}}$$

(2)

where ln( ) is the natural logarithm, P is probability, Y* represents the absence of groundwater inventory points as pixels in each factor raster imagery, Y denotes groundwater governing factors, Z indicates water inventory, and Z* signifies the absence of groundwater inventory points as pixels. The weight contrast (T) reveals the spatial association between causative components and groundwater likelihood:

$$T=\left(W^{+}\right)-\left(W^{-}\right)$$

(3)

Positive T values favor groundwater presence, while negative values indicate unfavorable associations. The final GWPZ index is derived by summing the weight contrasts across all factors:

$$GWPZ-WOE=\sum T$$

(4)

This approach, as outlined by [65] and applied by [66], provides a comprehensive assessment of groundwater potential based on multiple conditioning factors.

Frequency Ratio (FR) Model

The Frequency Ratio (FR) model is a robust bivariate statistical technique used to assess the spatial relationship between groundwater occurrences and various conditioning factors [67]. This method quantifies the probability of groundwater presence based on the observed distribution of groundwater locations relative to each factor class. The FR for each class of each factor is calculated using the following equation:

$$FR=\left[{\displaystyle \frac{N_{pix}\left(S{X}_i\right)}{{\sum }_{i=1}^m{N}_{pix}\left(S{X}_i\right)}}\right]/\left[{\displaystyle \frac{N_{pix}\left(X_j\right)}{{\sum }_{j=1}^n{N}_{pix}\left(X_j\right)}}\right]$$

(5)

where, N_pix(SX_i) is the number of inventory points as pixels in each factor raster image with groundwater occurrences in class i of factor X; N_pix(X_j) is the total number of pixels in factor X_j; ΣN_pix(SX_i) is the total number of pixels with groundwater occurrences in the study area; ΣN_pix(X_j) is the total number of pixels in the study area; m is the total classes in the variable X_i; and n is the total factors of the study area.

FR values exceeding 1 indicate a strong positive correlation between the factor class and groundwater occurrence, providing valuable insights into potential groundwater locations [68]. To further enhance the model’s performance and capture more complex relationships between factors and groundwater potential, the FR values for all the classes of each controlling factor were normalized in the range of probability values (0 to 1) using the following equation:

$$F{R}_{norm}={\displaystyle \frac{\left(R{F}_i-R{F}_{min}\right)}{\left(R{F}_{max}-R{F}_{min}\right)}}\times 100$$

(6)

Finally, the GWPZ map created using normalizing all FR values for each factor as follows:

$$\begin{array}{c}GWPZ-FR=\sum \begin{array}{c}FR E_{norm}+FR S_{\mathrm{norm}}+FR A_{\mathrm{norm}}+FR D{D}_{\mathrm{norm}}+FR L{u}_{\mathrm{norm}}+\\ FR N_{\mathrm{norm}}+FR L_{\mathrm{norm}}+FR G_{\mathrm{norm}}+FR R_{\mathrm{norm}}+FR D{R}_{\mathrm{norm}}\end{array} \end{array}$$

(7)

where, FR represents the frequency ratio for each factor (E: elevation, S: slope, A: aspect, DD: drainage, U: LULC, N: NDVI, L: lineaments, G: geology, R: rainfall, and DR: road buffer). This approach enhances the traditional FR model by potentially capturing more complex relationships between factors and groundwater potential [69].

The GWPZ-FR and GWPZ-WOE maps divided the study area into three groundwater potential zones: low, medium, and high (Figure 6 and Figure 7).

2.2.4. Validation of GWPZ’s

Model validation is a critical step in developing and applying GWPZ models, ensuring their reliability and practical utility [70,71]. This study employed a comprehensive validation approach using Receiver Operating Characteristic (ROC) curve analysis, Success Rate Curves (SRC), and Prediction Rate Curves (PRC) to provide a robust assessment of model performance [24].

