Erbil Basin Groundwater Recharge Potential Zone Determination Using Fuzzy-Analytical Hierarchy Process (AHP) in North Iraq

Severe water scarcity has occurred in the Erbil Basin (EB) due to climate change and mismanagement of water resources during the past three decades. Assessment of the potential area of groundwater recharge is extremely significant for the protection and management of groundwater systems and water quality. This research aims to use the Fuzzy-Analytic Hierarchy Process (F-AHP) technique to recharge the aquifer in places in the EB that are likely to be groundwater recharge areas in a geographic information system (GIS) environment. GIS, remote sensing (RS), and F-AHP techniques were used to map the groundwater recharge potential zone in EB. Eight different geo-environmental factors were used to determine potential groundwater areas, namely: rainfall, lithology, geology, soil, slope, lineament density, land use/land cover (LULC), and drainage density (D d ). Then, the weights of the different thematic layers were assigned using a pairwise comparison matrix through the F-AHP. The total groundwater potential zone was shown to cover a very high area of 210.85 square kilometers (km 2 ), a high area of 188.94 km 2 , a moderate area of 573.06 km 2 , a low area of 1956.48 km 2 , and a very low area of 216.34 km 2 , according to the groundwater recharge potential zones (GWRPZs) map. As a result, nearly one-third of the areas investigated were found to have moderate-to-very high groundwater recharge potential. This type of research can provide decision-makers and local governments with a broad perspective on current and future planning for groundwater scarcity. .


Background
Groundwater is a valuable resource and it is one of the important freshwater sources for domestic use, agriculture, and industry [1]. At present, nearly 34% of the world's water resources belong to groundwater [2]. Groundwater occurs in almost all landscapes [3], and all surface water features include streams, wetlands, lakes, reservoirs, and estuaries, which are usually hydraulically connected to groundwater [4].
Moreover, the natural and climatic environment of the landscape determines the presence of groundwater. For instance, a stream in a humid climate may receive groundwater in ux, but a stream in an identical physiographic environment in a dry climate may lose water to groundwater [4]. Glacial or elevation from 200 meters in the south to 500 meters in the northeast. Typically, these hills are narrow, with extremely wide plains in between [41].
The amount of recharge of the aquifer storage in the recharge area, as well as the velocity of the porous media in the region, determine the increase in groundwater level [40]. The major groundwater basins are formed by broad synclinal valleys that are lled with sedimentary sequences ranging in age from Late Miocene to Recent. The main groundwater divides, especially in the elevated parts of the Low Folded Zone, where groundwater discharge occurs along streams and rivers, coincide with surface water ow. The potential for groundwater development along these streams and rivers could be signi cant [44].

Study area
This research is being carried out in the EB, which includes Erbil City, the capital of Iraq's Kurdistan Region (KRI). The geographical setting for the EB is between latitudes 35° 46' N and 36° 34' N and longitudes 43°3 4' and 44° 19' E. The GZR is the most important branch of the Tigris River, which springs from southeastern Turkey at an altitude of more than 4,000 m above sea level and ows into northern Iraq [45]. The Lower Zab River, on the other hand, extends from northeast Iran to Iraq and is located south of the GZ. The normal temperature ranges from 1°C in December-February to 44°C in July-August. The elevation ranges between 171 and 1091 meters above sea level and consists of numerous hills and at terrains as the most prominent morphological features of semi-arid climatic conditions with the potential for direct run-off, Figure 1. This area was chosen because it contains the largest groundwater reservoir in the Erbil Governorate and is one of the most important groundwater aquifers in the Middle East, with conglomerates, sandstones, sand, and gravel forming the majority of the aquifers [41].

