Application of Analytical Hierarchy Process and Geophysical Method for Groundwater Potential Mapping in the Tata Basin, Morocco

Abstract

Ensuring water availability for agriculture and drinking water supply in semi-arid mountainous regions requires control of factors influencing groundwater availability. In most cases, the population draws its water needs from the alluvial aquifers close to villages that are already limited and influenced by current climatic change. In addition, the establishment of deep wells in the hard rock aquifers depletes the aquifer. Hence, understanding the factors influencing water availability is an urgent requirement. The use of geographic information system (GIS), and remote sensing (RS), together with decision-making methods like analytical hierarchy process (AHP) will be of good aid in this regard. In the Tata basin, located in SE Morocco, ten factors were used to explain the groundwater potentiality map (GWPM). Five categories of potential zones were determined: very low (8.67%), low (17.74%), moderate (46.77%), high (19.95%), and very high (6.87%). The efficiency of the AHP model is validated using the ROC curve (receiver operating characteristics) which revealed a good correlation between the high potential groundwater zones and the spatial distribution of high flow wells. Geophysical prospecting, using electrical resistivity profiles, has made it possible to propose new well sites. It corresponds to conductive resistivity zones that coincide with the intersection of hydrogeological lineaments.

1. Introduction

Global climate change, an omnipresent reality, is manifested in the Morocco Kingdom as rising temperature [1], which has increased the risk of water scarcity in the coming decades. Furthermore, the scenario is aggravated by severely polluted water from agriculture, the main economic activity in this region. Out of the 13 billion m³ of surface water that can be mobilized annually, 8.8 billion m³ are lost through evaporation, discharge into the Atlantic Sea or existing leaks in drinking-water networks. Therefore, to meet the needs stated, the country’s natural water resources must be developed through a coherent, holistic plan, considering the need for conservation, prevention, and protection, both in water quantity and water quality, particularly in areas characterized by strong climatic contrast and increased scarcity of water resources. The development of these regions seems inevitably related to the shortage of water resources.

One of the main basins in this region is the Tata basin (sub-basin of the Drâa Wadi) since its economy depends mainly on agriculture, an activity whose production depends primarily on the availability of water resources [2]. Indeed, this activity is mainly developed at oases where traditional palm groves are irrigated from alluvial groundwater captured by springs, wells, and surface water. The total gross water need for this agricultural activity amounts to 82 million m³ [2]. The current status of water use shows that the annual volume of water used in the Tata basin is 69.3 million m³ [2], distributed between 8.3 million m³/year (12%) from surface water and 61 million m³/year (88%) from groundwater.

Comparing the theoretical water needs (82 million m³/year) with the volume of water that is currently available (69.3 million m³/year) shows a significant deficit for agricultural water demands. The coverage rate is 85%; the 15% deficit in the balance sheet is a matter of concern, as the irrigated areas are constantly increasing. This deficit will probably increase in the future, which will undoubtedly hinder the region’s economic development, an area already ruined by climate change [2].

This paper aims to update the knowledge on the existing water resources in this basin and to establish a groundwater potentiality map by diversifying the water supplies from aquifers other than the alluvial ones whose hydraulic equipment is subject to flood phenomena [3]. An elaboration of the groundwater potential map (GWPM) is useful to predict this resource’s development, conservation, and management strategies [4]. GWPM is generally provided by hydrogeological studies integrating, geophysical, geological, and hydrogeological surveys. It remains expensive, complex, and requires experts [5,6]. In contrast, the integration of geographic information system (GIS) and remote sensing (RS) methods allows for the efficient assessment of water resources, given their low cost and high capacity to analyze a dataset in a short time [7,8]. The delineation of GWPM mainly relies on integrating multiple factors such as topography, geomorphology, geological structures, lithology, and drainage networks [9,10]. These factors have rarely been studied together due to the unavailability of data and integration tools [11]. However, GIS and RS tools have emerged as effective tools for spatial data execution and decision-making [12].