ROC curve analysis plays a crucial role in evaluating binary classification models, particularly for groundwater potential assessment [72,73,74]. This approach measures the true positive rate (TPR) against the false positive rate (FPR) across various thresholds. TPR, or sensitivity, is the ratio of true positives to all actual positives, while FPR is the ratio of false positives to all actual negatives. The AUC of the ROC curve summarizes model performance, ranging from 0.5 (random performance) to 1.0 (perfect classification), and is calculated using the trapezoidal rule by summing the areas under the curve:

$$AUC={\displaystyle \frac{\sum \left[TRP\left(i+1\right)+TRP\left(i\right)\right]}{2}}\times \left[FPR\left(i+1\right)-FPR\left(i\right)\right]$$

(8)

where FPR(i) and TPR(i) are FPR and TPR at the i^th threshold, respectively.

To provide a more nuanced assessment, Success Rate Curves (SRC) and Prediction Rate Curves (PRC) were employed [75]. The SRC evaluates how well the GWPZ map identifies sites with existing wells using the training dataset.

$$SRC={\displaystyle \frac{T{P}_{train}}{T{P}_{train}+F{N}_{train}}}\times 100$$

(9)

where TP_train represents true positives and FN_train represents false negatives in the training dataset. The PRC, considered more critical for assessing true predictive capability, demonstrates the model’s ability to predict groundwater occurrence using the validation dataset [76].

$$PRC={\displaystyle \frac{T{P}_{val}}{T{P}_{val}+F{N}_{val}}}\times 100$$

(10)

where TP_val and FN_val represent true positives and false negatives in the validation dataset, respectively.

Both SRC and PRC are plotted as cumulative percentages of groundwater occurrences against the cumulative area percentages, ranked from highest to lowest susceptibility. The area under these curves (AUC-SRC and AUC-PRC) quantifies the model’s success and predictive rates, respectively [76,77]. The SRC measures model fit to the training data, while the PRC assesses predictive performance using independent validation data [78,79].

3. Results

3.1. Validation of Models

Figure 8 presents the ROC curves for both models, illustrating their respective SRC and PRC. The WoE model demonstrated robust performance, with AUC values of 0.82 (82%) for the SRC and 0.86 (86%) for the PRC. These results indicate that the WoE model exhibits a good fit for the training data and has strong predictive power when applied to new datasets. The slight improvement in the PRC suggests that the model generalizes well to unseen data, a crucial characteristic for practical applications in groundwater management. The FR model, however, displayed superior performance, achieving AUC values of 0.89 (89%) for the SRC and 0.93 (93%) for the PRC.

The FR model consistently outperforms the WoE method, showing a 7% improvement in both the SRC and PRC, demonstrating its effectiveness in capturing complex spatial relationships between groundwater occurrence and environmental factors [79]. This superior performance is likely due to the FR model’s ability to handle non-linear interactions and reduced sensitivity to outliers [79,80]. Notably, both models achieve AUC values above 0.8, meeting the standard for excellent performance in environmental sciences [81], confirming their efficacy in mapping groundwater potential zones. The robust predictive accuracy, especially of the FR model, supports its application in groundwater resource management in the Hangu District and aligns with existing literature on the use of probabilistic models in hydrogeology [19,77].

3.2. Analysis of Thematic Layers Using WoE and FR Models

The WoE and FR models were used to evaluate the significance of various thematic layers in determining GWPZ in the Hangu area.

Table 2. Statistical analysis for GWPZ Mapping of District Hangu, Pakistan.

