Groundwater Potential Zone Mapping: Integration of Multi-Criteria Decision Analysis (MCDA) and GIS Techniques for the Al-Qalamoun Region in Syria

Abstract

One of the most critical processes for the long-term management of groundwater resources is Groundwater Potential Zonation (GWPZ). Despite their importance, traditional groundwater studies are costly, difficult, complex, and time-consuming. This study aims to investigate GWPZ mapping for the Al-Qalamoun region, in the Western part of Syria. We combined the Multi-Influence Factor (MIF) and Analytic Hierarchy Process (AHP) methods with the Geographic Information Systems (GIS) to estimate the GWPZ. The weight and score factors of eight factors were used to develop the GWPZ including drainage density, lithology, slope, lineament density, geomorphology, land use/land cover, rainfall, and soil. According to the findings, about 46% and 50.6% of the total area of the Al-Qalamoun region was classified as suitable for groundwater recharge by the AHP and MIF methods, respectively. However, 54% and 49.4% of the area was classified as having poor suitability for groundwater recharge by the AHP and MIF methods, respectively. These areas with poor suitability can be utilized for gathering surface water. The validation of the results showed that the AHP and MIF methods have similar accuracy for the GWPZ; however, the accuracy and results depend on influencing factors and their weights assigned by experts.

1. Introduction

Groundwater is the most significant natural water resource, and effective groundwater management depends on the quantity and quality of available groundwater. The existence and volume of groundwater depend on the lithological characteristics and the porosity of geological formations [1]. Groundwater moves to discharge locations including springs, streams, lakes, and the sea [2]. As a result, its supply is restricted, and identifying prospective groundwater zones is a considerable challenge in several parts of the world. Climate change affects the quantity of water needed and the supply availability [3]. Groundwater storage is influenced by several factors, such as geological formations, geomorphological structure, porosity, weathering, lineament density, drainage, land use and land cover (LULC), and rainfall [4].

Several studies have used Multi-Criteria Decision Analysis (MCDA) [5,6,7,8] and machine learning algorithms [9,10] to estimate the Groundwater Potential Zonation (GWPZ). Remote sensing can investigate large-scale observations of the earth’s surface, which makes it a useful tool for GWPZ studies [11]. Furthermore, GIS can effectively manage data in different thematic levels, such as lithology, drainage density, elevation, lineament density, geomorphology, slope, and LULC. All these factors must be considered and integrated accurately when determining the GWPZ [12].

In various regions of the world, several researchers have used MCDA approaches for groundwater studies integrated with remote sensing and GIS techniques [1,4,13,14,15,16,17,18,19,20]. The Multi-Influence Factor (MIF) is a modern MCDA technique for detecting and defining the GWPZ based on specialist opinion [21]. For instance, Bhattacharya et al. [22] used the geospatial approach and MIF technique to allocate the weightage of thematic layers when mapping the GWPZ of the Purulia district, West Bengal. The accuracy of their approach was calculated as 82%. Nag et al. [23] used the same approach to assess the potential groundwater zone in the Khatra Block of the Bankura district, West Bengal. In addition to the MIF method, several studies have used the Analytical Hierarchy Process (AHP), as developed by Saaty [24], to detect and define the GWPZ [25]. The AHP is an MCDM technique for pairwise comparisons of spatial criteria that are assigned weights based on specialist assessment [11,26,27]. It is a common subjective approach that allows users to choose the weight of each criterion when solving problems with many criteria [28,29].

Carefully selecting predictive factors is an important step in MCDA. Several studies have reviewed previous research to select the predictive factors for their models (e.g., [28,30]). In this study, we reviewed 29 recently published, high-quality research papers focusing on GWPZ that were obtained from the Scopus database (Table 1). More than 72.4% of these papers used LULC, drainage density, soil, lithology, slope, lineament density, rainfall, and geomorphology as predictive factors for GWPZ. The remaining factors, which were ignored in this study, were cited in less than 25% of these papers (Table 1).

Several researchers have discovered that combining the AHP and MIF methods with GIS is an efficient and effective GWPZ approach [6,31,32,33,34,35,36,37,38]. Indeed, many scholars have utilized the AHP and MIF approaches to identify the GWPZ by determining the weights of distinct thematic layers and their classes [6,31,32,33,34,35,36,37,38]. By dramatically decreasing the mathematical complexity of decision-making based on methodical expert judgment, the AHP and MIF methodologies have attracted attention as promising tools for groundwater prediction that provide quick, precise, and cost-effective evaluation of groundwater recharge potential [21,39,40].

Nevertheless, other MCDA approaches, such as the certainty factor (CF) and weighted spatial probability modeling (WSPM), are also used in GWPZ studies. For example, Elewa et al. [41] identified the GWPZ in the Sinai Peninsula, Egypt, using Landsat (ETM+) imagery, GIS, watershed modeling system, and WSPM. Yeh et al. [42] used GIS and remote sensing data to find the GWPZs in the Chih-Pen Creek basin in eastern Taiwan.

Table 1. Literature review of the factors used to map potential groundwater zones.

