ORIGINAL ARTICLE

Integrated remote sensing and GIS-based approachfor deciphering groundwater potential zones in the central regionof Saudi Arabia

Shereif H. Mahmoud1 • A. A. Alazba1

Received: 6 May 2015 / Accepted: 13 October 2015 / Published online: 12 February 2016

� Springer-Verlag Berlin Heidelberg 2016

Abstract The scarcity of fresh water resources is a major

challenge facing Saudi Arabia. Groundwater is the major

conventional water source in Saudi Arabia. Therefore, it is

important to identify areas having groundwater potential;

however, the current methods of groundwater exploration

consume a lot of time and money. Alternatively, this paper

introduces a methodology to identify groundwater potential

zones in the Central part of Saudi Arabia using remote

sensing and GIS-based decision support system (DSS) and

fuzzy logic based spatial model. The DSS model generated

suitability maps for groundwater potential (GWP) zones

with five suitability classes, i.e. excellent, very good, good,

poor, and very poor based on the integration of nine the-

matic maps: soil type, land cover and land use, slope,

lithology, rainfall, geological structure, geomorphology,

lineament density, and drainage density using a hierarchi-

cal process analysis (AHP). The spatial distribution of the

GWP zones by AHP-DSS and fuzzy logic model showed

that ‘excellent’ suitable areas for GWP were concentrated

in the main wadi channels within the central, northeastern

and southeastern parts of the study area. The AHP-DSS

model showed that 1.47 % (5608.5 km2) and 4.15 %

(15,787.3 km2) of the study area was classified as excellent

and very good, respectively, while 12.59 %

(47,911.1 km2), 74.82 % (284,670.9 km2) and 6.97 %

(26,519.9 km2) of the area were classified as good, poor

and very poor, respectively. On the other hand, integrating

thematic layers using fuzzy logics indicates that 2.8 %

(10,739 km2), 8.8 % (33,587 km2), 7.9 % (30,106 km2),

60 % (228,361 km2), and 20.4 % (77,705 km2) of the

entire study area is considered as excellent, very good,

good, poor, and very poor areas for groundwater potential,

respectively. The majority of the areas with excellent to

very good suitability had slopes between 0 and 3 % and

were in intensively cultivated fields. The major soil type in

the excellent to very good zones was Arenosols and

Lithosols. The rainfall ranged from 125 to 226 mm/year,

while the main lithological structures are alluvial deposits,

carbonate rocks, and mixed sedimentary consolidated

rocks. These areas are mainly located in the carbonate–

sulfate formations, which extend from the Lower Jurassic

to the Lower Cretaceous and the sandstone aquifers.

Moreover, they have lineament density ranged from 4.75 to

8.34 km/km2, and drainage density ranged from 1 to

1.83 km/km2. Furthermore, the study revealed that linea-

ment density is closely related to groundwater occurrence

and yield and is essential to groundwater surveys, devel-

opment, and management. Validation of the two models

employed depends on comparing existing groundwater

well locations with the suitability map generated using the

proximity analysis tools of ArcGIS 10.2. The results

obtained from AHP-DSS approach and fuzzy model were

verified with field survey and were found to be in very

good and good agreement, respectively. The validation

results showed that the database and methodology used for

developing the suitability model for GWP zones, including

the suitability levels of the criteria and the criteria’s rela-

tive importance weights, to have yielded accurate results.

Keywords Remote sensing � Geographic information

system (GIS) � Groundwater potential zones � Groundwatermanagement � Analytic hierarchy process (AHP) � Fuzzylogic � Decision support system (DSS)

& Shereif H. Mahmoud

eng.shereif1@hotmail.com; shmahmoud@ksu.edu.sa

1 Alamoudi Water Research Chair, King Saud University,

PO Box 2460, Riyadh 11451, Saudi Arabia

123

Environ Earth Sci (2016) 75:344

DOI 10.1007/s12665-015-5156-2

Introduction

Water management in the Kingdom of Saudi Arabia (KSA)

is facing major challenges due to the limited water

resources and increasing uncertainties caused by climate

change. The exploitation of subsurface water from deep

aquifers also depletes resources that have taken decades or

centuries to accumulate and on which the current annual

rainfall has no immediate effect (Mahmoud et al. 2014a;

Mahmoud and Alazba 2014). The country receives an

estimated 158.47 billion m3 of rainwater annually (Al-

Rashed and Sherif 2000). The total reserve in the alluvial

deposits is estimated at 84 billion m3 in the largest single

alluvial reservoir of KSA (Khouri et al. 1986; Ukayli and

Husain 1988; Abdulrazzak 1995). While, the total volume

of groundwater extracted from the deep aquifers in KSA

over the last two decades is estimated around 254.5 bil-

lion m3 were pumped from Saudi Arabia to satisfy the

needs for the expansion in the agricultural sector (Al-

Rashed and Sherif 2000). While, the recharge of the deep

aquifers during the last two decades was limited to

41.04 billion m3 (Al-Rashed and Sherif 2000). The major

item of water consumption in KSA is the agriculture sector

about 20 billion m3/year by the year 2000 (Al-Rashed and

Sherif 2000). The agricultural water demands were

83–90 % of the total water demands during 1990–2009

(Chowdhury and Al-Zahrani 2015). To address the water

conservation policy, KSA has adopted a strategy to reduce

agricultural water demands by introducing modern irriga-

tion techniques, which lead to a decline in consumption of

water for agricultural purposes, at an average annual rate of

2.5 % between 2004 and 2009 (MOEP 2014). Owing to

such limitations in water resources and the potential

increase in the area under cultivation, it is necessary to

delineate groundwater potential zones for agricultural

development.

Remote sensing (RS) and geographic information sys-

tem (GIS) techniques are widely used for the management

of various natural resources (Dar et al. 2011; Magesh et al.

2011; Kumar et al. 2012; Mahmoud 2014a, b; Mahmoud

et al. 2014a, b, c, 2015; Mahmoud and Alazba 2015a, b;

Mahmoud and Tang 2015). In recent years, a number of

attempts to delineate potential groundwater zones using RS

data has been applied successfully by various researchers

Murthy (2000), Srinivasa Rao et al. (2000), Jaiswal et al.

(2003), Israil et al. (2006), Srivastava and Bhattacharya

(2006), Kumar et al. (2007), Kukillaya (2007), Semere and

Woldai (2007), Mondal et al. (2008), Chowdhury et al.

(2009), Dar et al. (2011), Dhakate et al. (2012), Nag and

Ghosh (2013), Deepika et al. (2013), Balakrishna et al.

(2014), Venkateswaran et al. (2014), Sahebrao et al. (2014)

and Al-Abadi (2015).

Various researchers have used different factors for

delineating GWP zones such as lithology, topography,

geological structures, slope, geomorphology, rainfall,

drainage pattern, and lineaments. However, these fac-

tors have rarely been studied together because of the non-

availability of data. Hence, a systematic study of these

factors led to a better delineation of the prospective zones

in an area, which is then followed up on the ground through

detailed hydrogeological and geophysical investigations.

Several researchers have reported a close relationship

between lineaments and groundwater flow and yield

(Magowe and Carr 1999; Fernandes and Rudolph 2001;

Mabee et al. 2002; Solomon and Quiel 2006; Neves and

Morales 2007). Meanwhile, others studied the relationships

between groundwater productivity and lineament density

rather than the lineament itself (Hardcastle 1995). There-

fore, mapping of lineaments closely related to groundwater

occurrence and yield is essential to groundwater surveys,

development, and management. Nag (2005) has used the

lineament and hydro geomorphology-based approach in

delineating GWP zones. Gupta and Srivastava (2010) used

RS and GIS to identify GWP zones in the hilly terrain of

Paragraph, Gujarat, India. The various thematic maps

prepared for delineating GWP zones are lineament density,

drainage density, digital elevation model (DEM), slope

map and land use/land cover (LULC). Dar et al. (2011)

identified GWP zones in the Mamundiyar basin (India)

using geospatial technology based on six thematic layers;

geomorphology, geology, land use/land cover, lineament,

relief, and drainage. Nag and Ghosh (2013) used geomor-

phology, lineament density and slope to delineate GWP

zone in Chhatna Block, Bankura District, West Bengal

(India). Konkul et al. (2014) applied a similar method to

map the hydrogeological characteristics and groundwater

potentiality of Huay Sai area (Thailand), using potential

surface analysis. Antonakos et al. (2014) developed a

method to produce a distribution map of site suitability for

drilling new production boreholes in the Korinthia Pre-

fecture (Greece) using multicriteria analysis within a GIS

environment. The most important of these criteria relate to

the productivity of the aquifers, groundwater quality, and

economic and technical issues.