Data set
For this study area, USGS Earth Explorer (https://earthexplorer.usgs.gov/) was used to download Landsat 8 OLI to prepare the LULC map, SRTM-DEM to obtain average slope, drainage network information, and drainage density map. The geological and lithological maps were obtained from the Directorate of Surveying of Iraq, while the soil map was gained and digitized from Iraq's exploratory soil map from 1960. Table 1, summarizes the details of all used data and output layers. This study combined GIS, RS, and fuzzy-AHP approaches to identify and assess groundwater recharge potential zones. Eight in uencing factors were selected, namely; rainfall, geology, lithology, slope, drainage density, lineament density, LULC, and soil. Different tools were used to create various thematic maps, which were then converted to raster format with a spatial resolution of 30 meters and projected in UTM Projection, Zone 38 N, and WGS 84 Datum. The main factors that in uence groundwater recharge potential in the study area are listed in Table 2. For the computation of weights of different features and thematic layers, Saaty's multi-criteria evaluation, which is the most applicable approach for solving problems, was used. The weighted index overlay method was used to combine all of the thematic layers. The data layers were then given weights to re ect their relative importance [22].
In general, in order to comprehend the concept of overlay analysis, two terms must be understood, "in uence" and "scale value". "In uence" refers to a layer's overall importance, which is measured in percentages for each thematic layer, while the "scale value" is based on the importance of the features in the layer [46]. The least important value is one (1), and the most important value is ve (5). In AHP, the percentage in uence will compare each factor pairwise. In the current study, for example, AHP will compare rainfall to the other seven thematic layers, as well as other factors. Then, on a scale of 1 to 9, a value will be assigned. Nine (9) means "extremely important," ve (5) means "strongly important," and one (1) means "equally important.", Table 3 [22].

Preparation and computation of the thematic layers
Rainfall data were collected from various rain gauging stations throughout the study area for the twenty years 2001-2020 and summed up into an annual average. Average annual precipitation was calculated and projected to generate a rainfall thematic layer using the IDW interpolation method. For overlaying purposes, the rainfall map was classi ed into ve classes. The geological map was produced by the general directorate of the survey. The geological map of the study area was categorized into ve classes, namely: polygenetic and slope deposits, Bai Hassan, Fatha, Injana, and Mukdadyah. In terms of the sur cial lithology map, it has been prepared using the geological map from the general directorate of the survey. The EB is covered most by Quaternary sediments, which are considered most important for groundwater storage due to their high hydraulic conductivity, followed by sandstone, silty sandstone, and silty/sandy mudstone [47]. Moreover, Landsat 8 operation land imager for 2021 was downloaded from the Earth Explorer USGS website and processed in ENVI 5.3 and ArcGIS 10.7.1 to produce a land use/land cover map. Bands 1, 2, 3, 4, 5, 6, and 7 were composited and generated the LULC map. Henceforward, the study area was classi ed into ve classes namely, barren land, built-up, cropland, rangeland, and water bodies. After digitizing the training samples, the signature le was developed for supervised mapping through a maximum likelihood classi er. On the other hand, the sur cial lineament density was produced using hill shade under raster surface, arc toolbox in ArcGIS of Landsat 8 OLI. A group of polylines was de ned and produced lineament in ArcGIS 10.7.1. Drainage density (D d ) as a signi cant factor in groundwater exploration was also prepared by dividing the total length of streams in the area. The slope layer was extracted from DEM at 30 m and divided into ve classes. Slope affects the amount of water availability for in ltration due to effects on local soil water balances [48]. Lastly, the digital soil map produced by FAO/UNESCO, in 1995 was prepared and used in this study.

Fuzzy-AHP
In the "fuzzy set" theory, there is a very precise and clear boundary to indicate if an entity belongs to a well-de ned "set" of entities, and there is a sharp, crisp, and unambiguous distinction between a member and a nonmember of any well-de ned "set" of entities. To put it another way, when someone asks, "Is this entity a member of that set?" Either "yes" or "no" is the answer. This holds true in both deterministic and stochastic situations. "What is the probability of this entity being a member of that set?" is a question that can be asked in probability and statistics. Thus, the fuzzy set theory will be seen to be a natural extension of the classical set theory, as well as a rigorous mathematical concept [49]. The concept of fuzzy sets is a conceptual and mathematical framework for investigating imprecise and ambiguous phenomena [50], and it enables the individual to operate in uncertain and ambiguous situations and to solve poorly posed problems or problems with incomplete information [51].
Furthermore, the process of the 'fuzzi cation', is the transformation of a crisp set into a fuzzy set or a fuzzy set into a fuzzier set. This operation essentially converts precise, crisp input values into linguistic variables, which are then converted into membership functions [52]. De-fuzzi cation, on the other hand, is de ned as the process of converting a fuzzy member into a crisp member or reducing a fuzzy set to a crisp set [53].
Generally, this judgment should have been supported by experience in the study area. A verbal judgment should follow certain guidelines, such as consistency ratio (CR), to be acceptable. Moreover, if the CR consistency ratio is less than 0.05 for the 3x3 matrix, 0.09 for the 4x4 matrix, and 0.1 for larger matrices, the pairwise comparison matrix is said to be consistent [54]. The CR for all elements in the current study was less than 0.1, indicating that the matrix and element evaluations were consistent.
The pair-wise comparison matrix was generated using a denotative 9-point scale, with 1, 3, 5, 7, and 9 representing important, moderately important, strongly important, extremely strongly important, and extremely strongly important, respectively, Table 4. 6 (5, 6, 7) 8 (7,8,9) Fuzzy-AHP is a dominant approach and has been used by many scientists to determine the potential area of groundwater [29]. In this work, the fuzzy triangular number technique was used to represent a pair-wise comparison of GWRPZ selection. The fuzzy-AHP method was considered in this study since the traditional AHP is not able to deal with the inaccurate nature of the linguistic calculation while making pairwise comparisons of the criteria [37].