Literature	LULC	Drainage Density	Soil Texture	Lithology	Slope	Lineament Density	Rainfall	Geomorphology	Elevation	NDVI	Groundwater Depth	Distance to River	Aquifer Thickness	Recharge Rate	Pond Density	Sentinel Water Index	Topographic Wetness Index	Soil Depth	Hillshade
[43]	*	*	*	*	*	*		*										*
[37]	*	*	*	*	*	*	*		*	*			*		*
[44]	*	*	*	*	*	*	*
[34]	*	*	*	*	*	*	*	*
[45]	*	*	*	*	*	*	*	*
[38]	*	*	*	*	*	*	*	*
[46]	*	*	*	*	*	*	*	*
[47]	*	*	*	*	*	*	*	*
[48]	*	*	*	*	*	*	*	*
[49]	*	*	*	*	*	*	*	*
[50]	*	*	*	*	*	*	*	*
[51]	*	*	*	*	*	*	*	*
[52]	*	*	*	*	*	*	*	*
[53]	*	*	*	*	*	*	*	*
[54]	*	*	*	*	*	*	*	*
[15]	*	*	*	*	*	*	*	*
[6]	*		*		*			*	*	*	*								*
[35]	*	*	*	*	*		*		*	*	*			*
[55]	*	*	*	*	*	*		*
[56]	*	*	*	*			*	*		*							*
[57]	*	*	*	*	*	*			*	*			*	*		*
[22]	*	*	*	*	*
[58]	*	*	*	*	*	*	*	*				*
[59]	*	*	*		*	*	*	*
[60]		*		*	*	*						*
[61]	*	*	*				*	*	*
[62]	*	*		*		*			*		*
[63]	*	*	*	*	*		*	*	*		*
[64]	*	*	*	*	*	*	*
Average rate%	96.6	96.6	93.1	89.7	89.7	79.3	75.8	72.4	24.1	17.2	13.8	6.9	6.9	6.9	3.5	3.5	3.5	3.5	3.5

Table 1. Literature review of the factors used to map potential groundwater zones.

Literature	LULC	Drainage Density	Soil Texture	Lithology	Slope	Lineament Density	Rainfall	Geomorphology	Elevation	NDVI	Groundwater Depth	Distance to River	Aquifer Thickness	Recharge Rate	Pond Density	Sentinel Water Index	Topographic Wetness Index	Soil Depth	Hillshade
[43]	*	*	*	*	*	*		*										*
[37]	*	*	*	*	*	*	*		*	*			*		*
[44]	*	*	*	*	*	*	*
[34]	*	*	*	*	*	*	*	*
[45]	*	*	*	*	*	*	*	*
[38]	*	*	*	*	*	*	*	*
[46]	*	*	*	*	*	*	*	*
[47]	*	*	*	*	*	*	*	*
[48]	*	*	*	*	*	*	*	*
[49]	*	*	*	*	*	*	*	*
[50]	*	*	*	*	*	*	*	*
[51]	*	*	*	*	*	*	*	*
[52]	*	*	*	*	*	*	*	*
[53]	*	*	*	*	*	*	*	*
[54]	*	*	*	*	*	*	*	*
[15]	*	*	*	*	*	*	*	*
[6]	*		*		*			*	*	*	*								*
[35]	*	*	*	*	*		*		*	*	*			*
[55]	*	*	*	*	*	*		*
[56]	*	*	*	*			*	*		*							*
[57]	*	*	*	*	*	*			*	*			*	*		*
[22]	*	*	*	*	*
[58]	*	*	*	*	*	*	*	*				*
[59]	*	*	*		*	*	*	*
[60]		*		*	*	*						*
[61]	*	*	*				*	*	*
[62]	*	*		*		*			*		*
[63]	*	*	*	*	*		*	*	*		*
[64]	*	*	*	*	*	*	*
Average rate%	96.6	96.6	93.1	89.7	89.7	79.3	75.8	72.4	24.1	17.2	13.8	6.9	6.9	6.9	3.5	3.5	3.5	3.5	3.5

The present study was designed to generate a GWPZ map for the Al-Qalamoun region in Syria using the AHP and MIF methods. Although this area suffers from droughts and water scarcity, the Al-Qalamoun region has never been studied before. Therefore, we integrated remote sensing and GIS data to produce high-accuracy GWPZ results. Eight predictive factors were used including drainage density, lithology, slope gradient, lineament density, geomorphology, LULC, rainfall, and soil.

2. Methodology

2.1. Study Area

The study area, Al-Qalamoun, is in the western part of Syria. It covers 1149 km² of the Al-Qalamoun Mountain and Eastern Lebanon Mountain series between 36°25′E–37°0′ E and 34°0′ N–34°15′ N (Figure 1). The temperatures in Al-Qalamoun range between moderate in the summer and cold in the winter. The coldest temperatures range from about 1 °C to 15 °C, whereas the highest temperatures range from about 22 °C to 41 °C. These temperatures are typical for areas located between an altitude of 846 and 2598 m above sea level. The study area represents a desert region with an average annual rainfall ranging between 111 and 430 mm. The groundwater level in the study area ranges from 30 to 530 m [65].

The lithology of the study area is mainly chalky and nummulitic limestone followed by calcareous sandstones, dolomites, and quaternary sediments (i.e., conglomerates and sandstones) [66]. The soil types are mainly aridisols and entiosols [67]. Agriculture, which is considered the main economic activity in the area, uses both surface water and groundwater. Therefore, water resource management is a considerable issue that must be addressed in the Al-Qalamoun region.

