Groundwater potential zone mapping of Llandrindod Wells, Wales, UK using GIS, remote sensing, and analytic hierarchy process

An important surface-level source of freshwater is groundwater (Arabameri et al. 2019). Whether or not groundwater is accessible in a given area depends heavily on the degree and direction of primary and secondary porosity. Delineating and mapping various lithological, structural, and geomorphological units is an integral part of groundwater exploration. Geological units' lineament density, slope, soil type, land use/land cover (LULC), rainfall characteristics, drainage density, and their interrelationships all have an impact on groundwater availability and flow. A variety of applications, including drinking, irrigation, and industrial processes, rely on groundwater on a global scale (Chen et al. 2018; Pradhan et al. 2021). Because it is less likely to be affected by catastrophic events (such as surface contamination and pollution) on Earth's surface, drinking groundwater is safer and tastes better than drinking surface water (Das 2019). Due to development, inadequate rainfall, and agricultural expansion, groundwater levels have been decreasing and pressure on existing groundwater resources has been rising in many parts of the world. This is happening in response to increasing energy demands and concerns about food security on a global scale (Taheri et al. 2020a, b; Majidipour et al. 2021). Due to factors such as a growing global population, increased urbanization and industrialization, and the already-existing extreme dryness of water resources, which will be worsened by global warming, water scarcity may persist for a longer period of time (Owolabi et al. 2021). One of the keys to tackling water concerns globally is identifying zones with potential groundwater. There has been a recent uptick in worldwide initiatives to pinpoint areas with groundwater potential (Muavhi et al. 2021). Therefore, it is essential to regularly and accurately map and comprehend the accessible groundwater resources on a broader scale.