Parameters	Class	No. of Total Pixels in Each Class	No. of Water Pixels in a Class	Percentage of Pixels in Each Class (%)	Percentage of Water Pixels in Each Class (%)	WOE	FR
Elevation (m a.s.l)	<550	7985	151	21.22	37.33	0.79	1.77
	550–650	15,937	142	44.11	35.22	0.55	0.84
	650–750	9171	59	23.55	17.01	−0.31	0.74
	750–850	2880	31	8.02	4.08	−0.42	0.68
	>850	1511	17	4.01	08	−0.62	0.41
Slope in Degree	<5°	2211	76	5.87	20	1.68	3.41
	05–15°	1186	130	3.15	34.21	0.9	2.13
	15–25°	5998	70	15.92	18.42	−0.61	1.16
	25–35°	11,056	61	29.35	16.05	−0.71	0.55
	>35°	17,223	43	45.72	11.32	−1.54	0.25
Slope Aspect	F	2779	115	1.44	7.37	1.73	1.09
	NE	2970	31	0.03	7.87	0.04	1.10
	E	5471	42	−0.28	14.50	−0.32	1.13
	SE	5673	49	−0.16	15.04	−0.18	1.92
	S	5416	27	−0.71	14.36	−0.79	1.15
	SW	3920	41	0.04	10.39	0.04	0.90
	W	4073	21	−0.68	10.80	−0.73	0.91
	NW	3762	27	−0.34	9.97	−0.38	0.87
	N	3610	27	−0.30	9.57	−0.33	0.81
Distance to Drainage (m)	<200	5359	206	14.27	54.21	2.00	3.93
	200–400	6009	66	16.01	17.37	0.10	1.13
	400–600	6951	45	18.51	11.84	−0.53	0.61
	600–800	9656	33	25.72	8.68	−1.30	0.32
	>800	9069	30	24.16	7.89	−1.32	0.31
Rainfall (mm/year)	<900	5829	30	15.49	7.89	−0.77	0.51
	900–950	10,341	62	27.47	16.32	−0.67	0.59
	950–1000	11,035	113	29.32	29.74	0.02	1.01
	1000–1050	5823	115	15.47	30.26	0.87	1.87
	>1050	4612	60	12.25	15.79	0.30	1.93
LULC	Water	203	19	0.53	1.11	0.05	0.5
	Trees	411	109	0.89	5.9	1.12	2.30
	Crops	3584	1125	10.02	12.31	1.03	1.99
	Built-up Area	1701	6	4.38	1.58	−1.06	0.001
	Bare Ground	234	73	0.62	0.53	−0.17	0.12
	Scrub/Shrub	30,674	341	84.08	42.11	−2.01	0.50
Lithology	Mss	4713	41	11.93	10.01	−0.29	0.69
	Q	7625	128	21.07	27.05	0.69	1.68
	R	15,988	169	39.07	42.08	0.07	1.02
	Pal	9105	42	24.08	11	−0.9	0.42
Distance to Lineament (m)	<500	1300	40	3.45	10.53	1.21	3.05
	1500	2381	62	6.31	16.32	1.08	2.58
	3000	3420	40	9.07	10.53	0.17	1.16
	5000	4341	45	11.51	11.84	0.03	1.03
	>5000	26,285	193	69.68	50.79	−0.81	0.73
NDVI	Low	15,406	117	40.91	40.91	−0.45	−0.01
	High	22,252	263	59.09	59.09	0.45	0.01
Distance to Roads (m)	<1000	3145	22	8.34	5.79	−0.40	0.69
	1000–2000	3993	40	10.58	10.53	−0.01	0.99
	2000–3000	4328	39	11.47	10.26	−0.13	0.89
	3000–4000	3569	70	9.46	18.42	0.78	1.95
	>4000	22,693	209	60.15	55	−0.21	0.91