More recently, studies integrating RS and GIS have

gained ascendance in targeting GWP zones (Balakrishna

et al. 2014; Venkateswaran et al. 2014; Sahebrao et al.

2014; Al-Abadi 2015). Balakrishna et al. 2014 conducted a

study to identify GWP zones in fractured aquifers of

ophiolite formations, Port Blair (South Andaman Islands)

using electrical resistivity tomography (ERT) and vertical

electrical sounding (VES). Al-Abadi 2015 attempted to

identify GWP zones at northeastern Wasit and Missan

governorates (Iraq) using a data-driven weights of evidence

344 Page 2 of 28 Environ Earth Sci (2016) 75:344

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technique in framework of GIS. The identification of

potential groundwater zones was identified in two steps (1)

location inventory map consisting of 143 with relatively

high yields ([8 L/s) was prepared. (2) Eight influencing

groundwater factors, namely altitude, slope, geology, land

use/land cover, distance to roads, distance to faults, aquifer

type, and depth of wells were prepared and integrated into

a spatial database.

The analytic hierarchy process (AHP) is a multi-criteria

decision-making approach introduced by Saaty (1977,

1980, 1990, 1994, 2008). As a key decision-making tool,

AHP was used in this study to obtain appropriate solutions

to the suitability assessment for GWP zones. Saaty (1990)

noted that the process includes the structuring of factors

that are selected in a hierarchy, starting from the overall

aim to the criteria, sub-criteria and alternatives. A type of

GIS-based MCDM that combines and transforms spatial

data (input) into result decisions (output), the AHP uses

geographical data, the decision maker’s preferences and

manipulation of the data and preferences according to

specified decision rules referred to as factors and con-

straints, respectively. Several research studies used an

integration between RS data and GIS-based MCDM suc-

cessfully in a wide variety of applications such as rainwater

harvesting, groundwater recharge, artificial recharge, rain-

fall-runoff modelling (Zahedi 1986; Vargas 1990; Forman

and Gass 2001; Kumar and Vaidya 2006; Hossain et al.

2007; Wang et al. 2009; Young et al. 2010; Garfı̀ et al.

2011; Anane et al. 2012; Krois and Schulte 2014; Mah-

moud 2014b; Mahmoud et al. 2014a, b, c; Mahmoud and

Alazba 2014).

This paper introduces a methodology to identify

groundwater potential zones in the Central part of Saudi

Arabia using remote sensing and GIS-based decision sup-

port system (DSS) and fuzzy logic based spatial model. In

addition, this study aims to develop a thematic map

showing the potential zones for groundwater. Furthermore,

the reliability of groundwater potentiality mapping was

verified by using existing well data for the two techniques.

This study will help decision makers and water resources

planners in Riyadh province, Saudi Arabia in proper

development and utilization of both groundwater and sur-

face water resources for eliminating water scarcity and

thereby improving the irrigation practices.

Description of the study area

Al-Riyadh Province (Fig. 1) is the second largest province in

Saudi Arabia. It has an area of 380,497.8 km2 and a popu-

lation of 6,777,146 (2010), making it the second largest

province in terms of both area (behind the Eastern Region)

and population (behind Makkah Region). It is situated in the

center of the Arabian Peninsula (24� 380 N and longitude 46�430 E) on a large plateau. Its relief ranges from 200 to

1200 m above mean sea level (Fig. 2), constituting a part of

the Nejd Plateau. This plateau extends to the Tuwayq

Mountains on its western edge, to the Awanid Scarp on the

northern edge, to the Kharj rise on its southern edge, and to

the Dahna sand belt on its eastern edge. However, this whole

tableland is broken by protruding long cliffs that are formed

by the fault scarps near the AwanidMountains and by the Hit

Scarp between Riyadh and Kharj. Its capital is the city

of Riyadh, which is also the national capital. The recently

completed and ongoing constructions boast of having some

of the ambitious architectural designs in the Kingdom. Such

as, King Abdullah Financial District Metro Station and a host

of other forthcoming proposed projects. Riyadh is the capital

and largest city of Saudi Arabia. It is also the capital of

Riyadh Province. The average high temperature in July is

42.6 �C. Winters are warm with cold, windy nights. The

monthly average relative humidity ranges from 15 % (during

summer) to 51 % (winter season). The mean annual relative

humidity is 32.5 %. The overall climate is arid, and the city

experiences an annual rainfall ranges from 41 to 230 mm/

year. The construction of 57 dams in the region for

groundwater recharge and rainwater harvesting, the increase

of water consumption, the expansion in the agriculture sector

have a significant effect on relative humidity. Such factors

may cause microclimatic changes. Flash floods occur peri-

odically in Riyadh province due to several factors, including

rugged topography and geological structures. The northern

areas of Al-Riyadh province have the highest potential risk of

flood generation with a large flash floods record. Which

always causes traffic jams, and suspend studies in schools

and universities for the day due to its serious damage.

Methodology

In this study, identification of the GWP zones and the

groundwater modeling has been done for the central region

of KSA using RS and GIS-based decision support system

and fuzzy logic based spatial model. The methodology

adopted for the present study is shown in Fig. 3.

The major steps in mapping in this study are:

• Criteria selection (factors influence groundwater

movement),

• Remote sensing data (pre-processing of remote sensing

data),

• Data collection (collection of spatial data for the

criteria through various sources, including a GPS

survey to supplement and generate maps using GIS,

• GIS processing (building database),

• Assessment criteria for suitability levels,

Environ Earth Sci (2016) 75:344 Page 3 of 28 344

123

Fig. 1 A location map of the

study area

344 Page 4 of 28 Environ Earth Sci (2016) 75:344

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• Assignment of criteria weights,

• Development of a GIS-based suitability model, which

combines maps through a Spatial Multi Criteria Eval-

uation process (SMCE),

• Generation of suitability maps,

• Validation of the developed GWP map.

Selecting factors influencing groundwater storage

potential

The groundwater conditions at any given area can vary greatly

according to various factors that influence the occurrence and

replenishment of groundwater. In this study, nine criteria were

selected for mapping GWP zones. These factors are expressed

in terms of nine thematic layers, namely: soil type, Land cover

and land use (derived from available RS data), slope (i.e.,

topography), lithology, rainfall, geological structure, geo-

morphology, lineament density, drainage density. The criteria

and their application for mapping GWP zones in the region are

presented in Fig. 4. Because of the different scales on which

the criteria measured, the values contained in the criterion

maps have to be converted into comparable units for SMCE.

Therefore, the criteria maps were re-classed into six compa-

rable units or suitability classes, namely: 6 (‘excellent’), 5

(‘very good’), 4 (‘good’), 3 (‘moderate’), 2 (‘poor’) and 1

(‘very poor’). The suitability classes were then used as the

basis to generate the criteria map.

Assessment of suitability level for criteria using

AHP-DSS approach

The values for each suitability category were scaled from 1

to 9 and were based on the criteria proposed by various

studies (Chowdhury et al. 2009; Dar et al. 2011; Dhakate

et al. 2012; Nag and Ghosh 2013; Deepika et al. 2013;

Balakrishna et al. 2014; Venkateswaran et al. 2014;

Sahebrao et al. 2014; Al-Abadi 2015). The suitability

rankings for soil type, land cover and land use, slope,

lithology, rainfall, geological structure, geomorphology,

lineament density, drainage density are shown in Table 1.