The identification of factors affecting groundwater availability differs among researchers. Khan et al. [10] suggested rainfall, geology, lineaments, slope, drainage, and geomorphology map GWPM in the Central Eastern Desert in Egypt. Zghibi et al. [13] add land use and soil in the Korba aquifer, Tunisia. Naghibi et al. [14] consider elevation, plan curvature, profile curvature, distance from the river, distance from faults, stream power index (SPI), and topographic wetness index (TWI) for delineating potential areas in the Moghan basin, Iran. For the Tata basin, following the availability of data received through satellite image processing, a digital elevation model (DEM), and also through a geological map, ten factors were used: permeability, slope, topographic wetness index (TWI), plan curvature, profile curvature, stream transport index (STI), stream power index (SPI), lineament density, node density, and drainage density.

To integrate and analyze the set of factors, several methods were used: frequency ratio (FR) [14,15], evidential belief function (EBF) [16], Shannon’s entropy (SE) models [14], boosted regression tree (BRT) [17], logistic regression (LR) [18], statistical index (SI) [19] and analytical hierarchy process (AHP) [20,21]. Murmu et al. [22], Abijith et al. [23] and Arefin [24] showed that the latter method coupled with GIS is the most effective model for editing GWPM. The AHP model is a multi-criteria decision-making method based on GIS by assigning weights for different factors based on expert opinion [25,26]. This method has been chosen to be applied in the Tata basin to optimize the various choices for future wells. The performance of AHP was evaluated using the receiver operating characteristics curve (ROC) and confirmed using a geophysical study.

2. Study Area

The Tata basin, with an area of 2567 km², is situated in the southeast of Morocco. It is located between the coordinates of latitudes 29°34′00″ N to 30°14′00″ N and longitudes 8°32′00″ W to 7°46′00″ W. The basin has a mountainous topography where the altitudes vary from 474 to 2518 m (Figure 1). The continental and semi-arid climate prevailing in this area is characterized by low rainfall (<150 mm/year) [3,27].

During a 70-year rainfall monitoring period (1931–2001), it appears that there were alternate humid periods, with a duration of 8 years, followed by more extended dry periods. Monthly (Figure 2a), two periods can be distinguished: a rainy period from September to March (with a maximum of 18mm in October) followed by a dry period from April to August. It is worth noting that during summer, violent rainstorms occurred, as confirmed by average precipitation of around 7 mm (Figure 2b). The average monthly temperatures registered at the Tata station for the 1985–2020 time-period show an alternating pattern of two contrasting periods with a cool winter (11.49 °C) and a warm and dry summer (33.09 °C) (Figure 2b). The average relative moisture values show significant monthly variations, exceeding 40% in summer. They are over 40% in November, December, January, and February, when low temperatures (<16 °C) are registered (Figure 2b). The average monthly flows of Wadi Tata are highly variable due to the high variability of rainfall in the basin. The average annual discharge is 3.2 m³/s.

Geologically, the basin is occupied by Precambrian formations composed of granite, gabbro, and schist at Ighrem, Tagragra, and Agouliz inliers (Figure 3). The Paleozoic cover occupies a large part of the basin, including volcano-sedimentary deposits progressing to carbonate deposits of “Lower limestones” topped with pelites of purplish-red color “Lie-de-vin” followed by carbonate deposits of “Upper limestones” and schists–calcareous series [28,29,30,31,32,33,34]. Sandstones and quartzites of the Ordovician age occupy the southern part of the basin, followed by Devonian shale and limestone [35]. Alluvial deposits fill the valleys and form alluvial aquifers. Structurally, three major fault directions exist in the basin: NNE–SSW, NE–SW, and E–W [29] (Figure 3). NE–SW sills and doleritic dykes related to the opening of the Atlantic Ocean in the Triassic/Lias period, traverse all these terrains [36].

3. Materials and Methods

The search for favorable sites for implementing wells requires the integration of hydrogeological, geological, and geomorphological characteristics within the framework of the AHP model, which is summarized in the flowchart (Figure 4). The methodological approach consists of the following three steps: (1) identification and elaboration of decision factors, (2) classification and standardization of these factors, and (3) weight assignment of the factor and their combination according to the multi-criteria approach.

3.1. Analytical Hierarchy Process (AHP) Model

The analytical hierarchy process (AHP) is a multi-criteria decision-making approach that evaluates quantitative as well as qualitative criteria and alternatives on the same scale of preferences [37]. It integrates both empirical data and the subjective opinion of specialists to reach a robust decision-making process [38].