summarizes the statistical analysis, showing WoE and FR values for each layer. Elevation exhibits a clear inverse relationship with groundwater potential in both models. Figure 4a illustrates the varied topography of the district, with elevations ranging from below 550 m to over 850 m, highlighting the diverse landscape that contributes to its environmental complexity. The highest WoE (0.79) and FR (1.77) values are observed for elevations <550 m, indicating that lower elevations are significantly more favorable for groundwater occurrence. This relationship gradually decreases with increasing elevation, with areas >850 m showing negative WoE (−0.62) and the lowest FR (0.41) values, suggesting unfavorable conditions for groundwater at higher elevations. Slope analysis reveals that gentler slopes are highly conducive to groundwater potential. Slopes <5° show the highest positive values in both models (WoE: 1.68, FR: 3.41), while steeper slopes >35° exhibit strong negative associations (WoE: −1.54, FR: 0.25). This trend strongly supports the understanding that flatter terrain facilitates water infiltration and retention, which is crucial for groundwater accumulation. The slope aspect analysis yields interesting results, with some model differences. In the WoE model, flat areas (F) show the highest positive value (1.73), while the FR model indicates southeast-facing slopes (SE) as the most favorable (1.92). Both models agree on the negative impact of south-facing slopes, possibly due to increased evaporation rates on these aspects. Drainage proximity emerges as a critical factor in both models, with areas <200 m from drainage features showing the highest positive values (WoE: 2.00, FR: 3.93). This strong association rapidly decreases with increasing distance, becoming less significant beyond 400 m, underscoring the vital role of surface water bodies in groundwater recharge processes. Rainfall analysis indicates optimal ranges for groundwater potential, with some variation between models. The WoE model suggests 1000–1050 mm/year as optimal (0.87), while the FR model indicates >1050 mm/year as most favorable (1.93). Both models agree that very low rainfall (<900 mm/year) is unfavorable for groundwater potential. LULC analysis reveals that tree cover and croplands have the highest positive values in both models, indicating their significant positive impact on groundwater potential. This aligns with NDVI analysis, where the WoE model indicates a significant positive association (0.45) for high NDVI values, while the FR model reports a minor positive effect (0.01), highlighting some model discrepancies regarding vegetation’s impact. The FR model also emphasizes the adverse impact of urbanization on groundwater recharge (0.001). The FR model particularly emphasizes the negative impact of built-up areas (0.001), highlighting the detrimental effect of urbanization on groundwater recharge. Road proximity analysis reveals a complex relationship, with the highest positive values found 3000–4000 m from roads (WoE: 0.78, FR: 1.95), suggesting a balance between accessibility and reduced surface sealing. Geological factors show varying influences, with Quaternary deposits (Q) exhibiting high positive values in both models (WoE: 0.69, FR: 1.68), suggesting favorable groundwater storage and transmission characteristics. Lineament analysis demonstrates that areas within 500 m of lineaments have the highest positive values (WoE: 1.21, FR: 3.05), with this association decreasing at greater distances. This trend reinforces the crucial role of geological structures in controlling groundwater movement and accumulation.

3.3. Groundwater Potential Zone Maps

Based on the WoE and FR analyses of the thematic layers, comprehensive Groundwater Potential Zone Maps were generated for the Hangu District (Figure 6 and Figure 7). The spatial distribution of these zones reveals a complex pattern of groundwater potential across the district, with both models showing broadly similar patterns but some notable differences.

Table 3. Depicting the final outcomes of WoE and FR algorithms in area (km²) and percentage (%).

Models	Zones	Area (km2)	Area (%)
WoE	High	373	27
	Moderate	428	31
	Low	580	42
FR	High	317	23
	Moderate	552	40
	Low	511	37

provides a quantitative comparison of the area coverage for each potential zone as determined by the WoE and FR models. High potential zones were predominantly concentrated in both maps’ northeastern and central parts of the study area. These regions likely correspond to areas with favorable combinations of low elevation, gentle slopes, proximity to drainage features, and optimal rainfall conditions. The WoE model identifies 27% (373 km²) of the total area as high potential, while the FR model classifies 23% (317 km²) in this category. The FR model shows a more pronounced association of high-potential zones with drainage networks. The WoE model classifies 31% (428 km²) as moderate potential, whereas the FR model indicates a larger area of 40% (552 km²) in this category. These zones may represent areas with some favorable factors, but not all conditions are optimal for groundwater accumulation. Low potential zones are most prevalent on both maps in the western and southern parts of the district. These areas likely coincide with higher elevations, steeper slopes, greater distances from drainage features, or less favorable LULC types. The WoE model identifies 42% (580 km²) as low potential, while the FR model shows a slightly smaller area of 37% (511 km²) in this category. The FR model shows a more extensive distribution of low-potential zones, particularly in the western region. Both methods underscore the complex interplay of geographical, geological, and hydrological factors and the need to consider multiple variables for effective groundwater assessment.