Recharge rates are highest in areas with minimal slope

allowing more time for rainwater to infiltrate in addition,

GWP to be generally more appropriate in areas with a

rather flatter slope, whereas the high slope generate less

Fig. 2 Relief map of the study

area

Environ Earth Sci (2016) 75:344 Page 5 of 28 344

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recharge. Areas with high lineament density are good for

GWP zones (Haridas et al. 1994). Areas with a large

rainfall surplus are ranked as highly suitable because the

surplus ensures the availability of rainwater for ground-

water recharge (Mahmoud 2014a).

Assignment of criteria weights using AHP-DSS

approach

The criteria were assigned weights by applying the pair-

wise ranking and rank sum methods. The final weight

calculation requires the computation of the principal

eigenvector of the pairwise comparison matrix to produce a

best-fit set of weights. The WEIGHT module in the IDRISI

software was used for this calculation. The weighting

procedure is based on AHP. The relative importance of the

pairwise combinations of the factors was judged using the

following 9-point rating scale:

The expected value method was used to calculate the

weight, Wk, for criterion k according to Eq. (1) (Janssen

and Van Herwijnen 1994):

Wevk ¼

Xnþ1�k

i¼1

1

n nþ 1� ið Þ ð1Þ

where n = the number of criteria and k = criterion.

The rank sum method was used to calculate the weight,

Wk, for criterion k according to Eq. (2):

W rsk ¼ nþ 1� kPn

i¼1 nþ 1� ið Þ ð2Þ

where n = the number of criteria and k = criterion.

The accuracy of the pairwise comparisons was assessed

by calculating the consistency index (CI). This index

determines the inconsistencies in the pairwise judgments

and is a measure of departure from consistency based on

the comparison matrices. It is expressed as

Fig. 3 The work flow chart

1/9 1/7 1/5 1/3 1 3 5 7 9

Extremely Very strongly Strongly Moderately Equally Moderately Strongly Very strongly Extremely

Less important More important

344 Page 6 of 28 Environ Earth Sci (2016) 75:344

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CI ¼ k� n

n� 1ð3Þ

where, is the average value of the consistency vector and

n is the number of columns in the matrix (Garfı̀ et al. 2009;

Saaty 1990; Vahidnia et al. 2008). The consistency ratio

(CR) was then calculated as follows:

CR ¼ CI=RI ð4Þ

Where RI is the random index, which depends on the

number of elements being compared (Garfı̀ et al. 2009).

Table 2 presents the RIs of the matrices in the order 1–15,

as derived by Saaty (1980).

The pairwise rating procedure has several advantages.

First, the ratings are independent of any specific mea-

surement scale. Second, the procedure, by its very nature,

encourages discussion, leading to a consensus on the

weights to be used. In addition, the criteria that are omitted

from initial deliberations are quickly uncovered through

the discussions that accompany this procedure. Experience

has shown, however, that while it is not difficult to come up

with a set of ratings by this means, the ratings are not

always consistent. Thus, the technique used for developing

weights from these ratings also needs to be sensitive to the

problems of inconsistency and error. To provide a sys-

tematic procedure for comparison, a pairwise comparison

matrix was created by setting out one row and one column

for each factor in the problem (Table 3). The rating was

then calculated for each cell in the matrix. Because the

matrix is symmetrical, the ratings are provided for half of

the matrix and then inferred for the other half.

The consistency ratio of the matrix, which shows the

degree of consistency achieved when comparing the cri-

teria or the probability that the matrix rating was randomly

generated, was 0.018, which indicates acceptable consis-

tency (Saaty 1977). The values for different thematic are

shown in Table 4.

Fuzzy logic based spatial model for prediction

of GWP zones

In classical set theory, an object either belongs to a set or is

not the member of the set. Thus, we call it a crisp set with a

sharp and rigid boundary. There is no partial membership,

which makes it impossible to model some concepts with

transitional membership. To solve this problem, Zadeh

(1965) proposed the fuzzy set theory, which was further

developed by other researchers (Kaufmann and Gupta

1988; Zimmermann 1991; Zadeh 1997; Ross 2009). The

membership of a set is defined as true or false, 1 or 0. The

value 1 indicates full certainty that the value is in the set,

and 0 indicates with full certainty that it is not in the set.

All other values are some level of possibility, with the

higher values indicating more likelihood of membership.

The process of transforming the original input values to the

0–1 scale of possibility of membership is called the

fuzzification process.

Fig. 4 A conceptual framework for groundwater potential mapping

Environ Earth Sci (2016) 75:344 Page 7 of 28 344

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The individual classes for each map might be defined

according to their degree of membership. The classes in

any map can be associated with fuzzy membership values

in an attribute table. Fuzzy membership values must lie in

the range (0, 1), but there are no practical constraints on the

choice of fuzzy membership values (Bonham-Carter 1996).

Given two or more maps with fuzzy membership functions

for the same set, a variety of operations can be employed to

combine the membership values together. Zimmermann

and Zysno (1980) and Zimmermann (1991) discussed a

variety of combination rules based on fuzzy mathemat-

ics. An et al. (1991) discuss five operators, namely fuzzy

AND, fuzzy OR, fuzzy algebraic product, fuzzy algebraic

sum, and fuzzy gamma operator. In fuzzy algebraic prod-

ucts, the combined membership function is defined as

lcombination ¼Yn

i¼1

li ð5Þ

where li is the fuzzy membership function for the ith map,

and i = 1, 2, 3… n maps are to be combined. The com-

bined fuzzy membership values tend to be very small with

this operator, due to the effect of multiplying several num-

bers less than 1. The output is always smaller than, or equal to

the smallest contributing membership value, and is therefore

‘decreasive’ (Bonham-Carter 1996). In this research fuzzy

algebraic product operator is used because of its high sen-

sitivity in specifying GWP zones. Table 5 presents the

membership functions for each of the thematic layers.

To analyze the relationships and interaction between all

the sets of the multiple criteria in the overlay model, fuzzy

overlay techniques are used. Since the fuzzification process

is based on the degree of membership to a set, the overlay

techniques describe the interaction of the inaccuracies in

Table 1 Suitability levels for different factors for groundwater

potential zones

Sl. no. Thematic layers Individual features Suitability

values

1 Lithology Alluvial deposits 6

Dunes or shifting sand 5

Acid plutonic rocks 1

Precambrian basement 2

Carbonate rocks 5

Mixed sedimentary

consolidated rocks

6

Siliciclastic sedimentary

consolidated rocks

3

2 Geology Cenozoic 4

Mesozoic—Jurassic and

Cretaceous

3

Mesozoic—Triassic 5

Plutonic rocks 1

Precambrian

(Archean ? Proterozoic)

2

Quaternary 4

Upper paleozoic (Dev, Car,

Per)

5

3 Soil type Arenosols 6

Lithosols 5

Miscellaneous land units 3

Regosols 4

Solonchaks 3

Yermosols 2

4 Slope (%) 0–1 6

1–3 5

3–5 4

5–15 3

15–45 2

[45 1

5 Lineaments density

(km/km2)

0–0.72 1

0.721–2.03 2

2.04–3.3 3

3.31–4.74 4

4.75–8.34 6

6 Drainage density

(km/km2)

0–0.34 1

0.34–0.70 2

0.70–1 4

1–1.83 5

7 Land use/cover Urban and Built-Up Land 1

Irrigated Cropland and Pasture 6

Sparsely Vegetated 3

Shrubland 4

Bare soil 2

Mixed Tundra 3

8 Rainfall (mm/year) 0–40 1

40–60 2

60–95 3

95–125 4

125–155 5

155–226 6

Table 1 continued

Sl. no. Thematic layers Individual features Suitability

values

9 Geomorphology

(stream order)

1 1

2 2

3 3

4 4

5 5

Table 2 Random indices (RI)

for n = 1, 2,…,15 (Saaty 1980)n RI n RI n RI

1 0.00 6 1.24 11 1.51

2 0.00 7 1.32 12 1.48

3 0.58 8 1.41 13 1.56

4 0.90 9 1.45 14 1.57

5 1.01 10 1.49 15 1.59

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the membership of the sets. The fuzzy overlay techniques

are based on set theory. Set theory is the mathematical

discipline quantifying the membership relationship of

phenomenon to specific sets. In fuzzy overlay, generally a

set corresponds to a class. The available fuzzy set overlay

techniques are Fuzzy And, Fuzzy Or, Fuzzy Product, Fuzzy

Sum, and Fuzzy Gamma. Each of these techniques

describes the cell’s membership relationship to the input

sets. Fuzzy And creates an output raster where each cell

value is given the minimum assigned fuzzy value for each

of the sets the cell location belongs to.