Discoveries and investigations into GWPZ have relied on several time-honored methods, such as hydrogeological research, geophysical surveys, and geological investigations (Arulbalaji et al. 2019; Adesola et al. 2023). Electromagnetic, seismic refractive, gravity, magnetic, and electrical resistivity tests are among the geophysical methods that have been employed to study groundwater (Pradhan et al. 2019; McLachlan et al. 2021; Olatinsu & Salawudeen 2021). Drilling deep into the earth is typically required for these geophysical methods to identify potential groundwater indicators. The electrical resistivity and electromagnetic techniques have shown to be the most successful among various geophysical methodologies (Raji & Abdulkadir 2020; Akingboye et al. 2022; Pradhan et al. 2022). One disadvantage of these approaches is that they require a thorough understanding of each kind of aquifer, which may be time-consuming, complicated, expensive, and labor-intensive (Akingboye et al. 2023). Subham et al. (2021a, b, c, 2022a, b, 2023a, b, 2024a, b) and Suranjan et al. (2023) conducted several studies addressing key sustainability challenges in India, focusing on water management, flood and waterlogging risks, agriculture, urban environmental quality, urban quality of life (UQoL), urban vitality, social vulnerability, and livelihood security. The results of these studies in sequence will be mentioned briefly to view details related to the current study and to know the criteria applied in those studies. Whereas, Subham et al. (2021a, b, c) mapped groundwater potential in the Darjeeling Himalaya using geospatial techniques and AHP, identifying high potential in the south and low potential in the northern hills, which supports sustainable water management. Another study (2021) assessed waterlogging risks in Siliguri using the analytic hierarchy process (AHP) and geographic information system (GIS), identifying 46% of the city as high risk and 38% as highly vulnerable, helping plan and mitigate future incidents. Subham et al. (2021a, b, c) evaluated flood risks in the Sub-Himalayan Jalpaiguri region using a multi-criteria decision approach, producing susceptibility, vulnerability, and risk maps to support effective flood mitigation strategies. Subham et al. (2022a, b) emphasized the importance of efficient land use for agriculture in the Sub-Himalayan Jalpaiguri District, creating a suitability map using remote sensing (RS), GIS, and a multi-influencing technique to guide sustainable practices. In a related study (2022), they assessed urban environmental quality in Siliguri using RS and GIS, finding good quality in peripheral areas and lower quality in the city center, aiding sustainable urban planning. Further research by Subham et al. (2023a) evaluated UQoL in Siliguri, revealing significant disparities and clustering, providing insights for spatial planning and sustainable urban policies. Subham et al. (2023b) assessed urban vitality in Darjeeling, Kalimpong, and Kurseong, showing high vitality in town centers, influenced by expansion patterns and European-style blocks. Suranjan et al. (2023) developed an urban social vulnerability index across 146 urban centers in Eastern India, revealing high vulnerability due to moderate exposure and sensitivity, and low adaptive capacity, aiding policymakers in targeted strategies. Subham et al. (2024a, b) assessed West Bengal's sustainable livelihood security index, indicating low performance across most districts and suggesting policies to enhance livelihoods and resilience. Lastly, Subham et al. (2024a, b) examined inequalities in living conditions in Eastern India due to unplanned urban expansion, using GIS and a composite index, and identifying notable spatial variations in living standards. Natural and groundwater resources are essential for ecological, biological, and socioeconomic activities, particularly in arid and semi-arid regions where demand is increasing due to low rainfall and overexploitation (Chaitanya et al. 2021; Rajesh et al. 2021). Several studies have used geospatial techniques like GIS, RS, weighted overlay analysis (WOA), and AHP to map groundwater potential zones (GWPZs) and identify areas for soil and water conservation (Vinay et al. 2023). Rajesh et al. (2021) mapped GWPZ using factors like land use, geology, and geomorphology, finding 49.71% of the area as ‘good’, 41.05% as ‘moderate’, and 9.22% as ‘poor’. Chaitanya et al. (2021) used an integrated approach with AHP, multiple influence factors, and Receiver Operating Characteristic (ROC) to classify potential zones into five categories, providing a framework for sustainable planning. In the Jakham River Basin, Vinay et al. (2023) used eight thematic layers and AHP, showing that 43.88% of the area has moderate potential and 49.21% has a low potential, highlighting the need for recharge planning in semi-arid regions. Kanak et al. (2023) applied similar methods in the Damoh district, revealing that 45% of the area falls under moderate potential, emphasizing groundwater management due to geology and high runoff. Rahul et al. (2023) focused on the Urmodi River Basin, showing 84% accuracy in identifying zones from excellent to poor, underscoring the effectiveness of these methods for sustainable groundwater management in diverse regions. These studies collectively provide a comprehensive approach for assessing, developing, and managing groundwater resources globally under changing climate conditions. For these reasons, it is critical to conduct a comprehensive assessment of groundwater resources using state-of-the-art methodologies such as GIS and RS.

Satellite RS can simplify regional lithological, structural, and geomorphological maps. They show landforms, significant rock formations, folds, faults, lineaments, and fractures because of their multispectral capabilities and wide coverage. Science can efficiently and accurately evaluate remotely sensed satellite pictures using the climate analytical keys through utilizing GIS software (Mahato & Pal 2018). RS data that matched site data, according to Shailaja et al. (2018), groundwater potential was detected using GIS and RS in numerous countries. The facts were precise (Pinto et al. 2015; Kumar et al. 2020).