3.2. Determination of Decision Factors

To make a more accurate prediction of GWPM, the selection of a model and the quality of the conditioning factors are essential [39]. Based on the research conducted by [14,17,40,41], ten influential factors were used: permeability, slope, topographic wetness index (TWI), plan curvature, profile curvature, stream transport index (STI), stream power index (SPI), lineament density, node density, and drainage density. The combination of these leads to the development of the GWPM, which can be valid only after verification of the consistency ratio (CR).

3.3. Development of Decision Factor Maps

3.3.1. Drainage Density (DD)

DD is the ratio of the sum of the lengths of the wadi sections to the basin’s surface. This criterion reduces infiltration at the expense of runoff [42] and, consequently, reduces the probability of having significant groundwater reserves [43,44]. The DD map is classified into five categories (Figure 5a). The high DD areas are located in the Tagmout syncline and downstream of Tata city.

3.3.2. Lineaments Density (LD)

The digital processing of satellite images to identify and extract lineaments is fundamental in geological and structural mapping. Some examples of the processing carried out in Morocco [45,46,47,48] have proved that applying filtering processes (spatial and directional) facilitate the identification of lineaments. For this purpose, Landsat 8 OLI satellite images, acquired in 2021, are used to extract lineaments. In the Tata basin, Sobel-type filters oriented at 0°, 45°, 90°, and 135° are used to maximize the extraction of lineaments in all directions [49,50].

The LD map represents the segments derived from the superposition of information contained in the four filtered images and the ones corresponding to the faults extracted from geological maps. Three major prominent groups of preferred directions: NNE–SSW, NE–SW, and ENE–WSW, are distinguished. The NE–SW trending lineaments are predominant and can facilitate the accumulation of a large volume of water (Figure 5b).

The LD map includes five classes: very low, low, moderate, high, and very high (Figure 5b). It highlights a difference in this map’s arrangement and concentration of lineaments. Indeed, the core of the Tagmout syncline shows a low density of lineaments occupied by younger sediments (Quaternary). On the other hand, the northwest of the Tagmout and Tata areas, and the northeast of Tata city, show a high density of lineaments, which corresponds to a favorable accumulation of groundwater zones.

3.3.3. Slope (SL)

Geomorphology is the main factor that influences the recharge of aquifers. Thus, the slope greatly influences the runoff to infiltration ratio [44,51,52]. The SL map has been divided into five classes: very low, low, moderate, high, and very high. The slopes greater than 16% are considered areas of very low infiltration [53,54] and cover more than half of the total surface of the basin (Figure 5c). In contrast, less than 16% of slopes are poorly represented and located in the central part and downstream of the basin.

3.3.4. Node Density (ND)

Chowdhury et al. [41] consider that high and moderate density areas of lineaments and their nodes correspond to high fracture permeability regions. These structures provide secondary porosity that plays an important role in water accumulation (Figure 5d). ND varies from 0.35 to 2.05 km/km². Areas with high ND are located in the northeastern and northwestern parts of Tata city and southeastern Tagmout village. This offers interesting possibilities for groundwater development.

3.3.5. Topographic Wetness Index (TWI)

The TWI is a classic index used to quantify the effect of topography on hydrological processes (including waterlogging). It highlights relatively flat and naturally wet areas due to their position in the basin [55] (Figure 5e). The TWI is calculated using the following equation [56]:

$$\mathrm{TWI} = \ln (\frac{\mathrm{As}}{\tan \mathsf{\beta }})$$

(1)

where As is flow accumulation and β is slope gradient.

3.3.6. Permeability (PER)

Low permeability, consisting of Precambrian bedrock formations, is located in the northern and southeastern parts of the basin (Figure 5f). In contrast, high permeability areas occur along the alluvial deposit surrounding Tata Wadi, locally in the Tagmout syncline, and the downstream part of Jbel Bani. More than 50% of the surface of the basin is characterized by moderate permeability. Regions of high permeability located outside the alluvial zones are characterized by highly fractured bedrock with large openings favorable for storing a significant quantity of water.