Results

Development of thematic maps

Details of thematic maps relevant for identification of

GWP are given in the following subsections.

Lineaments density

Lineaments are any linear features that can be picked out as

lines in satellite imagery. If geological, these are usually

faults, joints, or boundaries between stratigraphic forma-

tions. Other causes of lineaments include roads and rail-

roads, contrast-emphasized contacts between natural or

man-made geographic features (e.g., fence lines), or vague

‘‘false alarms’’ caused by unspecified factors. Lineaments

are hydro-geologically very important as they provide the

pathways for groundwater movement. Lineament density

of an area can indirectly reveal the groundwater potential,

since the presence of lineaments usually denotes a per-

meable zone. There are several techniques for the delin-

eation of linear features and geomorphological parameters

of any area. The automatic lineament delineation is based

on the decision of the most appropriate band for edge

enhancement, followed by edge sharpening enhancement

technique, and apply LINE module of PCI Geomatica for

Table 4 Normalized pairwise comparison

Lineaments

density

Slope Drainage

density

Land

use/cover

Soil Rainfall Geomorphology Geology Lithology

Lineaments

density

0.048 0.006 0.049 0.155 0.119 0.027 0.067 0.168 0.009

Slope 0.336 0.043 0.295 0.017 0.020 0.020 0.054 0.056 0.021

Drainage density 0.048 0.007 0.049 0.052 0.178 0.081 0.089 0.084 0.009

Land use/cover 0.016 0.129 0.049 0.052 0.030 0.040 0.134 0.034 0.028

Soil 0.024 0.129 0.016 0.103 0.059 0.161 0.054 0.042 0.169

Rainfall 0.144 0.172 0.049 0.103 0.030 0.081 0.067 0.056 0.254

Geomorphology 0.192 0.215 0.148 0.103 0.297 0.322 0.268 0.336 0.169

Geology 0.048 0.129 0.098 0.259 0.238 0.242 0.134 0.168 0.254

Lithology 0.144 0.172 0.246 0.155 0.030 0.027 0.134 0.056 0.085

Table 3 The pairwise comparison matrix for groundwater potential zones

Lineaments

density

Slope Drainage

density

Land

use/cover

Soil Rainfall Geomorphology Geology Lithology

Lineaments

density

1 1/7 1 3 2 1/3 1/4 1 1/9

Slope 7 1 6 1/3 1/3 1/4 1/5 1/3 1/4

Drainage density 1 1/6 1 1 3 1 1/3 1/2 1/9

Land use/cover 1/3 3 1 1 1/2 1/2 1/2 1/2 1/3

Soil 1/2 3 1/3 2 1 2 1/5 1/4 2

Rainfall 3 4 1 2 1/2 1 1/4 1/3 3

Geomorphology 4 5 3 2 5 4 1 2 2

Geology 1 3 2 5 4 3 1/2 1 3

Lithology 3 4 5 3 1/2 1/3 1/2 1/3 1

Environ Earth Sci (2016) 75:344 Page 9 of 28 344

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recognizing lineaments. For example, Pradhan and Lee

(2010) used manually extraction method based on auto-

matically pre-processed images with enhanced edges.

Abdullah et al. (2010) used PCI Geomatica software with

module LINE, which is used for extraction of linear fea-

tures from raster images.

In the present study, PCI Geomatica 2014 was used for

extracting lineaments (Fig. 5a) from a Landsat TM/ETM

image for the year 2013 with 15 m resolution obtained

from King Abdulaziz City for Science and Technology

(KACST). The lineament extraction algorithm of PCI

Geomatica software consists of edge detection, threshold-

ing and curve extraction steps (PCI Geomatica 2001). The

lineament density map (cumulative length of lineaments

per unit area) was established using the Density function in

the ArcGIS 10.1 Spatial Analyst extension. The raster layer

obtained was reclassified into five classes according to the

infiltration rates. Areas with high lineament density are

good for GWP zones (Haridas et al. 1994). Therefore,

higher values were assigned to more dense areas to define

the infiltration index rates raster. These classes have been

assigned to ‘excellent’ (4.75–8.34 km/km2), ‘good’

(3.31–4.74 km/km2), ‘moderate’ (2.04–3.3 km/km2),

‘poor’ (0.721–2.03 km/km2), and ‘very poor’ (0–0.72 km/

km2). The lineament density map of the study area is

shown in Fig. 5b, and it reveals that the high lineament

density is observed in the center of the study area with a

value ranging from 0 to 8.34 km/km2.

Slope

A DEM with 30-m resolution (Fig. 6) developed at

KACST was used to generate the slope map for Al-Riyadh.

Sinks and flat areas were removed to maintain the

Table 5 Fuzzy membership functions for each of the thematic layers

Thematic layers Individual features Fuzzy

membership

Lithology Alluvial deposits 0.69

Dunes or shifting sand 0.57

Acid plutonic rocks 0.11

Precambrian basement 0.23

Carbonate rocks 0.57

Mixed sedimentary

consolidated rocks

0.69

Siliciclastic sedimentary

consolidated rocks

0.34

Geology Cenozoic 0.70

Mesozoic—Jurassic and

Cretaceous

0.52

Mesozoic—Triassic 0.87

Plutonic rocks 0.17

Precambrian

(Archean ? Proterozoic)

0.35

Quaternary 0.70

Upper paleozoic (Dev, Car,

Per)

0.42

Soil type Arenosols 0.51

Lithosols 0.42

Miscellaneous land units 0.25

Regosols 0.34

Solonchaks 0.25

Yermosols 0.17

Slope (%) 0–1 0.57

1–3 0.48

3–5 0.38

5–15 0.29

15–45 0.19

[45 0.10

Lineaments density

(km/km2)

0–0.72 0.07

0.721–2.03 0.14

2.04–3.3 0.22

3.31–4.74 0.29

4.75–8.34 0.43

Drainage density

(km/km2)

0–0.34 0.07

0.34–0.70 0.13

0.70–1 0.27

1–1.83 0.33

Land use/cover Urban and built-up land 0.06

Irrigated cropland and pasture 0.34

Sparsely vegetated 0.17

Shrubland 0.23

Bare soil 0.11

Mixed tundra 0.17

Table 5 continued

Thematic layers Individual features Fuzzy

membership

Rainfall (mm/year) 0–40 0.11

40–60 0.21

60–95 0.32

95–125 0.42

125–155 0.53

155–226 0.64

Geomorphology

(stream order)

1 0.23

2 0.46

3 0.68

4 0.91

5 1

344 Page 10 of 28 Environ Earth Sci (2016) 75:344

123

Fig. 5 Lineaments for the study

area (A) and lineaments density

(B)

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123

continuity of flow of water to the catchment outlets. The

KACST DEM data were used to derive the slope map

(Fig. 7), which is presented in terms of percentage using

the ‘slope’ function in ArcGIS. Slope gradient is an

important factor that affects the infiltration of rainfall and

groundwater recharge. The slope layer was used to deter-

mine GWP zones (Ganapuram et al. 2009). Moreover,

recharge rates are highest in areas with minimal slope

allowing more time for rainwater to infiltrate, whereas the

high slope generate less recharge because water runs

rapidly off the surface during rainfall and generate high

runoff allowing insufficient time to infiltrate the surface

and recharge the saturated zone. The slope was divided into

six categories. The areas having 0–1 % (nearly level) fall

into the excellent category because of the flat terrain and

relatively high infiltration rate and are usually a very good

recharge zone. The areas having 1–3 % (very gently

sloping) were considered as very good for groundwater

storage potentiality due to slightly undulating topography

with little surface runoff. The areas having a slope of

3–5 % (gently sloping) lead to a relatively moderate to low

runoff, and hence are categorized as good for groundwater

recharge. The areas having a slope of 5–15 % (moderately

sloping) were considered as moderate recharge zone, due to

relatively high to moderate runoff and allowing insufficient

time to recharge the saturated zone. Areas having a slope of

15–45 % (moderately steep-to-steep slope) and higher than

45 % (very steep) were categorized as poor and very poor

recharge zones, as they generate rapid and very high runoff

and extremely low infiltration rate.