RS techniques and geographic information systems are currently widely used in studying groundwater resources because of their great benefits. The final GWPZ map can be created by processing data that describes the potential of moving groundwater within a specific geographical area. There are many different international studies that have drawn groundwater maps with different results depending on the geography of the region and relied on them in decision-making because they are highly accurate despite their variation (Andualem & Demeke 2019; Ahirwar et al. 2020). The AHP model has been used practically in many studies related to groundwater mapping and compositional evaluation (Kumar & Krishna 2018; Saranya & Saravanan 2020; Taheri et al. 2020a, b). To clarify the fundamental points on which the AHP relies, previous research, expert opinions, and pairwise comparisons were reviewed in order to determine the relative importance of the various elements affecting groundwater. Whereas, Pradhan et al. (2021) have used the AHP method combined with GIS tools to identify GWPZs in North Gujarat, India. GWPZs were effectively analyzed utilizing integrated geospatial and AHP techniques in the study of Kumar & Krishna (2018) as well. The main source of uncertainty introduced by AHP's reliance on expert knowledge is its restriction (Chowdary et al. 2013). A study has used the frequency ratio (FR) model and GIS to map groundwater potential in the Binary Integer Program (LVM) river sub-basin, Tunisia, considering 18 hydrological factors like elevation, slope, and geology. The data were split 70–30% for model training and validation. The resulting map identifies five GWPZs: very high, high, moderate, low, and very low, aiding in groundwater management (Trabelsi et al. 2019). Another study used RS and GIS to map groundwater prospects in the Pravara basin, analyzing factors like lithology, slope, and rainfall. Using influencing factor (IF) and FR methods in ArcGIS, the area is classified into five GWPZs: very high, high, moderate, low, and very low. The FR method proved more accurate (Areas Under The Curves (AUC) = 73%) than IF (AUC = 69%), providing an efficient tool for sustainable water management (Das & Pardeshi 2018).

The gap between previous studies and the current study is that most of the previous studies relied on one of the environmental or climatic indicators such as normalized difference vegetation index (NDVI), Normalized Difference Water Index (NDWI), or Modified Normalized Difference Water Index (MNDWI) and other indicators as one of the selected criteria. The contribution of the current study is first that the United Kingdom, due to its oceanic climate, lacks such studies, and second, the adoption of new weights to the selected elements that will be adopted in the current study, so that there will be a major role for the geological and climatic criteria in the weights that affect the GWPZ. The primary aim of this current study is to establish the correlation between surface characteristics, encompassing linear features, stream patterns, topography, rainfall distribution, and subsurface attributes, which involve geological and lithological structures. The goal is to identify regions likely to exhibit high infiltration rates. Consequently, the research aims to formulate GWPZs for the examined area, providing valuable insights into determining safe withdrawal ranges and promoting the sustainability of groundwater investment for sustainable development. Additionally, the technical objective of this investigation involves evaluating the feasibility of incorporating a GIS-AHP and the AHP method within an expert system. This integration is intended to facilitate the creation of a spatial database, ultimately yielding the desired outcomes.

All data needed for the study will be downloaded from the USGS-EarthExplorer website (https://earthexplorer.usgs.gov/), which provides satellite images with a resolution of 30 m, which is considered an acceptable accuracy for the current study and can be relied upon when analyzing it and extracting results that will be highly reliable.

In Wales, more than 77,000 people rely on private water supplies for drinking, with 94% of these sources being groundwater-based. These private water supplies have been mapped at the lower super output area level, resulting in the first comprehensive map of this kind for Wales. In certain rural regions, nearly 43% of properties use private water as their primary source. To better understand the complex geology of Wales, it has been simplified into ‘hydrostratigraphic units’, revealing that 97% of private water supplies originate from secondary aquifers characterized by low productivity and limited storage capacity. Notably, about 75% of these supplies come from Ordovician and Silurian bedrock aquifers and their associated Quaternary deposits. The combined groundwater abstraction from both licensed and unlicensed private water supplies across Wales is estimated to be 24.6 million liters per day. During periods of drought, many of Wales' low-storage aquifers are prone to running dry; in 2018 alone, 132 instances of water supply shortages were recorded, although the actual number is likely higher due to underreporting. As climate change leads to more extreme weather events and more people work from home, those depending on private water supplies from low-storage and low-permeability aquifers are increasingly at risk of experiencing water shortages (Farr et al. 2022).