Triangular fuzzy numbers
The fuzzy triangular number µ(x) can be depicted as in Figure 2, which is easily presented in the formula 1.
The fuzzy numbers 1, 2, and 3 represent the lower (l), middle (m), and upper (u) numbers from the triangular membership function, respectively, whereas the function value µá (x) is referred to as the grade of membership of (x) in á (x-axis). The fuzzing values will be used to replace the single and intermediate numbers in the fuzzy scale importance number (l, m, u). Where l ≦ m ≦ u, simultaneously, l=m=u, and it is a non-fuzzy number by convention.
Furthermore, for two triangular fuzzy numbers Ᾰ1= (l 1 , m 1 , u 1 ) and Ᾰ2= (l 2 , m 2 , u 2 ), the main operational laws are expressed as formula 2: Formula 3 will be used to convert the crisply valued into fuzzy numbers in order to obtain the fuzzy pairwise metrics.
Fuzzy-AHP was proposed by Buckley (1985), in which calculating weights through the geometric mean will be applied, by multiplying two fuzzy numbers, Formula 4 multiplied fuzzy number equation.
Then, the fuzzy geometric mean and the procedures for determining the weights of the criteria can be calculated by Buckley (1985) as formula 5: By implementing formula 6 for adding two fuzzy numbers, the reciprocal of the sum will be calculated.
Finally, the de-fuzzi cation method through applying the center of area (COA) method, formula 7, will be used in order to obtain the fuzzy weights. The end process is obtaining the total normalized weight, which is 1.
The nal step is to conduct a test to determine the extent of consistency associated with the comparison matrix using the consistency ratio (CR) formula 8.

Empirical illustration
The AHP model has been developed through four stages; weight assignment, development of a pairwise comparison matrix, a consistency ratio (CR) check for fuzzy pairwise comparison, and determining weight normalization. Thus, the next step will be to replace the scale of relative importance for all numbers after creating the pairwise comparison matrix, Table 5, and the scale of relative importance with the crispy values, Table 6.   Thus, the fuzzi cation, formula 2, is replacing the scale of relative importance like (1, 3, 5, 7, and 9) with fuzzy numbers. On the other hand, the fuzzy reciprocal numbers (1/3, 1/5, 1/7, and 1/9) in Table 5 will be converted to the fuzzy number by using formula 3 as presented in Table 6. Then, the geometric mean based on Buckley [55] will calculate the weights. Consequently, the fuzzy geometric mean value will be calculated using the formula 4 to multiply two fuzzy numbers. As a result of taking the fth root, the lower, middle, upper points will be all multiplied by the lower, middle, and upper points, Table 7. All other values will be calculated in the same way.
A 1 * A 2 = (l 1 , m 1 , u 1 ) * (l 2 , m 2 , u 2 ) = (l 1 + l 2 , m 1 + m 2 , u 1 + u 2 ) (5)  Furthermore, the De-Fuzzi cation process will be applied by using formula 6. Thus, the lower, middle, and upper weights were added together and divided by 3 in order to get the fuzzy weights. Usually, the total of the criteria weights is not acceptable due to its values being more than 1. Therefore, the weights should be normalized by dividing each weight by the total weight in order to obtain the normalized weights, as shown in Table 8. As a result, the steps outlined in Table 7 were applied to all criteria to obtain normalized weights for each criterion and sub-criteria in order to determine the potential groundwater zones in the study area.