Drainage density

Drainage density is defined as the closeness of spacing of

channels within a basin (Horton 1932). It is an indication of

basin permeability. It depends on the climate, lithology,

relief, infiltration capacity, vegetation cover, surface

roughness, and runoff intensity index (Bali et al.

2012). Soil permeability and underlying rock type affect

the runoff in a watershed; impermeable ground or exposed

bedrock will lead to an increase in surface water runoff and

therefore more frequent storms. Regions with high relief

will also have a higher drainage density than other drainage

basins if the other characteristics of the basin are the same,

Fig. 6 The digital elevation

model for the study region

344 Page 12 of 28 Environ Earth Sci (2016) 75:344

123

higher densities (also mean a high bifurcation ratio) can

indicate a greater flash flood risk. Drainage network map

(Fig. 8a) was derived from the filled DEM of the study. A

drainage density map (Fig. 8b) was prepared using density

analysis tool in ArcGIS software, as the total length of all

the streams in a drainage basin divided by the total area of

the drainage basin and is expressed as:

Drainage density ¼ RLt=A ð6Þ

where RLt = total length of all the ordered streams,

A = area of the basin.

The study area has been classified into four classes.

Drainage density is an inverse function of permeability.

These classes have been categorized as ‘very good’

(1–1.83 km/km2), ‘good’ (0.7–1 km/km2), ‘poor’

(0.34–0.7 km/km2), and ‘very poor’ (0–0.34 km/km2).

Very good drainage density is concentrated in the central

part of the study area. The poor and very poor drainage

density cover the majority of the study area. The less

permeable a rock is, the less the infiltration of rainfall

(Chowdhury et al. 2009). A low drainage density area

causes more infiltration (Srivastava and Bhattacharya

2006) and decreased surface runoff as compared to a high

drainage density region. It means that areas having high

density are not suitable for GWP zones because of the high

surface runoff (Dinesh Kumar et al. 2007).

Land use/cover

A Landsat TM/ETM image for 2013 (2013, with 30 m res-

olution) was incorporated with collected data and ultimately

served in categorizing land use and land cover (LULC). Iso

Cluster unsupervised classification and Maximum likeli-

hood classification function in the ArcGIS Spatial Analyst

were used for the unsupervised classification. Training

sampleswere collected during field surveys to create spectral

signatures (i.e., reflectance values) for the supervised clas-

sification to identify what the clusters represented (e.g.,

water, bare earth, dry soil, etc.). The LULC map classified

into six main classes (Fig. 9). Urban and built-up land

accounts for about (36,699.2 km2) 9.65 % of the total area.

While, irrigated cropland and pasture accounts for about

43,642.6 km2 or about 11.47 % of the total area, bare soil

occupies 177,248.4 km2 (46.58 % of the total area). In

Fig. 7 A slope map for the

study region

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123

Fig. 8 Drainage network

(a) and drainage density (b) forthe study area

344 Page 14 of 28 Environ Earth Sci (2016) 75:344

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addition, sparsely vegetated land, shrubland and mixed

tundra accounts for about 28,531.1 km2 (7.5 %),

67,107.8 km2 (17.64 %) and 27,268.6 km2 (7.17 %) of the

total area, respectively. The areas covered by each land cover

and land use are presented in Table 6.

Assessing the accuracy of a land cover map requires

ground truthing. Georeferenced ground truthing points

were collected using a GPS unit and used to validate the

land cover and land use maps. Validation analysis was

performed using the Kappa Agreement Index (KIA) where

a value exceeding 0.8 indicates a high classification per-

formance (Jensen 2005). The overall kappa statistic was

0.87, indicating that the classification of the land use and

land cover map was accurate, Fig. 9 shows the proposed

project for Al-Riyadh Metro. The contracts for the design

and construction of Riyadh’s new US $22.5 billion metro

system, the next major step in the development of the

largest public transport project in the world—the Riyadh

Public Transport Project. The Project encompasses a city-

wide metro, bus network, and park and ride services. This

project will connect most of the capital together and make

the transportation process easier from one place to another

in a limited time. It will lead to urban expansion and give

the people the opportunity to move to farther places from

their work to establish new house. However, it may

increase the prices of new real estate in the region.

Soil type

The soil map of the study area (Fig. 10) was prepared from

the published soil map obtained from Ministry of Agri-

culture. The study area covered by six different soil types:

Fig. 9 An LULC map for the

study region

Table 6 Areas covered by the different land cover and land use

Land cover/land use Area (km2) % of total area

Urban and built-up land 36,699.2 9.65

Irrigated cropland and pasture 43,642.6 11.47

Sparsely vegetated 28,531.1 7.50

Shrubland 67,107.8 17.64

Bare soil 177,248.4 46.58

Mixed tundra 27,268.6 7.17

Total 380,497.8 100

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123

Arenosols, Lithosols, Miscellaneous land units, Regosols,

Solonchaks, and Yermosols. Arenosols soil is a sandy soils

with little profile development, this type of soil has high

permeability, low water storage capacity and low biologi-

cal activity, which all promote decalcification of the sur-

face layers of Arenosols in the dry zone like Riyadh

province, even though the annual precipitation sum is

extremely low. Lithosols soil is a thin soils over rock

derived from sedimentary sandstones ‘‘Shallow soils with

rock \10 cm from the surface’’, its material is usually

coarse textured with a very low clay content and minimal

organic matter accumulation at the surface. Lithosols are

strongly acid and have a low water holding capacity due to

the coarse texture, abundant stones, and shallow depth.

However, infiltration rates can be high. Miscellaneous land

units, this soil consist of dunes, salt flats, and rock debris or

desert detritus. Regosols soil consists of a surface layer of

rocky material and its texture is mainly coarse tex-

ture. Solonchaks: Salty soil with little horizon

development. Yermosols (aridic soils) have an argillic

(clay accumulation), likely formed during a period with a

wetter climate. Water deficiency is the dominant charac-

teristic of Aridisols with adequate moisture for plant

growth present for no more than 90 days at a time. Crops

cannot be grown in these soils without irrigation. Aridic

soils are commonly light in color, and low in organic

matter content. Lime and salt accumulations are common

in the subsurface horizons.

The majority of the study area is dominated by Regosols

soil, which is about 62.6 %. This soil was classified as

good for GWP zones due to its coarse texture and high

infiltration rate. Followed, by Lithosols soil, which cover

about 12.6 % of the total area. This soil is mainly located

in the central regions of the study area. Moreover, this soil

was categorized as very good for GWP zones due to its

coarse texture, very low clay content, and low water

holding capacity. Miscellaneous land units are found along

the southeastern and northwestern parts of the study area.

Fig. 10 Soil type map for the

study area

344 Page 16 of 28 Environ Earth Sci (2016) 75:344

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These units represent 10.3 % of the total area, and classi-

fied as moderate recharge zones. Arenosols soil only

occupied 1.4 % of the study area. This type of soil has high

permeability, low water storage capacity, and low biolog-

ical activity. Therefore, it was classified as excellent

recharge zones according to their influence on groundwater

occurrence and excellent rate of infiltration. Solonchaks

(salty soil), and Yermosols soils occupied 11.2 and 1.9 %

of the total area. The infiltration rate of this soil is mod-

erately low due to clay content. Clay soil is classified as

poor due to poorly drained, slowly permeable, severely

eroded, and low hydraulic conductivity (Chowdhury et al.

2009). Therefore, these soils were considered as moderate

and poor recharge areas, respectively. The areas covered by

the different soil types are presented in Table 7.