Llandrindod Wells, located in Radnorshire, evolved into a spa town during the 19th century, experiencing a resurgence in the late 20th century as a hub for local governance. Prior to the 1860s, the town's site was common land in Cefnllys parish. Ranking as the fifth largest town in Powys and the largest in Radnorshire, Llandrindod Wells serves as the county town of Powys, situated amidst the scenic countryside of mid-Wales. Once a Victorian spa town, Llandrindod Wells boasts intricate architecture, a golf course designed by Harry Vardon, expansive green spaces, and a picturesque lake. Supporting the philosophy of ‘Llandrindod Wellness’ (Llawen-drindod), the town endeavors to promote, encourage, and celebrate a wholesome lifestyle. Serving as the headquarters for Powys County Council, Llandrindod Wells also stands as a prominent tourism center with well-established transport links, including the Heart of Wales railway line. Geographically, Llandrindod Wells is positioned at 52.242 degrees latitude, −3.379 degrees longitude, and an elevation of 709 feet. The topography within a 2-mile radius displays notable elevation changes, with a maximum shift of 709 feet and an average elevation of 765 feet above sea level. Extending to a 10-mile radius, there are substantial elevation variations (1,795 feet), while a 50-mile radius exhibits even more significant changes (2,969 feet). The immediate 2-mile vicinity of Llandrindod Wells is predominantly characterized by grassland (79%) and artificial surfaces (12%). Expanding to a 10-mile radius, grassland remains prevalent (77%), and within a 50-mile radius, the landscape consists largely of grassland (54%) and cropland (18%) (Welcome to Llandrindod Wells 2022).

Evidence of base metal mineralization, likely of strata-bound type, has been discovered in conjunction with a lava-tuff interface. However, the substantial degree of alteration in the material has hindered a comprehensive assessment of its nature and controlling factors. Despite this, indications suggest that the mineralization is linked to an intermediate lava horizon, likely erupted in a subaerial environment. The mineralization seems to have a limited aerial extent, rendering it of minor significance. The volcanic context of the mineralization raises the possibility of more significant base metal deposits occurring on the volcanic center's flanks within a marine environment. This is based on the idea that basins capable of trapping metal-rich brines may exist in such surroundings. Yet, it is likely that the equivalent age environment to the mineralized material is not exposed in the area, as it is covered by younger rocks. Alternatively, mineralization might be associated with the concluding stages of volcanism in the region, occurring in rocks younger than the primary tuff group. However, there is no direct evidence supporting this hypothesis from either drainage or overburden data (Llandrindod Wells and District u3a 2024).

A mathematical and psychological framework is provided by the AHP, which is utilized for the purpose of conducting complex decision-making. Since it was first developed in 1980 by Saaty (1996), it has undergone numerous iterations of improvement. The aim or problem that you are attempting to solve, a list of potential solutions (also known as alternatives), and the criteria that you will use to evaluate each alternative are the three sections that make up this document. The AHP provides a logical foundation for the conclusion that is required by quantifying the criteria and options for the decision and relating them to the overarching purpose. The AHP excels when there is a need to solve challenging and high-stakes activities. In comparison with other approaches to decision-making, it distinguishes itself by supplying numerical values for criteria and possibilities that were previously intangible. As opposed to being given a ‘correct’ answer, decision-makers (DMs) are directed to find a solution that is in line with their views and understanding of the situation through the use of AHP. AHP introduces a hierarchical structure to decision problems, breaking them down into criteria and alternatives. Central to AHP is the process of pairwise comparisons, where DMs systematically assess the relative importance of different elements. These comparisons undergo a mathematical synthesis, producing weighted values that reflect the DM's preferences. Rigorous checks for consistency enhance the reliability of the derived priorities. AHP's adaptability is a driving force behind its widespread use. It accommodates both quantitative and qualitative considerations, making it applicable to a diverse range of decision problems. The methodology finds practical utility in project management, resource allocation, risk analysis, supplier selection, and strategic planning. Its versatility is exemplified by its integration into decision-support systems and software tools, making it accessible to a broad audience (Saaty & Vargas 2012).