3.3.7. Plan Curvature (PLC)

The plan curvature is perpendicular to the direction of the maximum slope. Therefore, it affects the convergence and divergence of the wadi section flows. A positive value indicates that the surface is convex on the side of the treated cell. A negative value indicates that the surface is concave laterally to this cell. A zero value indicates that the surface is linear [57] (Figure 6g).

3.3.8. Profile Curvature (PRC)

The profile curvature is parallel to the direction of the maximum slope. Therefore, it affects the acceleration of water flows at the basin. A negative value indicates that the surface is convex upward at this cell. A positive profile suggests that the surface is concave upward on this cell. A zero value indicates that the surface is linear [58] (Figure 6h).

3.3.9. Stream Power Index (SPI)

The stream power index describes the potential erosion of the flow at a particular point in the basin. As this basin and the slope gradient increase, the amount of water input from upstream areas and the velocity of water flow also increases, thus increasing the power index and the potential for erosion (Figure 6i). The SPI is calculated using the following equation [56]:

SPI = As · tanβ

(2)

where As is flow accumulation and β is slope gradient.

3.3.10. Sediment Transport Index (STI)

The sediment transport index combines slope and slope length. This parameter is used to measure the sediment transport capacity of overland flow in the universal soil loss equation [59] (Figure 6j). STI is calculated using the following equation [60]:

$$\mathrm{STI} ={(\frac{\mathrm{As}}{22.13})}^{0.6}{(\frac{\sin \mathsf{\beta }}{0.0896})}^{1.3}$$

(3)

where As is flow accumulation and β is slope gradient.

3.4. Classification and Standardization of Factors

The factor classification is an equally delicate phase and must be carefully carried out. The choice of classes considers the variance of the data, and for better interpretation, their number has been reduced to five [61,62,63,64,65,66]. Standardization is necessary for good multi-criteria analysis as the factors are measured on different scales with other units. A standard range, from 1 to 10, is adopted for this purpose [63,67,68]. A score of 10 is assigned to the “very low” or “very high” classes depending on whether they contribute to the excellent performance of the considered indicator. Contrarily, an opposite score is given to the other extreme classes. Following the same logic, intermediate values are assigned to intermediate classes according to a linear distribution.

Table 1. Classification and standardization of groundwater potentiality factors.

Factor (units)	Class	Rating	Weight	Factor (units)	Class	Rating	Weight
Drainage density (m/km2)	1.56–1.85	1	1.85	TWI	5.68–8.78	1	0.55
	1.25–1.56	3			8.78–10.42	3
	0.84–1.25	5			10.42–12.69	5
	0.59–0.84	8			12.69–16.28	8
	0.33–0.59	10			16.28–26.47	10
Node density (m/km2)	0–0.21	1	1.86	STI	1568–3702	1	0.64
	0.21–0.49	3			726–1568	3
	0.49–0.82	5			275–726	5
	0.82–1.15	8			58–275	8
	1.15–2.04	10			0–58	10
Lineament density (m/km2)	0–0.32	1	1.35	SPI	509,811–1,108,331	1	0.53
	0.32–0.64	3			295,555–509,811	3
	0.64–0.97	5			5998–295,555	5
	0.97–1.37	8			160–5998	8
	1.37–2.19	10			0–160	10
Permeability	Low permeability	1	1.27	Plan curvature	0.42–17.87	1	0.49
	Medium permeability	3			−0.55–−0.42	3
	High permeability	8			−13.46–−0.55	5
Slope (%)	16<	1	0.96	Profile curvature	–16.98–−0.69	1	0.49
	12–16	3			–0.69–0.44	3
	8–12	5			−0.44–−15.21	5
	4–8	8
	0–4	10

shows the classification of the factors for creating the potentiality map.