Rainfall (rainfall surplus)

Riyadh has a very dry climate, which makes the summer

heat bearable. Precipitation at Riyadh is influenced by

Mediterranean winter, which results from a frontal system

that moves toward the east along the Mediterranean Sea

from the Atlantic Ocean and then travels inland, reaching

the Najd Plateau. Climatic data obtained from meteoro-

logical department, Ministry of Agriculture and Ministry of

Water and Electricity, these data were not sufficient to

meet the requirements of this study. Therefore, the fol-

lowing climate data were interpolated to obtain the spatial

rainfall distribution using the inverse distance-weighted

method: (1) Satellite images for monthly global precipita-

tion from 1979 to 2009 obtained from the World Data

Center for Meteorology. (2) NASA Tropical Rainfall

Measuring Mission (TRMM) Monthly Global Precipitation

Data from 1998 to 2010 obtained from NASA GES Dis-

tributed Active Archive Center. Penman–Monteith method

(Monteith 1965) was used for estimating the potential

evapotranspiration (ET) as follows:

ET ¼ DRn þ ðea � edÞ � q�cpra

k Dþ c � 1þ rsra

� �� � ð7Þ

where Rn = net radiation (W/m2), q = density of air,

cp = specific heat of air, rs = net resistance to diffusion

through the surfaces of the leaves and soil (s/m), ra = net

resistance to diffusion through the air from surfaces to

height of measuring instruments (s/m), c = hygrometric

constant, D = de/dT, ea = saturated vapour pressure at air

temperature and ed = mean vapour pressure.

ET refers to the total amount of water vapor enter into

the atmosphere through either the evaporation of water

from open water and soil surface or transpiration of water

from vegetation leaves. Estimating ET has been a major

scientific challenge for many years until Penman (1948)

came up with the combination approach, which solved the

problem for open water or wet soil surface, and Penman

(1953) further improved the model for the unsaturated

surface of single leaf by introducing resistance. Monteith

(1965) applied the Penman Equation for the canopy. The

Penman equation since become the famous Penman–

Monteith equation. The amount of ET is equally expressed

in two units: the amount of water left the surface in ET

(mm) or the amount of energy used in ET (W/m2). The

rainfall surplus (P-ET) map (Fig. 11) calculated by sub-

tracting long-term average monthly evapotranspiration

values of the precipitation for all meteorological stations

covering the period from 1950 to 2013. Evaporation losses

from the water surface in the area of Riyadh is very high

due to the kind of climate, which is hot and dry. The

records of the hydrological station at Riyadh show that the

mean monthly evaporation rate is 228 mm/month. The

total annual evaporation rate is 2690 mm/year. The maxi-

mum evaporation rate occurs during July, while the mini-

mum evaporation rate takes place in December. The annual

rainfall surplus calculated at each meteorological station by

adding only the positive values of the difference (P-ET),

spatial distribution of rainfall surplus maps generated by

interpolating previous data values using ArcGIS.

Geomorphology

The designation of stream order is the first step in the

drainage basin analysis. As per the Strahler’s (1964)

ordering scheme. In the Strahler method, all links without

any tributaries are assigned an order of 1 and are referred to

as first order. The stream order increases when streams of

the same order intersect. Therefore, the intersection of two

first-order links will create a second-order link, the inter-

section of two second-order links will create a third-order

link, and so on. The study area is a 5th order drainage

basin. Higher stream order is associated with greater dis-

charge. In addition, groundwater prospects are very high

near higher order streams (Dinesan et al. 2015). Various

stream orders indicate that streams up to 3rd order are

surging through highly dissected mountainous terrain,

which facilitates rapid runoff and allowing insufficient time

to recharge the saturated zone and less recharge into the

Table 7 Areas covered by the different soil types

Soil type Area (km2) % of total area

Arenosols 5430.3 1.4

Lithosols 47,874.9 12.6

Miscellaneous land units 39,032.2 10.3

Regosols 238,323.6 62.6

Solonchaks 42,783.6 11.2

Yermosols 7053.1 1.9

Environ Earth Sci (2016) 75:344 Page 17 of 28 344

123

subsurface resulting in low GWP in the zones of 1st, 2nd,

and 3rd order streams of the of the study area. Therefore,

stream order (Fig. 12) was ranked according to its suit-

ability for groundwater recharge, 5th order stream was

classified as very good for groundwater recharge. More-

over, from 1st to 4th stream order were categorized as very

poor, poor, moderate, and good for GWP zone,

respectively.

Geology

Two major geological blocks constitute the geological set-

ting of the Arabian Peninsula: Arabian Shield from the west

that covers about one-third of the total area of the Arabian

Peninsula including the basement complex of the Precam-

brian age and the Arabian platform, which represents a great

basin of sedimentary rocks to the east and ranges from

Paleozoic to Cenozoic (Vaslet et al. 1991). Al-Riyadh pro-

vince has a great thickness of continental and shallowmarine

limestone deposits. The study area is characterized by a

complex geological and structural setting. The geological

formations in the area (Fig. 13) are comprised of seven

geological units, namely: Cenozoic sedimentary rocks,

Mesozoic–Jurassic and Cretaceous, Mesozoic–Triassic,

Plutonic rocks, Precambrian (Archean ? Proterozoic),

Quaternary, and Upper Paleozoic (Dev, Car, Per). The dis-

tribution of these units within the study area is shown in

Table 8. Mesozoic–Jurassic and Cretaceous extend over a

large area in Al-Riyadh province (33.6 % of the total area).

Moreover, this formation was classified as moderate for

GWP zones. While, Mesozoic–Triassic occupied the lowest

area, which about 5.7 % of the total area. This feature was

assigned a very good suitability for GWP zones due to its

high permeability. Cenozoic occupied 10.8 % of the total

area. These rocks categorized as good for GWP zones. Plu-

tonic rocks represent 13.8 % of the total area, and due to its

extremely low porosity, they were assigned very poor suit-

ability for groundwater potential. Precambrian

(Archean ? Proterozoic) basement rocks occupy 15.6 % of

the total area. They are highly weathered and clay rich,

therefore permeability is low and have poor suitability for

GWP zones. Quaternary rocks are themost recent geological

period in Earth’s history, they occupy 12.6 % of the study

area, and due to its high porosity, and these rocks were

Fig. 11 The rainfall surplus

map for the study region

344 Page 18 of 28 Environ Earth Sci (2016) 75:344

123

assigned a good suitability for groundwater potential. Upper

Paleozoic (Dev, Car, Per)which occupy 8 %of the total area,

moreover they have high porosity and permeability. There-

fore, they were considered very good for groundwater

recharge potentiality.

Lithology

Lithological units in the Riyadh province (Fig. 14), range

from the Lower Jurassic to Lower Cretaceous, which out-

crop from upward succession, are Tuwayq Mountain

Limestone, Hanifah Formation, Jubilee Limestone, Arab

Formation, and the Hith Formation with a total thickness of

650 m. The Lithological formations in the area are com-

prised of seven Lithological units. The Riyadh plateau is

largely a sedimentary plateau, composed of silt and asso-

ciated fine sediments, including caliche-like and gypsifer-

ous deposits in untrained depressions, covered with alluvial

deposits. Alluvial deposits (11.1 % of the total area) are

ubiquitous unconsolidated Quaternary alluvia, predomi-

nantly of Holocene age, mostly siliciclastic sediments

found in fluvial and coastal plains and qualified as fluvisols

and gley soils in the plains by FAO/UNESCO (1975,

1986). Because of their high infiltration rate, these areas are

characterized by excellent suitability for groundwater

potential. Dunes or shifting sand (1.4 % of the total area),

correspond to late Pleistocene and Holocene dunes, and

were very limited in the study area. They have very little

chemical weathering and river transport in addition to their

high infiltration rate, these areas were categorized as very

good potential zones. Acid plutonic rocks (42,245.4 km2,

13.8 % of the total area), they are the igneous rock type and

they have fine-grained texture with poor permeability.