The unique and crucial concept of pairwise comparisons lies at the core of AHP. The term ‘pairwise comparisons’ describes a method whereby criteria and alternatives are systematically ranked according to their relative value. In a pairwise comparison format, DMs give numerical values on a scale to express the strength of their preference or judgment. In order to turn subjective judgments into a calculable basis for decision-making, a mathematics-based evaluation method is generated by using pairwise comparisons to provide a ranking of criteria and alternatives. When confronted with complicated challenges including empirical measures, subjective evaluations, and expert evaluations, the AHP's adaptability to real-world problems has shown to be invaluable (Kumar & Krishna 2018). A number of well-known decision-making algorithms use the Pairwise Comparison Matrix (PCM) to rank criteria or alternatives in order of importance. These algorithms include AHP and its broader cousin, the analytic network process (ANP). Nevertheless, PCM is only useful for small choice concerns due to the many complexities that make it hard to employ. Some examples of these problems include (1) the high dimensionality curse, which forces DMs to rely on an excessive number of pairwise comparisons; (2) inconsistent preferences; and (3) the restricted rationality of DMs, which leads to imprecision in preferences. Consequently, PCM is intelligently partitioned into several parts based on dependency scores using an early-stage binary integer algorithm. As a result, the PCM is more consistent and fewer pairwise comparisons are needed. Since the subsets do not overlap, we can easily find the most independent pivot element that connects them, which allows us to derive global weights for the components from the original PCM. Both AHP and ANP techniques are subject to the proposed Binary Integer Program (BIP). Importantly, the DM's subjective determination of the ideal number of subgroups introduces the possibility of biases and judgment mistakes. To deconstruct PCM into subsets that have been optimally identified, the second step proposes a tradeoff PCM decomposition approach. A BIP is implemented to strike a compromise between the two competing goals of (1) minimizing the amount of time spent on pairwise comparisons and PCM inconsistencies and (2) ensuring that the weights are accurate. By applying it to AHP and comparing it to other well-established approaches, the methodology demonstrates its benefits. As a third step, we use a method of moments approach to generalize a broad variety of imprecise pairwise comparison distributions using a beta distribution. Afterwards, a model based on non-linear programming is created to determine the weights of PCM elements in a way that maximizes the DM's preferences while reducing inconsistencies. In order to confirm that the suggested technique is correct, comparative tests are carried out utilizing datasets from the literature. The allocation of weights to various thematic maps and their characteristics was determined by conducting a thorough analysis of literature, previous research discoveries, and field expertise (Eugene 2013). The procedure included normalization using Saaty's AHP technique (Goepel 2013).

Many factors affect groundwater potential, such as drainage density, precipitation, slope, soil, geology, and LULC. Possible further considerations include those pertaining to the research area's unique setting. Researchers' access to various data resources dictates how they use thematic layers in groundwater analysis, which in turn varies among research areas. Possible sites for groundwater were being researched as part of the continuing investigation. Factors such as drainage density, rainfall, slope, soil, geology, and LULC are all part of this research (Table 1). The components were hand-picked for their proven significance, and the weights assigned to the relationships between them were based on what was known from previous studies. Bear in mind that the elements with higher weights are thought to have a stronger impact on the groundwater system's established connections, while the factors with lower weights are thought to have a hardly noticeable effect. The use of a WOA inside the ArcGIS program (Version 10.2.2) (Selvam et al. 2015) was crucial in enhancing our comprehension of these intricate linkages and providing a comprehensive depiction of the possible groundwater zones. It is possible to lay a solid groundwork for studying and comprehending the patterns of groundwater distribution in the research region by using this technique to thoroughly analyze the complicated properties of the many components. The results of the systematic review are shown in Table 1, which scores and classifies each theme level and its sublevels. The impact on groundwater potential was carefully considered while evaluating each theme layer and its associated sublevels. As indicated in Table 2, the AHP technique was used to assign weightings to each level after experts evaluated each theme layer and its sublevels. Table 2 shows that each component was evaluated using the figure-shown rating scale of 1–5. According to Saaty (1996), there is a GWPZ table that assigns a particular rating to each factor. This is crucial for understanding how they significantly impact the probability of groundwater occurrence. The analytic hierarchy technique systematically assesses pairwise comparisons of variables inside specified clusters, following Saaty's scale that measures the relative intensity of relevance. This information is shown in Table 3.