2.2. Factors Used for Modeling

The MIF and AHP methods, GIS, and remote sensing techniques were integrated to map the GWPZ. We used the following factors for modeling: lithology, lineament density, LULC, drainage density, slope, geomorphology, rainfall, and soil. The lithology map of the study area was prepared using a hardcopy of the geological map obtained from the Department of Geological Survey and Mineral Research of Syria (1: 200,000 scale) [68]. The soil map was prepared using the soil map of the Arab Center for the Studies of Arid zones and Dry Lands (ACSAD) (1: 1,000,000 scale) [69]. The geomorphology map of the study area was created by digitizing the geomorphologic map of Syria published by the Department of Geological Survey and Mineral Research of Syria (1:1,000,000 scale) [70]. The three maps (lithology, soil, and geomorphology) were scanned with 400 dpi and digitized manually in ArcMap. The Digital Elevation Model (DEM) of the Shuttle Radar Topographic Mission (SRTM), including 30 m spatial resolution data, was obtained from the EarthExplorer website [71] and used to extract the drainage pattern and the slope gradient and prepare the drainage density of the study area. The faults were not well identified in the large-scale geological map (1: 200,000 scale) of the study area. Therefore, we extracted the lineaments using the DEM and the Landsat 8 satellite imagery acquired from the EarthExplorer website [71] on 24 June 2021 (Path: 174 and Row: 036) with 30 m spatial resolution. The Landsat 8 OLI satellite imagery was also used to prepare the LCLU map. The LCLU map was verified with fieldwork, for the accessible areas, and Google Earth, for the inaccessible areas. Meteorological station data were not available for the study area. Therefore, we used CHIRPS rainfall data, which is used in several types of research [72,73,74,75,76,77]. The CHIRPS data has a spatial resolution of 0.5 degrees and a temporal resolution of the daily, monthly, pentad, decadal, annual, and temporal domains (1981—present) [76]. The rainfall data for 18 points distributed over the entire study area were collected from the Food and Agriculture Organization (FAO) of the United Nation online platform [78] (https://wapor.apps.fao.org) for the years 2009 to 2019. We interpolated these points with a spatial resolution of 30 m to create a map using the Inverse Distance Weight (IDW) technique in ArcGIS. To validate the observed precipitation data, we compared the CHIRPS rainfall dataset with data obtained from the Al-Nabek and Qara meteorological stations. Forty-eight monthly precipitation measurements, covering the period from October 2009 to September 2013, were compared with the CHIRPS data (Figure 2). The result showed a strong correlation, where the coefficients of determinations (R²) were >0.97 and the p-values were <0.05.

2.3. Statistical Models

We used the MIF and AHP methods to analyze the model factors and derive their rating score. The GWPZ was then estimated using the factor weightage and rank. The final GWPZ was created using overlay analysis.

This study used eight main factors (i.e., lithology, lineament density, LULC, drainage density, slope, geomorphology, rainfall, and soil) to identify the GWPZ. We tested 10 models: five models for the MIF method and five models for the AHP method. In each model, we used different weights for each factor (Appendix A:

Table A1. Weight and scores of specific characteristics in (MIF1 and AHP1) are assigned to factors that influence GWPZ.

Factors	Sub-Classes	MIF1		AHP1
		Weight	Score	Weight	Rank
Drainage Density	Very Low	20	20	35	9
	Low		16		7
	Medium		12		5
	High		8		3
	Very High		4		1
Slope	80–87	18	2	19	1
	60–80		6		3
	40–60		10		5
	20–40		14		7
	0–20		18		9
Lithology	Quaternary sands, loams	15	15	16	9
	Quaternary conglomerates, sandstones, loams		12		7
	Cretaceous limestone, marl dolomites		9		5
	Neogene limestone, conglomerates, sands		6		3
	Paleogene Chalky limestone, marls		3		1
Geomorphology	Flood plain	13	13	10	9
	Upper quaternary and recent alluvial fans		10		7
	Low mountains with small and low ridges		7		5
	Desert weathering outliers		4		3
	Low mountains with coniform and cuesta—hilly relief		1		1
	Medium-height mountains with flattened divides and steep abrupt slopes		1		1
Rainfall (mm)	270–430	11	3	10	9
	197–270		5		7
	163–197		7		5
	139–163		9		3
	111–139		11		1
Soil	Entisols-Lithic Torriorthents, Coarse and medium- Orthids, level to Steep.	9	9		9
	Low Entisols-Lithic Torriorthents, Coarse and medium-Rock outcrop, steep.		7	5	7
	Aridisols-Typic Camborthids, medium- Typic Calciorthids, Level.		5		5
	Aridisols-Typic Paleorthids, Coarse and medium-level sloping and steep		3		3
	Aridisols-Typic Calciorthids, Coarse- Paleorthids, Sloping.		1		1
LULC	Built-Up Land	7	3	3	1
	Bare Mountain		4		3
	Barren Land		5		5
	Pasture Land		6		7
	Agriculture Land		7		9
Lineament Density	Very High High	7	7 6	2	9 7
	Medium		5		5
	Low		4		3
	Very Low		3		1

,

Table A2. Weight and scores of specific characteristics in (MIF2 and AHP2) are assigned to factors that influence GWPZ.

Factors	Sub-Classes	MIF2		AHP2
		Weight	Score	Weight	Rank
Lithology	Quaternary sands, loams	20	20	31	9
	Quaternary conglomerates, sandstones, loams		16		7
	Cretaceous limestone, marl dolomites		12		5
	Neogene limestone, conglomerates, sands		8		3
	Paleogene Chalky limestone, marls		4		1
Slope	80–87	18	2	21	1
	60–80		6		3
	40–60		10		5
	20–40		14		7
	0–20		18		9
Drainage Density	Very Low	15	15	16	9
	Low		12		7
	Medium		9		5
	High		6		3
	Very High		3		1
Geomorphology	Flood plain	13	13	11	9
	Upper quaternary and recent alluvial fans		10		7
	Low mountains with small and low ridges		7		5
	Desert weathering outliers		4		3
	Low mountains with coniform and cuesta- hilly relief		1		1
	Medium-height mountains with flattened divides and steep abrupt slopes		1		1
LULC	Built-Up Land	11	3	9	1
	Bare Mountain		5		3
	Barren Land		7		5
	Pasture Land		9		7
	Agriculture Land		11		9
Lineament Density	Very Low	9	1		1
	Low		3	5	3
	Medium		5		5
	High		7		7
	Very High		9		9
Rainfall (mm)	270–430	7	7	4	9
	197–270		6		7
	163–197		5		5
	139–163		4		3
	111–139		3		1
Soil	Entisols-Lithic Torriorthents, Coarse and medium-Orthids, level to Steep.	7	7	3	9
	Entisols-Lithic Torriorthents, Coarse and medium-Rock outcrop, steep.		6		7
	Aridisols-Typic Camborthids, medium- Typic Calciorthids, Level.		5		5
	Aridisols-Typic Paleorthids, Coarse and medium-level sloping and steep.		4		3
	Aridisols-Typic Calciorthids, Coarse- Paleorthids, Sloping.		3

,

Table A3. Weight and scores of specific characteristics in (MIF3 and AHP3) are assigned to factors that influence GWPZ.