Which makes them very poor zones for groundwater

potentiality. Precambrian basement occupy (59,391.6 km2)

15.6 % of the total area, particularly, have poor to mod-

erate permeability, which revealed why most wells in the

area usually have low yields and water quality. In addition,

Groundwater quality can sometimes be poor due to ele-

vated fluoride concentration. Moreover, moderately per-

meable unconfined aquifer might be prone to

contamination (Mogaji and Olayanju 2011). In contrast,

Carbonate rocks have a large share (127,928.7 km2,

33.6 % of the total area) (Table 9), in addition to their

Fig. 12 Geomorphology map

(stream order) for the study area

Environ Earth Sci (2016) 75:344 Page 19 of 28 344

123

good porosity and permeability due to their porosity and

pore throat size. While, the Mixed sedimentary consoli-

dated rocks occupy (62,741.6 km2) 16.5 % of the total

area, and have a significant amount of porosity, high per-

meability which put them in the excellent category for

GWP zones. Whilst, the Siliciclastic sedimentary consoli-

dated rocks tends to have relatively moderate to

low porosity, they occupy 8 % of the total area. Therefore,

they were categorized as moderate recharge zones.

Development of a GIS-based suitability model using

AHP-DSS

All the processing involved in generating a GWP zone map

was implemented in a suitability model developed in the

model builder of ArcGIS 10.2. The suitability model gen-

erates suitability maps for GWP zone based on the inte-

gration of various thematic maps such as soil type, Land

cover and land use, slope, lithology, rainfall, geological

structure, geomorphology, lineament density, and drainage

density using a Weighted Overlay Process (WOP), using

both vector and raster databases. With a weighted linear

combination, criteria were combined by applying a weight

to each factor, followed by a summation of the results to

yield a suitability map. This was undertaken using the

weight module of Idrisi software used for this calculation

and the final weight presented in Table 10.

Table 8 Areas covered by the different geology features

Geology features Area (km2) % of total area

Cenozoic 41,067.7 10.8

Mesozoic–Jurassic and Cretaceous 127,928.9 33.6

Mesozoic–Triassic 21,673.5 5.7

Plutonic rocks 52,418.1 13.8

Precambrian

(Archean ? Proterozoic)

59,391.7 15.6

Quaternary 47,760.6 12.6

Upper paleozoic (Dev, Car, Per) 30,257.4 8.0

Fig. 13 Geology of the study

area

344 Page 20 of 28 Environ Earth Sci (2016) 75:344

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Groundwater potential zones by AHP-DSS

approach

Based on an AHP analysis taking into account nine the-

matic layers, the spatial extents of the GWP zone were

identified using MCE. Different spatial analysis tools were

used in the model to solve spatial problems in the process

of identifying potential areas. The suitability model

generated a GWP map with five suitability classes, i.e.

excellent, very good, good, poor, and very poor. According

to their means (Table 11), 1.47 % (5608.5 km2) and

4.15 % (15,787.3 km2) of the study area was classified as

excellent and very good, respectively. While 12.59 %

(47,911.1 km2), 74.82 % (284,670.9 km2) and 6.97 %

(26,519.9 km2) of the area were classified as good, poor

and very poor, respectively.

Table 9 Areas covered by the

different lithology featuresLithology features Area (km2) % of total area

Alluvial deposits 42,245.4 11.1

Dunes or shifting sand 5515.1 1.4

Acid plutonic rocks 52,418.0 13.8

Precambrian basement 59,391.6 15.6

Carbonate rocks 127,928.7 33.6

Mixed sedimentary consolidated rocks 62,741.6 16.5

Siliciclastic sedimentary consolidated rocks 30,257.3 8.0

Fig. 14 Lithology of the study

area

Environ Earth Sci (2016) 75:344 Page 21 of 28 344

123

The majority of the areas with excellent to very good

suitability had slopes between 0 and 3 % and were in

intensively cultivated areas. The major soil type in the

excellent to very good zones was Arenosols and Lithosols.

The rainfall ranged from 125 to 226 mm/year. While, the

main lithological structures are alluvial deposits, carbonate

rocks, and mixed sedimentary consolidated rocks. These

areas are mainly located in the carbonate-sulfate forma-

tions, which extend from the Lower Jurassic to the Lower

Cretaceous and the sandstone aquifers. Moreover, they

have lineament density ranged from 4.75 to 8.34 km/km2,

and around and within the 5th and 4th stream order. While,

drainage density ranged from 1 to 1.83 km/km2.

Accuracy of AHP-DSS approach

To verify the accuracy of AHP-DSS approach, well loca-

tions were checked against the constructed GWP zones

map. These validation results showed the database and

methodology used for developing the suitability model for

potential groundwater zones, including the suitability

levels of the criteria and the criteria’s relative importance

weights, to have yielded accurate results. Validation of the

technique employed depends on comparing existing

groundwater well locations with the suitability map gen-

erated using the proximity analysis tools of ArcGIS 10.2.

Most exiting wells were within the excellent to very good

areas. Moreover, the AHP-DSS approach was verified with

field survey and is found to be in very good agreement.

Groundwater potential zones by fuzzy logic based

model

The theory of fuzzy sets deals with a subset A of the

universe of discourse X, where the transition between full

membership and no membership is more gradual rather

than abrupt. In the present, each class of all the thematic

maps is assigned with individual fuzzy set values within

the range of 0–1 according to their relative importance in

the prediction of groundwater occurrence. The maps are

then integrated through fuzzy operation to model the

GWP zone of the study area. The GWP zones suitability

map using fuzzy model is presented in Fig. 16. The

spatial distribution of the GWP zones based on the fuzzy

logic model showed that ‘excellent’ suitable areas for

GWP were concentrated in the main wadi channels within

the central, northeastern and southeastern parts of the

study area. Statistics for the suitability of the study area

for GWP are presented in Table 12. Integrating thematic

layers using fuzzy logics indicates that 2.8 %

(10,739 km2), 8.8 % (33,587 km2), 7.9 % (30,106 km2),

60 % (228,361 km2), and 20.4 % (77,705 km2) of the

total area is considered as excellent, very good, good,

poor, and very poor suitable areas for groundwater

potential, respectively.

Accuracy of fuzzy logic based model

To verify the accuracy of fuzzy logic based model, well

locations were checked against the constructed GWP zones

map. The result shows that 86 % of the existing wells are

within excellent to good GWP zones. In addition, 14 % of

wells were within very poor to poor GWP zones according

to the fuzzy logic model. This may be due to the use of

fuzzy algebraic product operator, which would be an

appropriate combination operator, because at each location

the combined fuzzy membership values tend to be very

small with this operator, due to the effect of multiplying

Table 10 Weight (percent of influence)

No. Criteria Weight Weight %

1 Lineaments density 0.072 7.207

2 Slope 0.096 9.576

3 Drainage density 0.066 6.639

4 Land use/cover 0.057 5.682

5 Soil 0.084 8.423

6 Rainfall 0.106 10.617

7 Geomorphology 0.228 22.780

8 Geology 0.174 17.435

9 Lithology 0.116 11.643

Table 11 Areas under different suitability classes using AHP-DSS

approach

S. no Class Area (km2) Percent of total area

1 Very poor 26,519.9 6.97

2 Poor 284,670.9 74.82

3 Good 47,911.1 12.59

4 Very good 15,787.3 4.15

5 Excellent 5608.5 1.47

Table 12 Areas under different suitability classes using fuzzy logic

model

S. no Class Area (km2) Percent of total area

1 Very poor 77,705 20.4

2 Poor 228,361 60.0

3 Good 30,106 7.9

4 Very good 33,587 8.8

5 Excellent 10,739 2.8

344 Page 22 of 28 Environ Earth Sci (2016) 75:344

123

several numbers less than 1. Moreover, the fuzzy logic

model was verified with field survey and is found to be in

good agreement.