3.5. Weighting of the Deciding Factors

The weighting of the deciding factors was performed using the pairwise comparison method in the analytical hierarchy process (AHP) model developed by [69,70,71] and used by [22,72]. Each factor influencing groundwater was given a score from 1 to 4 based on its relative importance to other factors [73] and where 1 and 4 indicate equal and extreme influences of a factor relative to other factors [73]. This method is more appropriate than the direct weight assignment method because its consistency can be checked by calculating the consistency ratio [74]. Moreover, the direct weight assignment method depends only on the decision-maker’s preference [70]. In contrast to the weighting technique based on the arbitrary choice of weights, AHP is based on mathematical calculations that generate standardized weights whose sum is equal to 1. The pairwise comparison matrix and the weights for the selected factors are presented in

Table 2. Comparison matrix by pair of factors.

Factor	DD	ND	LD	PER	SL	TWI	STI	SPI	PLC	PRC
DD	1	2	1	3	2	3	3	2	3	3
ND	1/2	1	4	1	2	2	4	4	4	4
LD	1	1/4	1	1/2	3	2	3	3	3	3
PER	1/3	1	2	1	2	3	2	2	2	2
SL	1/2	1/2	1/3	1/2	1	4	2	2	2	2
TWI	1/3	1/2	1/2	1/3	1/4	1	1/2	1/2	2	2
STI	1/3	1/4	1/3	1/2	1/2	2	1	1	2	2
SPI	1/2	1/4	1/3	1/2	1/2	2	1	1	1/2	1/2
PLC	1/3	1/4	1/3	1/2	1/2	1/2	1/2	2	1	1
PRC	1/3	1/4	1/3	1/2	1/2	1/2	1/2	2	1	1

Note: DD, drainage density; ND, node density; LD, lineament density; PER, permeability; SL, slope; TWI, topographic wetness index; STI, sediment transport index; SPI, stream power index; PLC, plan curvature; and PRC, profile curvature.

and

Table 3. The weighting of the factors.

Factor	DD	ND	LD	PER	SL	TWI	STI	SPI	PLC	PRC	Weight
DD	0.19	0.32	0.10	0.36	0.16	0.15	0.17	0.10	0.15	0.15	1.85
ND	0.10	0.16	0.39	0.12	0.16	0.10	0.23	0.21	0.20	0.20	1.86
LD	0.19	0.04	0.10	0.06	0.24	0.10	0.17	0.15	0.15	0.15	1.35
PER	0.06	0.16	0.20	0.12	0.16	0.15	0.11	0.10	0.10	0.10	1.27
SL	0.10	0.08	0.03	0.06	0.08	0.20	0.11	0.10	0.10	0.10	0.96
TWI	0.06	0.08	0.05	0.04	0.02	0.05	0.03	0.03	0.10	0.10	0.55
STI	0.06	0.04	0.03	0.06	0.04	0.10	0.06	0.05	0.10	0.10	0.64
SPI	0.10	0.04	0.03	0.06	0.04	0.10	0.06	0.05	0.02	0.02	0.53
PLC	0.06	0.04	0.03	0.06	0.04	0.03	0.03	0.10	0.05	0.05	0.49
PRC	0.06	0.04	0.03	0.06	0.04	0.03	0.03	0.10	0.05	0.05	0.49

.

After defining the matrix, the consistency of the choice made is checked by the consistency ratio (CR), which is calculated by the ratio between the consistency index (CI) and the random index (RI) proposed by [69]. The reasoning is consistent if its consistency ratio is less than or equal to 0.1. Finally, the values of the random index (RI) are given in

Table 4. Random indices function of the number of elements compared [69].

n	1	2	3	4	5	6	7	8	9	10
RI	0	0	0.58	0.9	1.12	1.24	1.32	1.41	1.45	1.49

. These values are given as a function of the number of parameters compared.

$$\mathrm{CR}=\frac{\mathrm{CI}}{\mathrm{RI}}$$

(4)

where CI is the consistency index and RI is a random index.

$$\mathrm{CI}=\frac{{\mathsf{\lambda }}_{\max }-\mathrm{n}}{\mathrm{n}-1}$$

(5)

λ_max is the maximum eigenvalue of the comparison matrix and n is the number of factors [69].

The consistency ratio (CR) of the pairwise comparison matrix is 0.07 with a CI calculated for: λ max = 10.92, n = 10, and RI = 1.49. The CR ratio less than 0.1 indicates that the weighting coefficients obtained can be used for water resources potentiality mapping.