Table 4 displays the RCI values obtained using Saaty's category for various n values. A CR value of 0.10 or below is considered acceptable when the sample size is 5. Similarly, a CR value of 0.09 or lower is acceptable when the sample size is 4, and a CR value of 0.05 or lower is acceptable when the sample size is 3. Alternatively, the relative importance of each criterion will be reassessed to enhance the consistency of judgment and prevent any inconsistencies.

Let A represent the normalized weight of the i thematic layer, B represent the rank value of each feature class related to the j class, m represent the total number of thematic layers, and n represent the total number of feature classes in the thematic layer.

Integrating the AHP and GIS with other multi-criteria decision-making (MCDM) methods, such as fuzzy AHP, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), or ANP, offers a robust framework to enhance decision-making by cross-validating and comparing results, thus addressing the inherent limitations of each individual method and providing more comprehensive insights. AHP is widely used to decompose complex decision problems into a hierarchical structure and to assign weights to criteria based on expert judgment. However, the method's reliance on subjective judgments can introduce biases and inconsistencies, potentially skewing results. Integrating AHP with fuzzy AHP, which incorporates fuzzy logic to manage uncertainty and vagueness in expert assessments, helps model the ambiguity that often accompanies real-world decision scenarios, thereby increasing the robustness and reliability of the decision-making process (Ferdousi et al. 2021). This integration allows for a more flexible evaluation of criteria weights and reduces the impact of subjectivity. Similarly, combining AHP with TOPSIS, an MCDM method that ranks alternatives based on their proximity to an ideal solution, provides a more balanced approach by considering both the best and worst possible outcomes (Yousefi et al. 2022). This dual consideration enables DMs to gain a deeper understanding of the relative performance of each alternative, ensuring that decisions are not just driven by the highest-ranking option but are also informed by those closest to optimal conditions. Moreover, integrating AHP with the ANP can address one of AHP's significant limitations – its assumption of criteria independence by allowing for the modeling of interdependencies among criteria. This is particularly valuable in complex spatial decision-making scenarios involving GIS, where different criteria, such as land use, topography, and soil type, often interact and influence each other (Chakraborty et al. 2022). The combination of these methods enables cross-validation, where the results derived from one technique can be corroborated by another, thereby enhancing reliability and reducing uncertainty in decision-making (Al-Douri et al. 2023). This hybrid approach helps counterbalance the weaknesses of one method with the strengths of another, resulting in more accurate, transparent, and credible outcomes. Such integrative frameworks have proven particularly effective in areas like environmental management, urban planning, natural resource management, and disaster risk assessment, where decisions are complex, multidimensional, and fraught with uncertainty. By leveraging the complementary strengths of AHP, GIS, and other MCDM methods, DMs can create a more versatile and effective decision-support system that improves the overall quality and defensibility of the decision-making process (Machado et al. 2023).

DEMs focus on computer representations of the Earth's surface devoid of features like buildings and vegetation. Continual terrain elevation readings over a topographic surface, be it Earth or another planet or moon, are what generate a DEM. Many free apps on the web make use of DEMs, although their quality is sometimes lacking. While there are a number of excellent sources, such as Advanced Spaceborne Thermal Emission and Reflection (ASTER) and Shuttle Radar Topography Mission (SRTM), with a resolution of 30 m, for example, on the polar or ocean masses, their orbit does not circle the world. Consequently, we deal with DEM because it is the greatest approach for building a DEM in GIS. In order to cover the entire planet with the correct elevation points, GIS allows us to discover or produce data from a wide variety of sources and approaches. Each raster cell in a DEM contains elevation data. Calculations, manipulations, and additional analysis of a region, particularly analysis based on elevation, are common uses for DEMs. Converting a DEM into a derivative map is a breeze with ArcGIS's built-in tools. When working with ArcMap, there are a few fundamental operations that may be performed. Tools from the Spatial Analyst category are involved here. When analyzing landscapes, DEMs work well. When doing analyses, ArcMap offers a plethora of more sophisticated and specialized capabilities and applications. Analysis of hydrology, geology, and geomorphology, as well as landscape design, fall under this category.