Factors	Sub-Classes	MIF3		AHP3
		Weight	Score	Weight	Rank
Lithology	Quaternary sands, loams	20	20	31	9
	Quaternary conglomerates, sandstones, loams		16		7
	Cretaceous limestone, marl dolomites		12		5
	Neogene limestone, conglomerates, sands		8		3
	Paleogene Chalky limestone, marls		4		1
Slope	80–87	18	2	22	1
	60–80		6		3
	40–60		10		5
	20–40		14		7
	0–20		18		9
Geomorphology	Flood plain	15	15	16	9
	Upper quaternary and recent alluvial fans		12		7
	Low mountains with small and low ridges		9		5
	Desert weathering outliers		6		3
	Low mountains with coniform and cuesta- hilly relief Medium-height mountains with flattened divides and steep abrupt slopes		3 1		1 1
Drainage Density	Very Low	13	13	11	9
	Low		10		7
	Medium		7		5
	High		4		3
	Very High		1		1
LULC	Built-Up Land	11	3	9	1
	Bare Mountain		5		3
	Barren Land		7		5
	Pasture Land		9		7
	Agriculture Land		11		9
Lineament Density	Very Low	9	1		1
	Low		3	5	3
	Medium		5		5
	High		7		7
	Very High		9		9
Rainfall (mm)	270–430	7	7	4	9
	197–270		6		7
	163–197		5		5
	139–163		4		3
	111–139		3		1
Soil	Entisols-Lithic Torriorthents, Coarse and medium-Orthids, level to Steep.	7	7	2	9
	Entisols-Lithic Torriorthents, Coarse and medium-Rock outcrop, steep.		6		7
	Aridisols-Typic Camborthids, medium-Typic Calciorthids, Level.		5		5
	Aridisols-Typic Paleorthids, Coarse and medium-level sloping and steep.		4		3
	Aridisols-Typic Calciorthids, Coarse-Paleorthids, Sloping.		3		1

,

Table A4. Weight and scores of specific characteristics in (MIF4 and AHP4) are assigned to factors that influence GWPZ.

Factors	Sub-classes	MIF4		AHP4
		Weight	Score	Weight	Rank
Lithology	Quaternary sands, loams	20	20	32	9
	Quaternary conglomerates, sandstones, loams		16		7
	Cretaceous limestone, marl dolomites		12		5
	Neogene limestone, conglomerates, sands		8		3
	Paleogene Chalky limestone, marls		4		1
Geomorphology	Flood plain	18	18	22	9
	Upper quaternary and recent alluvial fans		14		7
	Low mountains with small and low ridges		10		5
	Desert weathering outliers		6		3
	Low mountains with coniform and cuesta-hilly relief Medium-height mountains with flattened divides and steep abrupt slopes		2 1		1 1
Slope	80–87	15	3	15	1
	60–80		6		3
	40–60		9		5
	20–40		12		7
	0–20		15		9
LULC	Built-Up Land	13	1	11	1
	Bare Mountain		4		3
	Barren Land		7		5
	Pasture Land		10		7
	Agriculture Land		13		9
Drainage Density	Very Low	11	11	9	9
	Low		9		7
	Medium		7		5
	High		5		3
	Very High		3		1
Lineament Density	Very Low	9	1		1
	Low		3	5	3
	Medium		5		5
	High		7		7
	Very High		9		9
Rainfall (mm)	270–430	7	7	4	9
	197–270		6		7
	163–197		5		5
	139–163		4		3
	111–139		3		1
Soil	Entisols-Lithic Torriorthents, Coarse and medium- Orthids, level to Steep.	7	7	2	9
	Entisols-Lithic Torriorthents, Coarse and medium- Rock outcrop, steep.		6		7
	Aridisols-Typic Camborthids, medium- Typic Calciorthids, Level.		5		5
	Aridisols-Typic Paleorthids, Coarse and medium-level sloping and steep.		4		3
	Aridisols-Typic Calciorthids, Coarse- Paleorthids, Sloping.		3		1

and

Table A5. Weight and scores of specific characteristics in (MIF5 and AHP5) are assigned to factors that influence GWPZ.

Factors	Sub-Classes	MIF5		AHP5
		Weight	Score	Weight	Rank
Lithology	Quaternary sands, loams	20	20	32	9
	Quaternary conglomerates, sandstones, loams		16		7
	Cretaceous limestone, marl dolomites		12		5
	Neogene limestone, conglomerates, sands		8		3
	Paleogene Chalky limestone, marls		4		1
LULC	Built-Up Land	18	2	22	1
	Bare Mountain		6		3
	Barren Land		10		5
	Pasture Land		14		7
	Agriculture Land		18		9
Rainfall (mm)	270–430	15	15	15	9
	197–270		12		7
	163–197		9		5
	139–163		6		3
	111–139		3		1
Soil	Entisols-Lithic Torriorthents, Coarse and medium-Orthids, level to Steep.	13	13	11	9
	Entisols-Lithic Torriorthents, Coarse and medium-Rock outcrop, steep.		10		7
	Aridisols-Typic Camborthids, medium-Typic Calciorthids, Level		7		5
	Aridisols-Typic Paleorthids, Coarse and medium-level sloping and steep.		4		3
	Aridisols-Typic Calciorthids, Coarse-Paleorthids, Sloping.		1		1
Geomorphology	Flood plain	11	11	9	9
	Upper quaternary and recent alluvial fans		9		7
	Low mountains with small and low ridges		7		5
	Desert weathering outliers		5		3
	Low mountains with coniform and cuesta-hilly relief Medium-height mountains with flattened divides and steep abrupt slopes		3 1		1 1
Slope	80–87	9	1		1
	60–80		3	5	3
	40–60		5		5
	20–40		7		7
	0–20		9		9
Drainage Density	Very Low	7	7	4	9
	Low		6		7
	Medium		5		5
	High		4		3
	Very High		3		1
Lineament Density	Very Low Low	7	3 4	2	1 3
	Medium High		5 6		5 7
	Very High		7		9