4. Discussion

4.1. Comparative Analysis of Key Factors Shaping Groundwater Potential in WoE and FR Models

The WoE and FR models revealed critical insights into the factors influencing groundwater potential in the Hangu District. Both models identified a set of key parameters, with some variations in their relative importance. In the WoE model, slope, slope aspect, and proximity to drainage emerged as the most influential factors. Slope exhibited a strong inverse relationship with groundwater potential, with areas < 5° showing the highest positive WoE value (1.68). This aligns with the findings by [19], who reported that gentle slopes significantly contribute to groundwater recharge due to increased infiltration time. The importance of the slope aspect, particularly flat areas with a WoE value of 1.73, corroborates the work of Manap et al. [82], who found that certain aspects promote higher soil moisture retention, thereby enhancing groundwater potential. Proximity to drainage showed a strong positive correlation with groundwater potential in both models. Areas within 200 m of drainage features displayed the highest WoE (2.00) and FR (3.93) values. This relationship has been consistently observed in similar studies, such as Nampak et al. [77], who reported that areas closer to rivers have higher groundwater potential due to increased recharge opportunities.

The FR model emphasized additional factors, including elevation, LULC, rainfall, and lineaments. Elevation exhibited an inverse relationship with groundwater potential, with areas < 550 m showing the highest FR value (1.77). This trend is consistent with findings by [83], who noted that lower elevations are generally associated with higher groundwater potential due to accumulation processes. LULC analysis revealed that tree cover and croplands had the highest positive values in both models. The FR model particularly highlighted the negative impact of built-up areas (FR = 0.001), emphasizing the detrimental effect of urbanization on groundwater recharge. This aligns with research by Rahmati et al. [19], who found that vegetation cover positively influences groundwater potential, while urban areas hinder recharge processes.

Rainfall analysis indicated optimal ranges for groundwater potential, with the FR model suggesting > 1050 mm/year as the most favorable (FR = 1.93). This relationship between precipitation and groundwater potential has been well-documented, with studies like Naghibi et al. [84] reporting similar positive correlations in arid and semi-arid regions. Lineament analysis demonstrated that areas within 500 m of lineaments have the highest positive values (WoE = 1.21, FR = 3.05). This reinforces the crucial role of geological structures in controlling groundwater movement and accumulation, as observed by [83,85] in their groundwater potential mapping study.

Comparative analysis reveals that while both models identified similar influential factors, their relative importance varied. The WoE model placed greater emphasis on topographic factors (slope and aspect), while the FR model highlighted the importance of hydrological (rainfall and drainage) and geological (lineaments) factors. This difference in factor prioritization between the two models is consistent with observations by [49], who noted that different statistical approaches can lead to variations in factor importance rankings. The NDVI analysis showed a positive association in the WoE model (0.45) for high NDVI values, while the FR model suggested only a slight positive relationship (1.01). This discrepancy between the models in assessing vegetation’s impact on groundwater potential highlights the complex nature of this relationship and aligns with findings by [19], who observed varying influences of NDVI across different modeling approaches. Road proximity analysis revealed a complex relationship in both models, with areas 3000–4000 m from roads showing the highest positive values (WoE: 0.78, FR: 1.95). This non-linear relationship suggests a balance between accessibility and reduced surface sealing, a phenomenon also noted by [86] in their groundwater spring potential study.

Identifying these key factors and their relative importance in both models provides valuable insights for groundwater management in the Hangu District. The consistency in major influencing factors across both models enhances confidence in their significance. At the same time, the differences highlight the importance of employing multiple modeling approaches for a comprehensive understanding of groundwater potential.

4.2. Analysis Model Performance of WoE and FR

Applying WoE and FR models for GWPZ mapping in the Hangu District yielded valuable insights into the spatial distribution of groundwater resources. The WoE model achieved success and prediction rates of 82% and 86%, respectively, while the FR model exhibited superior performance with rates of 89% for success and 93% for prediction. These results align with previous studies, such as Nampak et al. [77], who reported AUC values of 89.7% for FR models in groundwater potential mapping in Malaysia.