Groundwater Potential Index
The Groundwater Potential Index (GWPI) is a dimensionless quanti cation index method that combines thematic layers to produce groundwater potential scores for various locations. The fuzzy-AHP method was used to determine the ratings and weight values for each of the parameters [32], which were then used to calculate the GWPI as follows: GWPI= [R w * R r + L w * L r + Ge w * Ge r + Sl w * Sl r + Lu w * Lu r + D dw * D dr + LN w * LN r + S w * S r ] (9) where GWPI stands for Groundwater Potential, R stands for rainfall, L stands for lithology, Ge stands for geology, Sl stands for slope, Lu stands for land use/land cover, Dd stands for drainage density, LN stands for lineament, and S stands for slope. In addition, the weight of each thematic layer used is marked by the subscript 'w,' and the rating of the features in each thematic layer is marked by the subscript 'r.' The parameters in this study were weighted based on the study area's hydrogeological properties and expert opinion, and the weight values and ratings of each parameter and sub-class were then used to calculate the groundwater potential index (GWPI) using equation 9.

Results
The availability of groundwater generally depends on rainfall, geology, lithology, soil, lineament density, drainage density, lulc, and other factors. Hence, all thematic maps of the current study area have been prepared according to the previously described methods. The following is a description of the prominent aspects of these topics. Due to the identi cation of appropriate recharge zones, eight factors were used to determine the highly penetrable and porous terrain.

Rainfall
The rainfall map of EB is shown in Figure 3a. The maximum amount of rainfall is about 576.9 mm/year, and the minimum value is about 281 mm/year. Furthermore, as you move westward, the amount of precipitation decreases gradually. The mean annual precipitation of this basin was about 467 mm during the last 20 years (2000-2020). Generally, the climatic factors that affect the hydrogeological conditions of groundwater aquifers have a direct impact on these aquifers. Precipitation is the primary source of groundwater recharge, and it has an impact on other meteorological factors such as temperature, relative humidity, wind speed, and direction [43].

Geology
Five geological classes were recognized in the EB based on their relative importance and impact on groundwater potential. The study area's dominant units are the quaternary deposit (polygenetic deposit) and the Bai-Hassan formations, which have good permeability and play an important role in groundwater supply and characterize the rainwater harvesting area. Thus, the surface drainage network contributes to the accumulation of surface ows in this area. Different geologic materials will have different characteristics for holding water beneath an aquifer system, Figure 3b, [43].

Lithology
Mainly, the occurrence and distribution of groundwater are in uenced by lithology, thus it is one of the signi cant factors that control the quantity and quality of groundwater occurrences in the study area. Consequently, there is an important relationship between lithology and the availability of groundwater, which in uences groundwater recharge by controlling water ow percolation [56]. The lithological features of the study area are mainly formed by the polygenetic environment, which is mainly a composite of alluvial sediments such as silt, clay, sand, and a mixture of gypsum and iron; and conglomerate aquifers in the quaternary and Pliocene within the formation of Bai Hassan [43]. A small portion of a uvial environment can be seen in the north of the study area, with a little portion of the shallow water sub-continental environment and lagoon, Figure 3c. The lithology theme was included in this study as an important factor affecting the groundwater, despite some investigations having observed lineaments and drainage characters as a function of primary and secondary porosity [57].

LULC
Land use/cover is a signi cant factor in recharging groundwater. It encompasses the distribution of soil sediments, urban areas, and vegetation cover. The study area was categorized into ve classes namely; cropland 1389.05 km 2 , which has a maximum area, rangeland 1013.3 km 2 , built-up area 431.32 km 2 , barren land 305.11 km 2 , and water bodies 17.063 km 2 . Cropland and rangeland have a better ability to recharge and retain groundwater than built-up and barren land areas. Cropland is found throughout the northwest and southwest of the study area, while the rangeland covers the eastern part of the basin, Figure 3d. Groundwater is more likely to be recharged and stored in dense agriculture areas, whereas in ltration and recharge are less likely in exposed bare rock and built-up areas [58].