). The significance score of the utilized factors was also determined using the MIF and AHP methods. We used both the MIF and AHP methods five times with different impacts and weights assigned to each factor. As a result, five maps were obtained for each method (Figure 3).

The results were validated using the groundwater level data, and the GWPZ was identified by combining all the thematic layers using the weighted overlay analysis method in ArcGIS 10.8. The GWPZ map was then classified into five classes using the natural breaks (Jenks) classification method. We named the classes very high, high, moderate, low, and very low (Figure 4).

2.3.1. Multi-Influence Factor (MIF) Techniques and Groundwater Potential Zone Method

Weights were assigned to each factor based on their relevance using the MIF approach. The primary and secondary interactions between the variables that influence the GWPZ were used to generate the rankings [21]. The MIF method is very effective and exact for estimating the weights of influential parameters [43].

Table 2. MIF weight scores for groundwater potential zone mapping.

Factors	Major Effect (j)	Minor Effect (n)	Proposed Relative Rate (j + n)	Proposed Score
Lithology	1 + 1 + 1 + 1	0.5	4.5	20
Slope	1 + 1 + 1	0.5 + 0.5	4	18
Drainage Density	1 + 1 + 1	0.5	3.5	15
Geomorphology	1 + 1	0.5 + 0.5	3	13
LULC	1 + 1	0.5	2.5	11
Lineament Density	1 + 1	0.0	2	9
Rainfall	1	0.5	1.5	7
Soil	1	0.5	1.5	7
Total			$\sum \left(j+n\right)$ = 22.5	100

lists the weight scores, where a 1.0 weight score is assigned to each main factor and a 0.5 weight score is assigned to each minor factor [21,22,27]. After assigning weights, the proposed relative rates for groundwater potentiality were computed based on the minor and major consequences of each factor. In this analysis, the significance of each factor was estimated based on published literature and the author’s knowledge of the hydrogeological conditions in the study area. Finally, as illustrated in equation 1 [6,11,35,36,37,44,64], the relative score was utilized to derive the suggested score of individual factors:

$$S_i=\frac{j+n}{\sum \left(j+n\right)}\times 100$$

(1)

where S_i is the proposed score of a factor and j and n are the major and minor effect factors, respectively.

After calculating the score for each factor, we allocated the ranks (R_i) of each sub-class of each factor. The first sub-class had the most important influence and received the same rank as the factor score (R_i1 = S_i) [35]. The rank of the second sub-class (R_i2) was calculated by dividing S_i by the total number of subclasses (n) and subtracting the resultant value (V_i) from R_i1 (i.e., second sub-class (R_i2) Equation (2)) [35,37,79]. The rank of the third sub-class (R_i3) was calculated by subtracting V_i from R_i2. The ranking process was repeated for all successive sub-classes (Table 2). For example, if the weight of the factor is 20, the first sub-class of the of the factor is also 20, and the number of the subclasses (n) of a given factor are 5 the results will be 16 (i.e., 20–(20/5) = 16). Equation (2) was defined as:

$$R_{i}2=R_{i}1-\left(\frac{S_i}{n}\right)$$

(2)

where S_i is the factor score, R_i is the rank of a sub-class of the factor, R_i1 is the rank of the first sub-class, R_i2 is the rank of the second sub-class, and n is the number of sub-classes of the given factor.

Finally, the GWPZ map was created by calculating the raster using Equation (3) [21,35,44]:

$$GWPZ={\displaystyle \sum }_{i=1}^n\left(S_i\times R_i\right)$$

(3)

where GWPZ is the groundwater potential zone, S_i is the score of each factor, and R_i is the rank of each sub-class of a given factor, mentioned above.

2.3.2. Analytical Hierarchy Process (AHP) Techniques and Groundwater Potential Zone Method

Saaty introduced the AHP approach in a series of articles [24,80,81]. The AHP approach works by constructing a set of pairwise comparison matrices that are used to compare all the important elements. This pairwise assessment of the relevance of distinct criteria and sub-criteria inside the judgment matrix converts the MCDA issue into a hierarchy [82,83]. The AHP approach compares the weight and relevance of each factor to the other factors and yields an overall weight for each relevant factor [36,84]. The hierarchy enables the identification of the GWPZ from competing sets of factors by considering each of the numerous features independently. In this work, we identified the GWPZs in the Al-Qalamoun study area by applying the AHP method to eight thematic layers that influence the occurrence of groundwater, including lithology, lineament density, drainage density, LULC, slope, soil, rainfall, and geomorphology. For the pairwise comparisons, each factor was given a score between 1 and 9 based on its relevance relative to other factors (

Table 3. Pairwise comparison matrix for all factors.