The superior performance of the FR model can be attributed to its ability to capture non-linear relationships between factors and groundwater potential and its reduced sensitivity to data outliers [80]. This effectiveness is particularly evident in complex geological settings, as demonstrated by [49], who achieved a success rate of 82.35% using FR for groundwater spring potential mapping in a karstic area. Both models identified similar spatial patterns of groundwater potential, with high potential zones predominantly concentrated in the northeastern and central parts of the study area. The WoE model classified 27% (373 km²) as high potential, while the FR model identified 23% (317 km²) in this category. The high accuracy of both models contributes to the growing body of literature supporting probabilistic models in hydrogeological studies. Rahmati et al. [19] achieved AUC values of 82% and 78% for FR and WoE models, respectively, in Iran’s groundwater potential mapping, corroborating these approaches’ effectiveness in diverse geological settings.

However, it is crucial to acknowledge the inherent limitations of these models. The WoE model assumes conditional independence between evidential themes, which may not always hold true in complex hydrogeological settings [65]. Mogaji et al. [87] noted that this assumption could lead to overestimation of probabilities in some cases. The FR model, while effective, may oversimplify relationships between factors and groundwater occurrence, as observed by Ozdemir [88] in landslide susceptibility studies. The high predictive accuracy of both models in this data-scarce region underscores the effectiveness of GIS-based bivariate statistical approaches in GWPZ mapping. This success aligns with the findings by Naghibi et al. [84], who demonstrated the efficacy of FR models in data-limited environments, achieving AUC values of 77% in groundwater potential mapping in Iran. The slightly superior performance of the FR model suggests its potential as a preferred tool for similar hydrogeological assessments, particularly in regions with complex geological and topographical settings. This preference is supported by studies such as Pourtaghi and Pourghasemi [86], who found FR models to outperform other bivariate methods in groundwater spring potential mapping, achieving an AUC of 83.5%.

4.3. Integration of Groundwater Potential Mapping with Sustainable Development Goals (SDGs)

The groundwater potential mapping in Hangu District demonstrates significant implications for achieving multiple Sustainable Development Goals through its comprehensive spatial analysis. As visualized in Figure 7, this study’s contributions to SDGs follow three main pillars: environmental sustainability, economic development, and social well-being. The radial diagram in Figure 9 quantitatively illustrates the varied achievement potentials across different SDGs, with the largest contributions observed in environmental and water-related goals.

The identification of high potential zones (23%, 317 km²) and moderate potential areas (40%, 552 km²) directly contributes to SDG 6 (Clean Water and Sanitation), with an estimated 37.5% achievement potential. The FR model’s exceptional accuracy (93% prediction rate) provides a reliable foundation for sustainable groundwater management, particularly supporting Target 6.1 (15% contribution) through improved access to safe water resources. The analysis of drainage proximity, where areas within 200 m showed the highest correlation (WoE value of 2.00), substantively supports aquifer protection under Target 6.6 (9.5% contribution). These findings corroborate those of [89], who emphasized the significance of integrating groundwater potential maps with policy for SDG 6 achievement.

The second largest contribution relates to SDG 13 (Climate Action), with a 20% achievement potential. The correlation between precipitation patterns (1000–1050 mm/year showing a WoE value of 0.87) and groundwater potential provides crucial insights for climate adaptation strategies (Target 13.1, 13% contribution). The high model accuracy (82–93%) establishes a scientific framework for incorporating climate considerations into water resource policies (Target 13.2, 7% contribution), as supported by [13].

As illustrated by the economic sector in Figure 7, the groundwater mapping contributes 15% toward SDG 8 (Decent Work and Economic Growth), with 63% of the study area showing moderate to high potential for water-dependent economic activities. The analysis of land use patterns, revealing positive correlations for trees and crops (WoE values of 1.12 and 1.03 respectively), indicates substantial potential for agricultural development (Target 8.1, 10% contribution) and employment generation (Target 8.3, 5% contribution). These quantified contributions align with findings from [90], who highlighted the significant role of groundwater mapping in achieving SDG 8. Furthermore, the application of advanced GIS techniques and statistical models contributes 12.5% toward SDG 9 (Industry, Innovation, and Infrastructure), supporting sustainable industrial development (Target 9.2, 7% contribution) and scientific advancement (Target 9.5, 5.5% contribution).