3.6. Determination of the GWPM

The weighted method was used in this study to explain the GWPM [44,61,62,72]. It consists of summing the standardized and weighted values of each criterion involved in the development of a given indicator [21,75], according to the following equation:

$$\mathrm{GWPM}={\displaystyle \sum }_{i=1}^n{w}_i·x_i$$

(6)

where w_i is the weight of the criterion and x_i is the standardized value of criterion i.

GWPM = (drainage density × 1.85) + (lineament density × 1.35) + (node density × 1.86) + (slope × 0.96) + (permeability × 1.27) + (profile curvature × 0.49) + (plan curvature × 0.49) + (TWI × 0.55) + (STI × 0.64) + (SPI × 0.53)

(7)

The elaboration of the GWPM consists of transferring in space the various values resulting from the summation of each criterion’s standardized and weighted values. All combinations required for the thematic map were performed in Raster mode using the Map algebra tool of the Spatial Analyst module of ArcGIS 10.4.

3.7. Validation of the GWPM

The GWPM is validated by applying the receiver operating characteristic (ROC) [40,76], which will be followed by geophysical prospection. Moreover, the wells not used in the model were retained and then superimposed on the GWPM for its validation. The ROC curve is a graphical representation of the true positive rate on the x–axis and the false positive rate on the y–axis [77,78]. The area under the ROC curve (AUC) demonstrates the accuracy of a prediction system by describing the ability of the system to predict the correct occurrence or non-occurrence of predefined “events” [79,80]. The values of the ROC curve, ranging from 0.5 to 1, are used to determine the accuracy of the applied model. They are classified as: excellent (0.9–1), very good (0.8–0.9), good (0.7–0.8), moderate (0.6–0.7), and poor (0.5–0.6) [81].

4. Results and Discussion

4.1. GWPMusing AHP Model

According to the AHP model, the combination of factors has been integrated into the GIS environment, generating the GWPM (Figure 7). The spatial distribution of the groundwater potential map (GWPM) in the Tata basin is mainly controlled by node density, drainage density, and lineament density, with respective weights of 1.86, 1.85, and 1.35. They confirm the results obtained by [61,82] applied in the Arghren basin in Morocco and the basin in Kurdistan Province in Iran. The weights assigned to the permeability and slope factors are less important than ND, DD, and LD. However, areas with high permeability (alluvial deposits) can comprise areas with high potential, provided the slope is low (less than 16%). On the other hand, plane curvature and profile curvature weakly contribute to water infiltration.

Five classes have been determined on the GWPM in terms of occupied surface: very low (8.67%), low (17.74%), moderate (46.77%), high (19.95%), and very high (6.87%). The areas of high to very high potentiality are mainly located in the downstream part of the basin and on the Tagmout plain where alluvial formations with concave surfaces and a slight slope are dominant. Likewise, the northwestern and central parts of the basin show high potential due to a high lineament density and node density that also coincide with moderately permeable rock outcrops. Lineaments provide favorable conditions for groundwater accumulation and, consequently, groundwater reserves. These results are in agreement with several studies [22,61,82].

The low and very low potential areas (26.41%) are limited to sites with a low density of lineaments and nodes, steep slopes, and low-permeability formations. These areas are located in the southwestern and eastern part of the basin, in the northeast of Tagmout village, and on mountain ridges.

The areas with high water potential near Tata city led us to optimize the choice of future well locations by avoiding areas of high flooding for sustainable security of well equipment. Geophysical prospecting using electrical profiles was applied to confirm and validate these delimited potential zones.

4.2. Validation of GWPM

Validation of results is an essential step in predictive modeling. The validation dataset must be independent of the training dataset, and the ability of a model to predict the potential zones from the validation dataset is the ultimate determinant of the quality and accuracy of models [17]. For this reason, the validation of GWPM was performed using data from existing wells in the basin.

ROC curve analysis was performed to validate the AHP model, comparing the existing 64 wells with GWPM [18,67]. The AUC value of the success rate curve is around 0.804, indicating a prediction accuracy of 80.4% (Figure 8). These results corroborate the results of [83,84,85], who have revealed the effectiveness of this model in identifying GWPM.