Factors	LI	SLP	DD	GM	LULC	LD	RN	SL
Lithology (LI)	1	2	3	3	5	5	7	9
Slope (SLP)	1/2	1	2	3	3	5	5	7
Drainage (DD	1/3	1/2	1	3	3	3	5	5
Geomorphology (GM)	1/3	1/3	1/3	1	2	3	4	5
Land use and Land cover (LULC)	1/5	1/3	1/3	1/2	1	3	5	5
Lineaments (LD)	1/5	1/5	1/3	1/3	1/3	1	2	3
Rainfall (RN)	1/7	1/5	1/5	1/4	1/5	1/2	1	3
Soil (SL)	1/9	1/7	1/5	1/5	1/5	1/3	1/3	1
SUM	2.82	4.71	7.40	11.28	14.73	20.83	29.33	38

) using a conventional Saaty’s 1–9 scale [24] (

Table 4. Conventional Saaty’s scale used in the AHP method [24].

Scale for Importance	Scale
Equally important (EI)	1
Weakly more important (WMI)	3
Strongly more important (SMI)	5
Very strongly more important (VSMI)	7
Absolutely more important (AMI)	9
Intermediate scale	2,4,6,8

).

The relative weight of each criterion was calculated by normalizing the eigenvectors of each matrix member as shown in

Table 5. Identifying the standardized weights for influencing factors in GWPZ.

Factor	LI	SLP	DD	GM	LULC	LD	RN	SL	Weight	Weight %
LI	0.35	0.43	0.40	0.27	0.34	0.24	0.24	0.24	0.31	31
SLP	0.18	0.21	0.27	0.26	0.20	0.24	0.17	0.18	0.21	21
DD	0.12	0.11	0.13	0.27	0.20	0.15	0.17	0.13	0.16	16
GM	0.12	0.07	0.05	0.09	0.14	0.14	0.14	0.13	0.11	11
LULC	0.07	0.07	0.04	0.04	0.07	0.14	0.17	0.13	0.09	9
LD	0.07	0.04	0.05	0.03	0.02	0.05	0.07	0.08	0.05	5
RN	0.05	0.04	0.03	0.02	0.02	0.02	0.03	0.08	0.04	4
SL	0.04	0.03	0.03	0.02	0.01	0.02	0.01	0.03	0.03	3
SUM	1	1	1	1	1	1	1	1	1	1
n	1	2	3	4	5	6	7	8	9	10
RI	0	0	0.58	0.90	1.12	1.24	1.32	1.41	1.49	1.51

λ_max = 8.517, n = 8, CI =0.0739, RI = 1.41, and CR = 0.0524 < 0.1.

. The Consistency Index (CI) and Consistency Ratio (CR) were used to assess the consistency of the matrix. The CI and CR were calculated using Equations (4) and (5):

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

(4)

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

(5)

where CI is the consistency index, λ_max is the greatest Eigenvalue of a matrix, n is the number of factors, CR is the consistency ratio, and RI is the Random Index value. RI was computed by Saaty based on the number of factors [80] (Table 5). The consistency of the matrix can be accepted if the CR is less than 0.1 [85].

Each sub-class of a thematic map was given a rank of 1–5 based on its effect on the occurrence of groundwater [86,87,88,89]. The rankings indicated the following effects: 1 = very low, 2 = low, 3 = moderate, 4 = high, and 5 = very high. Every thematic layer was given a weight, and every sub-class of each factor was given a rank (

Table 6. Weight and scores of specific characteristics that were assigned to factors that influence GWPZ.

Factors	Sub-Classes	MIF		AHP
		Weight	Score	Weight	Rank
Lithology	Quaternary sands, loams	20	20	31	9
	Quaternary conglomerates, sandstones, loams		16		7
	Cretaceous limestone, marl dolomites		12		5
	Neogene limestone, conglomerates, sands		8		3
	Paleogene Chalky limestone, marls		4		1
Slope	80–87	18	2	21	1
	60–80		6		3
	40–60		10		5
	20–40		14		7
	0–20		18		9
Drainage Density	Very Low	15	15	16	9
	Low		12		7
	Medium		9		5
	High		6		3
	Very High		3		1
Geomorphology	Flood plain	13	13	11	9
	Upper quaternary and recent alluvial fans		10		7
	Low mountains with small and low ridges		7		5
	Desert weathering outliers		4		3
	Low mountains with coniform and cuesta-hilly relief		1		1
	Medium-height mountains with flattened divides and steep abrupt slopes		1		1
LULC	Built-Up Land	11	3	9	1
	Bare Mountain		5		3
	Barren Land		7		5
	Pasture Land		9		7
	Agriculture Land		11		9
Lineament Density	Very Low	9	1		1
	Low		3	5	3
	Medium		5		5
	High		7		7
	Very High		9		9
Rainfall (mm)	270–430	7	7	4	9
	197–270		6		7
	163–197		5		5
	139–163		4		3
	111–139		3		1
Soil	Entisols-Lithic Torriorthents, Coarse and medium—Orthids, level to Steep.	7	7	3	9
	Entisols-Lithic Torriorthents, Coarse and medium—Rock outcrop, steep.		6		7
	Aridisols-Typic Camborthids, medium—Typic Calciorthids, Level.		5		5
	Aridisols-Typic Paleorthids, Coarse and medium-level sloping and steep.		4		3
	Aridisols-Typic Calciorthids, Coarse—Paleorthids, Sloping.		3

). Finally, the GWPZ map was created using Equation (6) [47,51,58,90], in the ArcGIS 10.8 environment:

$$\mathrm{GWPZ}=L{I}_{w}L{I}_r+L{D}_{w}L{D}_r+S{L}_{w}S{L}_r+R{N}_{w}R{N}_r+LU/L{C}_{w}LU/L{C}_r+D{D}_{w}D{D}_r+SL{P}_{w}SL{P}_r+G{M}_{w}G{M}_r$$

(6)

where GWPZ is the Groundwater Potential Zonation, LI is the lithology, LD is the lineament density, SL is the soil, RN is the rainfall, LULC is the land use/land cover, DD is the drainage density, SLP is the slope, and GM is the geomorphology. In addition, the “w” and “r” are the weight and rank of a given factor, respectively.