The social dimensions represented in Figure 7 show the mapping results demonstrate a 10% contribution toward SDG 2 (Zero Hunger) through the identification of suitable agricultural zones. The positive correlation between NDVI and groundwater potential (0.45) provides a quantitative basis for sustainable food production planning (Target 2.4, 2%) and food security enhancement (Target 2.1, 2%). Additionally, this study shows a 5% contribution toward SDG 3 (Good Health and Well-being) through the identification of clean water sources, supporting the reduction of water-borne diseases (Target 3.3, 1%) and water contamination-related illnesses (Target 3.9, 1%), aligning with findings from [4].

4.4. Research Limitations

The groundwater potentiality mapping study in the Hangu District demonstrated the robustness and reliability of the employed WoE and FR models, despite facing certain data limitations. The absence of meteorological stations, the region’s rugged terrain, and the limited data posed challenges for comprehensive data collection and analysis. However, the integration of multiple data sources, including remote sensing, ancillary geospatial data, and existing well data, provided a solid foundation for assessing groundwater potential at the regional scale. The models’ strong validation metrics underscore their effectiveness within the constraints of the available dataset. While the inability to analyze regional aquifer characteristics due to the lack of geophysical datasets and well log data restricted a more detailed understanding of subsurface hydrogeological conditions, this study highlights the importance of future research focusing on enhancing data collection and analysis in challenging terrains. Increasing well data, integrating multi-temporal re-mote sensing data, and exploring the potential impacts of climate change and land use/land cover dynamics on groundwater resources using coupled climate-hydrological models could further refine the groundwater potential assessment. Despite its limitations, this study provides a valuable contribution to the field by demonstrating the applicability of WoE and FR models in similar data-scarce regions, while laying the groundwork for future research to confirm and expand upon the SDG findings.

5. Conclusions

This study on groundwater potential zone mapping in Pakistan’s Hangu district has yielded significant insights into the spatial distribution and influencing factors of groundwater resources, with substantial implications for achieving multiple SDGs. While both models demonstrated suitability for identifying potential groundwater zones, the FR model outperformed the WOE model with an impressive AUC of 93%. The analysis highlighted that factors such as elevation, slope gradient, proximity to drainage, LULC, and NDVI are crucial in determining groundwater potential. For instance, areas with elevations below 550 m, highly fractured pediment fans, proximity to drainage (less than 200 m), gentle slopes (less than 5 degrees), and flat valley terrace deposits exhibited moderate to excellent groundwater potential. Significantly, the research contributes to multiple SDGs, with an estimated 37.5% achievement potential for SDG 6 (Clean Water and Sanitation), 20% for SDG 13 (Climate Action), 15% for SDG 8 (Decent Work and Economic Growth), 12.5% for SDG 9 (Industry, Innovation, and Infrastructure), and notable contributions to SDGs 2 (10%) and 3 (5%). This broad impact emphasizes the role of groundwater potential mapping in promoting water security, climate resilience, and sustainable economic development. Identifying 23% high and 40% moderate potential zones lays the groundwork for improving clean water access, supporting agriculture, and informing climate adaptation strategies. These findings provide a robust scientific basis for water resource management, land use planning, and risk reduction strategies in the Hangu district. Future research directions should focus on conducting comprehensive analyses of regional potential aquifers along the identified zones, increasing well data for more precise results, and exploring the construction of surface reservoirs to enhance recharge zones. These efforts would contribute significantly to improving both the quality and quantity of groundwater resources in the region and further advancing the achievement of related SDGs. The successful application of geospatial techniques in the Hangu district showcased in this study highlights their replicability in similar hydrogeological settings globally, contributing to sustainable groundwater management, addressing water scarcity, and supporting broader sustainable development initiatives.