Several groundwater regulating characteristics must be considered in order to identify prospective groundwater resources and pick the optimal site for future groundwater development and management. Thematic maps were combined using the GIS spatial analysis tool's WLC according to their weights and rates. When making decisions using a GIS, the WLC model is among the most popular options. Common areas of application include site selection, resource appraisal, and land use/suitability analyses. This method's widespread use may be attributed, in large part, to the fact that it requires little in the way of map algebra operations and cartographic modeling to integrate into an existing GIS setting. The approach is particularly appealing to DMs since it is simple and straightforward.

The hydrologic cycle would not be complete without rainfall, which affects the potential of groundwater. Groundwater recharging is more likely during periods of heavy rainfall and less likely during periods of light rainfall. A premier facility for the study of climatic change in all its forms, the Climatic Research Unit (CRU) at the University of East Anglia, collects rainfall data annually. The CRU produces a number of climatic datasets, including regional and global temperature, precipitation, pressure, and air circulation measures. One well-liked climate dataset, CRU gridded time series, covers every landmass on Earth (with the exception of Antarctica) and has a grid precision of 0.5° latitude by 0.5° longitude. To compile this dataset, monthly climatic anomalies were extrapolated from networks of weather stations that cover a vast area. The present study will collect rainfall data for the study region for 2023 and analyze it monthly to see how much of an influence climate change, both positive and negative, will have on precipitation rates. Using GIS software, we will achieve CRU rainfall data according to this technique. Making a NetCDF raster layer, projecting data, creating composite bands, using cell statistics and map algebra, transforming raster data to point, and lastly, using the inverse distance weighting (IDW) interpolation method as the interpolation technique are all steps in this approach.

In this case, Z_p is the unknown point's value, z_i is the known point's value, d^p is the distance to the known point, and n is an exponent that the user chooses (usually 1, 2, or 3).

Groundwater recharge is greatly affected by the type of rock that is exposed to the surface. The percolation of water is affected by geology, which in turn affects groundwater recharge. The recharge of groundwater is greatly influenced by the geology of any given place. Undivided Silurian rocks, undivided Ordovician rocks, undivided Carboniferous rocks, and undivided Devonian rocks are the four types of geological formations encountered in the research region, as shown in Figure 7. From oldest to youngest, the Paleozoic Era is divided into the Cambrian (538.8–485.4 million years ago), Ordovician (485.4–443.8 million ago), Silurian (443.8–419.2 million ago), Devonian (419.2–358.9 million ago), Carboniferous (358.9–298.9 million ago), and Permian (298.9–252.2 million years ago) periods. The majority of the sub-basin is covered by rocks from the Silurian and Devonian periods, in comparison with other rock types. Because of the high infiltration rates of certain rock types, water can seep through the soil and eventually reach the aquifer. In light of the foregoing, the geological map has assigned a total of five stars to the undivided Silurian rocks, four stars to the undivided Ordovician rocks, and three stars to the undivided Carboniferous and Devonian rocks.