2.4. Validation

To validate the GWPZ maps, we used the area under the curve (AUC) of the Receiver Operating Characteristics (ROC) method. This method has been widely applied by several researchers [38,43,44,91,92,93]. In this study, we compared the suitability of the MIF and AHP methods for creating GWPZ maps using the area under the curve (AUC) of the Receiver Operating Characteristics (ROC) [44,94]. The ROC plots show the relationship between the cumulative areas under different groundwater zones and the cumulative number of wells available in each potential region [43]. Data from a total of 22 wells were used to evaluate the accuracy of the GWPZ maps produced by the MIF and AHP methods. Most of the wells produced groundwater at an acceptable rate ranging between 35–55 m³/h. We used the ROC to select which method is the best for GWPZ mapping [6,95].

3. Results and Discussion

3.1. Evaluation of Predictive Factors

The GWPZs in the study area were estimated using eight factors: lithology, drainage density, slope, lineament density, LULC, geomorphology, rainfall, and soil. Figure 5c shows the lithological units of the study area, which are useful for determining the hydrogeological properties of rocks. The lithology includes Cretaceous limestone, marl dolomites (31%); Neogene limestone, conglomerates (3%), sands; and Paleogene Chalky limestone, marls (22%). In total, 44% of the area was covered by Quaternary conglomerates, sandstones, and loams (where Quaternary sands, loams accounted for 43% and Quaternary conglomerates, sandstones, loams accounted for 1%). The Quaternary conglomerates, sandstones, and loams are the most important aquifer in the basin. The MIF score of the sub-classes ranged from 4 to 20, whereas the AHP score of the sub-classes ranged from 31 to 279. The weightage of the lithology factor was 20 in the MIF method and 31 in the AHP method. A higher importance was given to Quaternary conglomerates, sandstones, and loams based on field investigation and their aquifer system. The rating and weightage of the lithology factor are listed in Table 6.

Low groundwater recharge occurs in media with a high drainage density, and high groundwater recharge occurs in media with a low drainage density [11,14,42]. Therefore, drainage density is one of the most important indicators of hydrogeological characteristics [34]. Permeability is inversely proportional to the drainage density [44,96]. The drainage density of the study area is classified into five classes: very low (10%), low (20%), medium (26%), high (27%), and very high (17%) (Figure 5b). The very low drainage density has a high infiltration rate. Thus, the high score was assigned to very low drainage density. The overall score for the drainage density factor ranged from 3 to 15 and 16 to 144 when calculated using the MIF and AHP methods, respectively. Table 6 shows the MIF and AHP weight and score of the drainage density factor.

The slope of an area is among the factors that regulate water permeation into the subsurface. Surface water infiltration does not occur in the same spot everywhere. In smooth slope areas, surface water runoff is weak and infiltration is high. In high-slope areas, surface water runoff is strong and infiltration is low [11,14]. The slope of the study area ranged from 0° to 87° (Figure 5a). Areas with the lowest slope, ranging from 0–20° (13.5%), were given the highest weight due to low runoff and high infiltration. The overall score of the slope factor is listed in Table 6.

The geomorphology features of the study area were floodplain (5%), Upper Quaternary and recent alluvial fans (52%), low mountains with small and low ridges (11%), desert weathering outliers (4%), low mountains with conform and cuesta- hilly relief, and medium- height mountains with flattened divides and steep abrupt slopes (28%) (Figure 5d). For GWPZ, the highest importance was given to the floodplain region due to the high amount of infiltration. All scores for the geomorphology factor ranged from 1 to 13 and 11 to 99 when calculated using the MIF and AHP methods, respectively (Table 6).

Rainfall is the primary source of recharge for aquifer units [38,44,97]. As a result, the possibility of GWPZs grows as rainfall distribution changes. The rainfall in the study area ranged from 111 to 430 mm (Figure 6a). The highest rainfall areas had a higher amount of GWPZ. The areas with 270 to 430 mm (19%) of rainfall areas were assigned high weightage. The overall score of the rainfall factor ranged from 7 to 3 and 4 to 36 when calculated using the MIF and AHP method, respectively (Table 6).

The rate of infiltration is determined by the porosity of the soil type [98], which is controlled by the amount of groundwater recharge. The soil types of the study area are listed in Table 6. Based on the infiltration rate of each soil, the highest overall score was assigned to type Entisols-Lithic Torriorthents, Coarse and medium- Orthids, level to Steep based. Figure 6d shows a soil map of the study area.

The LULC shows the surface of the earth. The LULC of the study area consists of built-up land (1%), bare mountains (30%), barren land (12%), pastureland (54%), and agricultural land (3%; Figure 6c). According to [22,35,38,40,99,100,101], agricultural land decreases the speed of surface water runoff, which raises water infiltration. Therefore, priority has been given to determining the groundwater potential zone of agricultural land. The overall weightage of LULC was 11 and 9 when calculated using the MIF and AHP methods, respectively. Table 6 shows the score and weightage values for each of the LULC sub-classes as calculated using the MIF and AHP methods.

Lineaments are a type of subterranean geological feature (fractures or structures) that can be discovered through remote sensing [102,103]. Groundwater yields in regions where lineaments parallel to drainage networks intersect can be higher than in other areas. As a result, lineaments provide information about groundwater transport and storage, as well as aid in the identification of groundwater zones in hydrogeological studies [104]. The lineament density in the study area is classified into five classes ranging from extremely low (48%) to very high (14%; Figure 6b). As shown in Table 6, the overall score of the lineament factor ranged from 1 to 9 and 5 to 45 when calculated using the MIF and AHP methods, respectively. The areas with high lineament density were given high importance for GWPZ because of their high infiltration.