Discussion and conclusion

Groundwater is a vital natural resource for the reliable and

economic provision of potable water supply in both urban

and rural environments. Hence, it plays a fundamental role

in aquatic and terrestrial ecosystems. The identification of

potential groundwater zones in the central region of Saudi

Arabia was considered as a multi-objective and multi-cri-

teria problem. The groundwater conditions at any given

area can vary greatly according to various factors that

influence the occurrence and replenishment of groundwa-

ter. The main difference between the present study and

previous studies (e.g. Gupta and Srivastava 2010, Dar et al.

2011, Nag and Ghosh 2013) is that this study used nine

factors for mapping GWP zones. These factors are

expressed in terms of nine thematic layers, namely: soil

type, Land cover and land use (derived from available RS

data), slope (i.e., topography), lithology, rainfall, geologi-

cal structure, geomorphology, lineament density, drainage

density. However, these factors have rarely been studied

together because of the non-availability of data. Hence, a

systematic study of these factors lead to a better delineation

of the prospective zones in an area, which is then followed

up on the ground through detailed hydrogeological and

geophysical investigations. The spatial extents of GWP

areas were identified by AHP-DSS and fuzzy logic based

model that considered the nine layers. The suitability

model generated a GWP map with five suitability classes,

i.e. Excellent, Very good, Good, Poor, and Very poor.

The spatial distribution of the GWP zones developed by

AHP-DSS (Fig. 15) showed that ‘excellent’ suitable areas

for GWP were concentrated in the main wadi channels

within the central, northeastern and southeastern parts of

the study area. Similarly, in the fuzzy model, ‘excellent’

suitable areas for GWP were concentrated in the main wadi

channels within the central, northeastern and southeastern

parts of the study area (Fig. 16). This finding agrees with

Fig. 15 Groundwater potential

zone map for the study area

‘‘AHP-DSS approach’’

Environ Earth Sci (2016) 75:344 Page 23 of 28 344

123

Shaheen (1973), who reported that Riyadh water supply

comes from the shallow aquifers in the wadi channels, the

karst aquifers of the carbonate-sulfate formations, which

extend from the Lower Jurassic to the Lower Cretaceous

and the sandstone aquifers of Minjur and Wasia forma-

tions. The majority of the areas with excellent to very good

suitability had slopes between 0 and 3 % and were in

intensively cultivated areas. The major soil type in the

excellent to very good zones was Arenosols and Lithosols.

The rainfall ranged from 125 to 226 mm/year. While, the

main lithological structures are alluvial deposits, carbonate

rocks, and mixed sedimentary consolidated rocks. These

areas are mainly located in the carbonate-sulfate forma-

tions, which extend from the Lower Jurassic to the Lower

Cretaceous and the sandstone aquifers. Moreover, they

have lineament density ranged from 4.75 to 8.34 km/km2,

and around and within the fifth and fourth stream order.

While, drainage density ranged from 1 to 1.83 km/km2.

High lineament density is observed in the central parts of

the study area with a value ranging from 3.31 to 8.34 km/

km2. Furthermore, the study revealed that lineament

density closely related to groundwater occurrence and yield

and is essential to groundwater surveys, development, and

management. Similarly, several researchers have reported a

close relationship between lineaments and groundwater

flow and yield (Haridas et al. 1994, Magowe and Carr

1999; Fernandes and Rudolph 2001; Mabee et al. 2002;

Solomon and Quiel 2006; Neves and Morales 2007).

According to AHP-DSS approach, 1.47 % (5608.5 km2)

and 4.15 % (15,787.3 km2) of the study area was classified

as excellent and very good, respectively. Moreover,

12.59 % (47,911.1 km2), 74.82 % (284,670.9 km2) and

6.97 % (26,519.9 km2) of the area were classified as good,

poor and very poor, respectively. On the other hand, the

fuzzy model indicates that 2.8 % (10,739 km2), 8.8 %

(33,587 km2), 7.9 % (30,106 km2), 60 % (228,361 km2),

and 20.4 % (77,705 km2) of the total area is considered as

excellent, very good, good, poor, and very poor suit-

able areas for groundwater potential, respectively.

The validation process of the two models revealed that

the GWP map developed by AHP-DSS approach is more

accurate than the suitability map developed by fuzzy

Fig. 16 Groundwater potential

zone map for the study area

‘‘Fuzzy logic based spatial

model’’

344 Page 24 of 28 Environ Earth Sci (2016) 75:344

123

model. In addition, the AHP-DSS approach was verified

with field survey and is found to be in very good agree-

ment. Where, almost all existing wells were within the

excellent to very good areas in the suitability map devel-

oped by AHP-DSS. While, validation of the suitability map

developed by fuzzy model reveals that 86 % of the existing

wells are within excellent to good potential zones. In

addition, 14 % of wells were within very poor to poor

potential zones according to the fuzzy logic model. This

may be due to the use of fuzzy algebraic product operator,

which would be an appropriate combination operator,

because at each location the combined fuzzy membership

values tend to be very small with this operator, due to the

effect of multiplying several numbers less than 1. More-

over, the fuzzy logic model was verified with field survey

and is found to be in good agreement.

According to field validation, RS- GIS, AHP and fuzzy

logic have been shown to be powerful for mapping

potential groundwater zones. The main advantages of

using these techniques for groundwater exploration are (1)

the reduction of cost and time needed (Murthy 2000;

Leblanc et al. 2003; Tweed et al. 2007). (2) The fast

extraction of information on the occurrence of groundwater

to establish the baseline information for GWP zones (Ti-

wari and Rai 1996; Das et al. 1997; Thomas et al. 1999;

Harinarayana et al. 2000; Muralidhar et al. 2000; Chowd-

hury et al. 2010), and (iii) the selection of promising areas

for further groundwater exploration. In addition, it is

widely used to characterize the earth’s surface (such as

lineaments, drainage patterns, and lithology) as well as to

examine the groundwater recharge zones (Sener et al.

2005). However, the technique used for developing weights

from rating needs to be sensitive to the problems of

inconsistency and error. This is the main drawback of the

AHP technique (Nefeslioglu et al. 2013) and hence weights

need to be assigned carefully. Rank reversal fact should be

considered carefully during the application. It defines the

changes of the order of the judgment alternatives when a

new judgment alternative is added to the problem. If the

value of the consistency ratio (CR) is less than or equal to

0.1, the inconsistency is acceptable (Saaty 1977), and if the

consistency ratio CR is equal to 0.00, it means the judg-

ment of the pairwise comparison matrix is perfectly con-

sistent. If the CR is greater than 0.1, we need to go back to

the step pairwise comparison matrix to rank the judgment

value carefully with regard to the dominant factor that

influences groundwater occurrences in the overall thematic

layer map. While, the main drawbacks of the fuzzy system

approach is that it can be substantially sophisticated in

application. The complexity of the system can increase

exponentially with an increase in the numbers of the

variables in the system, which can also mean a drastic

increase in the numbers of the membership functions, and

after a certain complexity, the system may become

unsolvable or not executable (Nefeslioglu et al. 2013).

Furthermore, the reliability of groundwater potentiality

mapping was verified by using existing well data. The

groundwater conditions in the study area can vary greatly

according to various factors that influence the occurrence

and replenishment of groundwater. These factors are

expressed in terms of nine thematic layers, namely: soil

type, Land cover and land use (derived from available RS

data), slope (i.e., topography), lithology, rainfall, geologi-

cal structure, geomorphology, lineament density, drainage

density.

Overall, the proposed method should be of great help to

hydrologists and can be applied in arid and semi-arid

regions. The developed AHP-DSS model in this study

could be used in any other area for groundwater

prospecting with proper modification. The databases and

the results obtained can also be used to develop conceptual

models for similar arid regions. Validation of the AHP-

DSS technique employed depends on comparing existing

groundwater well locations with the suitability map gen-

erated using the proximity analysis tools of ArcGIS 10.2.

Most exiting wells were within the excellent to very good

areas. This study will help decision makers and water

resources planners in Riyadh province, Saudi Arabia in

proper development and utilization of both groundwater

and surface water resources for eliminating water scarcity

and thereby improving the irrigation practices.

Acknowledgments This project was financially supported by King

Saud University, Deanship of Scientific Research, College of Food

and Agricultural Sciences Research Centre.

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