Soil Type
Another signi cant parameter that in uences the occurrence and distribution of groundwater is soil, which has an important role in recharging groundwater [56]. Four types of soil dominate the study area, viz., (1) brown soils, deep phase, (2) brown soils, medium and shallow phases, (3) lithosolic soils in limestone, and (4) lithoslolic soils in sandstone and gypsum. Moreover, the rst type dominates 83.1% of the total area and has gravel silt-clay layers with surface cracks under the brown soil layer, which is an appropriate type of soil for in ltration. The second type covers 13.64% and represents brown soil with medium to shallow phases covered by a layer of gravel and silty loam, which can be considered a proper type of soil for in ltration, while the third and fourth types are very poor classes for in ltration. Low to moderate runoff potentials are present in most of the areas ( rst and second types) due to the high rate of in ltration and consist mainly of sand, clay, and Bakhtyari group conglomerates, Figure 4a, [59].

Slope
The slope gradient has a direct impact on rainfall in ltration. Since the water ows quickly down a steep slope during rainfall, it does not have adequate time to in ltrate the surface and recharge the saturated zone. Thus, the higher the slope, the higher the runoff will be. From a groundwater perspective, the lower area with lower values will have higher groundwater potential due to its lower slope and vice versa. de ned as "very poor" due to the higher slope, which causes higher runoff due to increasing the amount of runoff with slope. Thus, roughly more than 70% of the EB is dominated by the at terrain in the central part, which reasonably reduces the runoff movement, Figure 4b.

Lineament Density
A lineament density is a manifestation of fractures, faults, and joints in a geographical landscape, which can be calculated by dividing the total length of all lineaments by the area under consideration. It is also a good indicator for understanding the relationship between the surface water in ltration and fracture structure, thus signifying a permeable zone. Increased lineament density designates the highest potential for groundwater recharge [60].
The lowest value of lineament density ranges between 0 and 0.15 km/km 2 . This part covers almost 1011 km 2 of the total area. However, the highest value of lineament density in the study area ranges between 0.60 and 0.75 km/km 2 and covers 216 km 2 , which means the higher the probability of groundwater Accordingly, the lower the drainage density, the higher the probability of groundwater. Therefore, the lowest D d surrounds the perimeter of the study area and covers 18.17% of the total area, which indicates a good potential zone for recharging the aquifer. Whereas the areas towards the center of the study contain moderate to high drainage densities.