3.2. Groundwater Potential Zonation

Many researchers have discovered that combining the AHP and MIF methods with GIS is an efficient and effective GWPZ approach. The AHP and MIF methods have been used to identify the GWPZ by determining the weights of distinct thematic layers and their classes [8,28,29,30,31,32,33,34,35]. By dramatically decreasing the mathematical complexity of decision-making based on methodical expert judgment, the AHP and MIF methods have attracted attention as promising tools for quick, precise, and cost-effective evaluation of groundwater recharge potential [21,39,40].

The GWPZ map of the study area was created utilizing GIS-based MIF, AHP, and overlay analyses of the factors that were identified as important groundwater predictors in our literature review (Appendix A: Figure A1, Figure A2, Figure A3, Figure A4 and Figure A5). To begin, the MIF and AHP methods were utilized to calculate the weight values of the factors and the score values of each sub-class. The score and weightage of each factor was multiplied and attributed to the respective raster file of the factors.

The AHP method classified the GWPZ of the study area as follows: very high, 182 km² (16%); high, 253 km² (22%); moderate, 178 km² (16%); low, 302 km² (26%); and very low, 229 km² (20%). In contrast, the MIF method classified the GWPZ of the study area as follows: very high, 180 km² (16%); high, 265 km² (23.2%); moderate, 260 km² (22.8%); low, 243 km² (21%); and very low 194 km² (17%). The Qara, Alhafar, and Alsehel regions were mainly in very high and high GWPZ, whereas the Al-Nabek area was in moderate potential areas. Other parts of the study area were covered by low and very low potential zonation. Figure 7 shows the GWPZ maps of the study area created by using the MIF and AHP methods.

Several researchers have found that the GWPZ map produced using the AHP approach is more efficient than that produced using the MIF technique [6,35,38,56]. However, others have found that using the MIF technique is more efficient than using the AHP method [36,37,44,57]. In this study, we found that the quality of a GWPZ map produced using the AHP and MIF methods depends on the thematic layers that are used and the impact and weights assigned by experts. Even small modifications in layer weightings and techniques can have a major influence on the findings. As a result, the importance of the thematic layers and their effect in defining the GWPZ should take precedence over the method used. In MCDA, the subjective attitude of scientists when choosing the influence of individual factors and weights affects the result of the models. Therefore, careful consideration of predictive factors is required to adequately assess the weightings of these factors according to specific site conditions [28,30]. Moreover, the weights for each factor must be precise. Weight values can be obtained from previous studies that investigated areas with similar climate conditions. However, the researcher should ignore outliers and nonlogical factors and weights of factors used by some of the articles.

3.3. Validation

Validation of results is one of the most crucial steps in determining the correctness of any model. Models are not very relevant from a scientific standpoint without a validation [43,105]. Various methods are used to validate GWPZ maps, such as receiver operating characteristics (ROC) analysis, the area under the curve (AUC), and correlation analysis (R²) [6,35,36,43,106,107]. We validated the accuracy of the ten GWPZ models obtained with the MIF and AHP methods using data from 22 wells (Figure 8) by scheming the accumulative regions under different groundwater potential zones and the cumulative percentage number of wells located in each potential zone. The area under the curve (AUC) was calculated using the graph. A good model typically has an AUC value between 0.6 and 0.8, whereas an outstanding model typically has values over 0.9 [44,56]. Our ROC analysis results indicated that areas under the curves (AUC) of the models were 62.16%, 60.86%, and 68.93%, 64.11% for MIF1, AHP1, and MIF5 and AHP5, respectively. The results for model 1 and model 5 showed that the MIF method is better than the AHP method [36,37,57]. However, the results of the ROC analysis for model 3 indicated that the AHP is more suitable than the MIF method for GWPZ [6,35,44,56], according to the AUC values of 66.15%, 62.40% for AHP3 and MIF3, respectively. For model 2 and model 4, the results of the ROC showed that the AHP and MIF methods produced GWPZ maps of similar quality, according to the AUC values of 67.51%, 67.65%, 69.85%, and 69.86% for AHP4, MIF4, AHP2, and MIF2, respectively. In this study, we adopted the second model for each of the two methods (MIF2 and AHP2), as indicated in Table 6.

This study is important for the long-term groundwater management of the study region. However, future work is required to improve groundwater management.

4. Conclusions

The Al-Qalamoun region was used in this study to map the GWPZ using the MIF and AHP methods based on GIS. To determine potential zones, several factors were considered and analyzed, including lineament density, lithology, LULC, drainage density, soil, slope, rainfall, and geomorphology. Remote sensing techniques were also used to construct geomorphology, LULC, slope, lineament density, and drainage density maps for the research area. The weight and score values of each factor were determined using the Multi Influence Factor approach. The GWPZ was calculated using the weight and rating values for each factor, and very high, high, moderate, low, and very low GWPZ were used to classify the study region into five categories. The validation of the results shows that the AHP and MIF methods have similar accuracy for GWPZ. However, the accuracy of the results depends on the model used and on the influencing factors and their weights.

The findings of this study are critical for the long-term management of the study region and the use of groundwater by local governments. Our results will also be beneficial for watershed planners and appropriate watershed management, notably in water budgeting initiatives. Moreover, based on the validation for the MCDA (i.e., the AHP and MIF methods) models, it appears that all models perform equally well, and the focus should be on the careful selection of the factors, which is far more important than the methods used.

For future work, we strongly recommend using evaporation and temperature factors to select the GWPZ. These factors were not considered in our analysis due to the lack of data within the study area. Furthermore, we advise testing the machine learning algorithms, such as random forests, support vector machine, and artificial neural network, when the required data are available. Machine learning algorithms might give a better result.