Areas with low drainage density tend to have less surface runoff and higher rates of infiltration, making them more suitable for groundwater recharge. In contrast, regions with high drainage density experience greater surface runoff and reduced infiltration, which lowers their groundwater potential. Rainfall is a crucial factor for groundwater recharge; areas with more rainfall generally have better groundwater potential due to increased infiltration. However, the effectiveness of rainfall in recharging groundwater also depends on its intensity, duration, and distribution. Slope also plays a role, as gentle slopes promote infiltration and groundwater recharge, while steep slopes encourage runoff and decrease infiltration, reducing groundwater potential. LULC significantly impacts groundwater recharge. Dense vegetation and forested areas facilitate infiltration and enhance groundwater recharge, whereas urbanized or built-up areas with impermeable surfaces hinder infiltration and increase runoff. Agricultural lands can either boost or diminish recharge, depending on the specific practices, such as irrigation methods. Soil type is another critical factor; sandy soils, with their high permeability, allow for greater infiltration and recharge, while clayey soils, with low permeability, limit infiltration, and encourage runoff. The geological characteristics of an area also determine groundwater potential, as permeable rocks like sandstone, limestone, or fractured rocks are more conducive to groundwater recharge and storage, whereas impermeable rocks such as granite limit recharge possibilities.

Groundwater potential study applications are crucial. As far as the GWPZ was concerned, it was one of the more cutting-edge uses. Several elements, including drainage density, rainfall, slope, soil, geology, LULC, and so on, must be investigated in order to extract the GWPZs for the present study's application. Results from this study demonstrate the viability of evaluating GWPZs utilizing GIS, RS, and MCDM techniques. The three parts of the technique are as follows: first, theme layer production; second, AHP application to collect weights; and third, overlay analysis to estimate the GWPZ. In order to generate the thematic layers, it has used GIS and data from remotely sensed satellite images that have been obtained from the USGS website to digitize preexisting maps. It has been offered utility weights for the alternatives using AHP. Through the application of weighting and overlay analysis on several thematic maps, the GWPZ has been identified. Five types of GWPZs have been discovered in the Llandrindod Wells region using RS, GIS, and MCDM approaches.

GWPZs in the Llandrindod Wells watershed in Wales, UK, were the focus of this investigation, which used GIS and RS methods for its evaluation. Using the data that was available, the thematic layers that impact GWPZs, including drainage density, rainfall, slope, soil, geology, LULC, and elevation maps, were being generated. Individual weights and subclass divisions have been applied to this conventional data provided by satellite images based on decisions made in the literature and by experts in the field. The prospective groundwater zones are as follows: Very high (147.7 km²), high (1,943.4 km²), moderate (2,318.7 km²), low (662.2 km²), and very low (4 km²), with a distribution and extent of 2.9, 38.3, 45.7, 13, and 0.08%, respectively. Needless to say, the research area includes regions with an abundance of groundwater; in fact, these regions constitute around 87% of the whole study area.

Indeed, it is necessary to implement similar studies in Iraq. However, the conditions in Iraq are markedly distinct, characterized by a dearth of rainfall, elevated temperatures, and severe evaporation. Consequently, the use of the approach described in the present research would be rendered ineffective. Consequently, scientists must devise techniques to explore groundwater and determine its possible locations and existence by including additional climatic factors specific to the study region and its needs. The primary emphasis should be placed on the severe climatic elements that have a direct impact on the study area. Various climatic indicators, such as the NDVI, soil-adjusted vegetation index, normalized difference built-up index, index properties of soils, and others can be utilized to determine their significance and incorporate them into the process of generating a potential groundwater map.

The management of groundwater resources is critical for ensuring sustainable water supply, especially in regions dependent on groundwater for drinking, agriculture, and industry. As demand for water increases and climate change drives more extreme weather events, effective groundwater management becomes increasingly important. Challenges such as overextraction, contamination, and aquifer depletion can compromise water quality and availability, impacting ecosystems and human communities alike. To address these issues through depending on the results of current research, there is a need for innovative strategies and policies that integrate scientific understanding with local context. Future research should focus on improving aquifer characterization, developing advanced monitoring and modeling techniques, and exploring the socioeconomic aspects of groundwater use. A deeper understanding of these elements will help DMs to create more resilient groundwater management frameworks that can adapt in different regions according to their vision and the geological and hydrological characteristics of the region, by relying on current research as a starting point that can be used in other regions to evolving environmental conditions and societal needs.

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.