Discussion
The results showed that groundwater in EB follows the topography of the region, thus it ows from the east to the west, which was also con rmed by [40,63,64]. Moreover, the east side revealed that it contains deeper groundwater levels than the west part of the study area, which has shallower groundwater levels, Figure 5. Therefore, groundwater withdrawals have expanded dramatically during the last 30 years, globally and in EB particularly.
The groundwater recharge potential map was created by using weighted overlay analysis in a GIS environment through combining multiple environmental factors. The results showed that the rainfall and the lithological factor were the most suitable and promising groundwater potential zone due to the good porosity and permeability caused by the loose and unconsolidated sediments, which almost covers 80% of the current study [63]. Furthermore, groundwater is a gravitational pull and ows slowly (years, decades, even thousands of years) through pores and fractures in the rocks, eventually discharging (draining) into springs, rivers, lakes, and the sea [9]. Therefore, as more permeable sediments become available, aquifer transmissivity often increases as in the central part of the syncline, along with river courses, and within alluvial fans in the study area [44].
The effect of the rainfall was the major source of groundwater storage. Thus, the higher the precipitation intensity, the greater the groundwater recharge and vice versa. Overall, the GWRPZ map illustrates that the study area is a suitable zone for aquifer recharge. Therefore, this study is useful for decision-makers to de ne a plan to recharge groundwater. Furthermore, a careful management plan is required to make better use of the groundwater resources [20].
The sustainable management of water resources that improves the environmental condition and increases water management can be provided through the involvement of rainwater harvesting areas and groundwater recharge zones, hence overcoming the problem of water scarcity [7]. The analysis of the groundwater potential area will depend on the experience of the study area. Therefore, the better you know the site, the better you can decide which factor to use. The AHP approach was used to obtain weight values for all the thematic layers, followed by the fuzzy rating values of the attribute classes and sub-classes for each individual thematic layer. The AHP approach was used to obtain weight values for all the thematic layers, followed by the fuzzy rating values of the attribute classes and sub-classes for each individual thematic layer. Table 9 displays that the most relevant parameter in the current work is lithology, having a weight value of 0.3843, followed by rainfall and geology, with weight values of 0.2515 and 0.1599, respectively. Moreover, other factors affecting groundwater potential with their descending weights are slope (0.0696), drainage density (0.0563), LULC (0.0393), soil (0.0236), and lineament density (0.0155).
However, the F-AHP speci ed the total weight and rating values for each individual sub-category. From Table 9, the rainfall sub-class (528-576) was the most effective parameter with a weight value of 0.1840, followed by sub-categories of shallow water to the sub-continental environment and uvial environment factors with weight values of 0.1551 and 0.1428, respectively.
Furthermore, the groundwater potential zone, Figure 5, shows that about 210.85 km2 of the total area falls under the "very high GWRPZ," however, the "high" GWRPZ occupies about 188.94 km2, and 573.06 km2 lies under the "moderate" GWRPZ. Conversely, the "low GWRPZ" covers the largest area of the study area, with 1956.48 km2, whereas 216.34 km2 of coverage lies under the "very low GWRPZ." Thus, the sustainable development of groundwater in this study basin could bene t from the GWRPZ map. This implies that recharging the groundwater is best done in areas with "very high" and "high" potential groundwater. This can also be con rmed by the depth of wells, which have lower depths in meters compared to the other well sites.
Frankly speaking, arti cial recharge of aquifers is necessary for the research area to avoid low groundwater levels, increased expenses for the user, reduced water availability in water bodies, and land subsidence [65]. There are adequate at regions that are suitable for spreading water in a thin sheet and achieving a higher rate of vertical in ltration of the study area. Moreover, around one-third of the area has a very high potential for in ltration since it forms from the study area's alluvial areas.
Additionally, it is observed that wells, symbolized in a triangle, are located in the north and south-western part of the basin and are mostly close to and even far from the GZR, have a very high groundwater potential, Figure 6. This reveals that some of the western parts of the EB are an active zone for recharging the groundwater table and it can be selected for an arti cial recharge zone. The wells from the central and eastern parts of the basin, on the other hand, show deeper to deepest groundwater tables, respectively, and are in the basin's moderate to very low groundwater potential zones. As a result, the groundwater potential zone can be seen to have a negative relationship with groundwater level.
Last and not least, groundwater recharge in the EB comes primarily from direct rainfall, in ltration through highly permeable sediments, faults, ssures, joints, and fractures in carbonate formations. In addition, surface run-off in rivers and valleys recharges the groundwater table [44].
Furthermore, as shown in Table 10, the lower groundwater potential zone is the deepest part of the groundwater level. Consequently, the Greater Zab River is the primary source of groundwater discharge, as Saether and De Caritat [5] have also con rmed. Besides, it can be observed that the minimum well depth is within the "low" to "very low" groundwater potential zones and vice versa. The resulting groundwater potential map is highly accurate in comparison to groundwater table maps and can be used to plan groundwater exploration and management in the EB. To be concluded, the groundwater potential zone map created in this study is extremely useful and can be used as a guide for concerned decision-makers and the government in planning future arti cial recharge projects to ensure sustainable groundwater utilization for current and future generations in the study area [37].

Validation of GWPZ maps
There are many methods to support the groundwater potential maps, such as receiver performance analysis (ROC), curve area (AUC), groundwater yield of wells performance during eld visits or by available water were plotted on a post-GWRPZ map in ArcGIS. The depth of the groundwater table below the surface ranges between 254.15 and 641.55 meters. The ndings revealed that 9 of the 28 wells were accurately located in potential zones of very high groundwater Figure 6, which were classi ed as having a shallower depth to groundwater level with depths ranging from 254.15 to 320.99 meters, compared to the other wells, which had depths ranging from 518.49 to 641.55 meters. Subsequently, six out of eight wells were identi ed in the high groundwater recharge zones, with depths ranging from 320.99 to 378.72 meters. While ve of six wells belonged to areas with moderate potential for groundwater. However, one well was located in the lower GWRPZ, and two of the last three wells were located in the very low areas, Figures 6 &  7. This indicates that the GWRPZ generated by the F-AHP model was consistent with the ground truth data (well locations). Furthermore, the validation results show that the database with techniques applied to build GWPZ models for potential groundwater zones has shaped accurate results by analyzing the suitability of the signi cant weights of the factors.

Conclusions
The main themes of the present study were the availability of groundwater and associated human perceptions. The delineation of potential groundwater zones was performed by applying "weighted  Membership function for triangular fuzzy numbers adapted after Shao, Huq [12].   The groundwater potential zones map of the study area Groundwater potential zones map of the study area and model validation through the wells.