The generated GWPM has some limitations due to the difficulty of having detailed geospatial data in the study area. The lack of climatological stations, which can provide a real spatial variation in precipitation, limits the integration of this factor in this study. Thus, it seems necessary to equip this basin with a set of homogeneously distributed stations. Bhattacharya et al. [44], Kumar et al. [86], and Al-Abadi et al. [87] faced the same situation as the Tata basin and were able to generate the GWPM without integrating precipitation. On the other hand, the errors related to image classification are mainly due to their very-low resolution [88]. Mohammadzadeh et al. [89] demonstrated that high-resolution satellite imagery enables more efficient data extraction and detection. The digital elevation model (DEM) and Landsat 8OLI satellite image used have a medium resolution of 30 × 30 m. The high resolution of input data can improve generated factor maps.

It is essential to also draw attention to the assignment of weights to factors, as the peer comparison is based on expert opinion. Inadequate judgment on a factor can influence the assigned weight [90,91]. Finally, the validation of the GWPM, based only on wells located near Tata wadi, is insufficient. The absence of wells in the other parts of the basin constitutes an obstacle to GWPM validation, even though the ROC curve gives 80.4% satisfaction.

4.3. Application of Geophysics: Analysis and Interpretation of Results for Area A (NE of Tata City)

Geophysical prospecting was conducted in two areas close to Tata city to showcase the efficacy of AHP-derived GWPM. In these areas, geophysical validation using the geo-electrical profiles methods was conducted. An initial area (A) was detailed to understand the geophysical behavior of the most permeable geological formations. It was chosen as a calibration area since it contains wells with different yields. Subsequently, once the geophysical parameters were defined, the same prospecting was applied to the NE area of Tata city (area B) to determine new potential sites to propose to the water agency. For the first calibration area (A), nine similar NW–SE electrical profiles were executed, separated by 500 m. For each resistivity measurement, the distance between the two transmitting electrodes was kept at 300 m. The measurements were made with a 50-m step.

The apparent resistivity values correspond to the geological formations located between 50 and 60 m depth. A resistivity variation map has been produced, showing a significant variation in this parameter that ranges from 85 to 4438 Ohm.m (Figure 9a). High resistivity is located at the NE and SW extremities of the prospected area. They probably correspond to the Lower Cambrian limestone. A low resistivity anomaly separates them with a NW–SE direction, where all the wells are embedded. These are probably basement formations (schists) covered by alluvial terraces which cover the passage zones of the hydrogeological lineaments (faults) detected through the processing of satellite images. Further, as we move away from the NW–SE lineament (Figure 9a), the well yields become low and the resistivity increases (Figure 9b). The probability of high yield increases with proximity to the hydrogeological lineaments, which are represented by low resistivity.

A high potential zone for water resources located NE of Tata city (area B) (Figure 10a) was also subjected to geophysical prospecting by electrical profiles to optimize the choice of future wells. The apparent resistivity map shows values ranging from 55 to 2000 Ohm.m (Figure 10b).

A decrease in resistivity values compared to the first prospected area (A) was observed due to clay and silty formations interspersed between the limestone banks. The conductive anomalies that may be hydrogeologically interesting are:

• Anomaly 1, which corresponds to the basement formations at a depth affected by faults and hydrogeological lineaments with an alluvial cover above and
• Anomaly 2, which corresponds to NW–SE faults affecting the limestone formations without alluvial cover.

Three points have been proposed for the establishment of future wells in the junction zones of hydrogeological lineaments with low resistivity values. In addition, they are located in areas with a low risk of flooding and can be tested by the local water agency.

5. Conclusions

Elaboration of GWPM in the Tata basin, located in southeastern Morocco, was carried out using GIS mapping tools, remote sensing, an AHP model, and geophysical prospection. According to the results of the applied model, factors influencing water availability are mainly geomorphological since they correspond to node density, drainage density, and lineament density factors. Furthermore, the validation by the ROC curve showed the model’s efficiency despite using low-resolution satellite data.

The geo-electrical prospection has been used to locate future well sites outside flood risk areas. Moreover, the proposed study model provides maximum optimization of time and financial means since it enables, by using accessible data, the precise location of well sites in a short time.