INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
www.rsisinternational.org
Page 4398
Analyzing the Efficacy: A Comparative Study Between the
Conventional AHP Model and Fuzzy- AHP Model for Groundwater
Potentiality Prediction in Basement Terrain Using Geophysical Data
Sets
Raheem Bodunde Salau*, Kehinde Anthony Mogaji
Department of Applied Geophysics, Federal University of Technology, Akure
*Corresponding Author
DOI: https://doi.org/10.51244/IJRSI.2025.120800399
Received: 16 September 2025; Accepted: 24 September 2025; Published: 18 October 2025
ABSTRACT
This study integrates geophysical, geological, and remote sensing techniques to evaluate groundwater potential
in the basement complex terrain of southwestern Nigeria, an area where sustainable groundwater development
remains a critical challenge. To produce a comprehensive groundwater potential map, eight thematic layers
known to influence groundwater occurrence and movement were derived from the available datasets. These
include lithology, slope, recharge rate, lineament density, aquifer transmissivity, hydraulic conductivity,
overburden thickness, and aquifer resistivity. Each parameter was carefully analyzed and weighted to reflect its
relative significance in groundwater occurrence. The mapping process employed both the Analytical Hierarchy
Process (AHP) and its advanced fuzzy-based extension (FAHP) to compare the performance of conventional
and modified multi-criteria decision-making techniques. The integrated analysis delineated the study area into
five distinct groundwater potential zones, namely very high, high, moderate, low, and very low. These classes
provided a spatial framework for understanding the variability of groundwater occurrence across the region.
Validation of the models was carried out using the Receiver Operating Characteristics (ROC) curve approach.
The FAHP-based groundwater potential model achieved a prediction accuracy of 81%, demonstrating a
marked improvement over the conventional AHP model, which yielded 73%. Additional qualitative validation
was conducted by correlating the FAHP-generated groundwater potential zones with the geological and
hydrogeological attributes of the study area. The comparison revealed a high level of agreement of
approximately 90%, confirming the robustness of the FAHP approach in capturing actual field conditions.
Overall, the findings highlight the effectiveness of integrating FAHP with geophysical, geological, and remote
sensing datasets for reliable groundwater potential assessment in basement terrains.
Keywords: Groundwater potential, Geophysics, Remote sensing and GIS, AHP, FAHP
INTRODUCTION
Groundwater refers to water stored within subsurface aquifers, occupying pore spaces in rocks. It serves as a
dependable source of water, particularly in remote areas where the development of surface water is limited
(Adeyemo et al., 2017). However, increasing population growth and rapid urbanization have placed significant
pressure on groundwater resources, posing challenges to their sustainable management.
In basement terrains, groundwater exploration typically targets aquifers within the weathered overburden or
fractured crystalline rocks, especially those of Precambrian origin (Omosuyi et al., 2003). These crystalline
rocks often contain fractures and fault zones formed by past tectonic activities. The identification and mapping
of such hydrogeologic features are crucial for delineating groundwater-bearing zones in basement settings
(Omosuyi, 2010). While fractured crystalline bedrocks can yield potable water, achieving high-yielding wells
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
www.rsisinternational.org
Page 4399
remains difficult due to the heterogeneity of fracture systems across regional scales. Groundwater occurrence
in these formations is largely controlled by secondary porosity and permeability generated through weathering
and fracturing. In hard rock environments, the subsurface profile generally consists of fresh bedrock overlain
by an overburden or regolith, which is further divided into aeration and saturation zones separated by the water
table (Omosuyi et al., 2012).
Effective groundwater management requires the application of remote sensing and geophysical methods to
evaluate groundwater potential at both local and regional scales. As noted by Jyrkama and Sykes (2007) and
Kaliraj et al. (2013), understanding groundwater recharge processes is fundamental to sustainable groundwater
use. In Nigeria, groundwater remains a critical resource for domestic, agricultural, and industrial purposes,
making its evaluation and sustainable exploitation essential (Ouedraogo et al., 2016; Yousefi et al., 2018).
With the pressures of population growth, urban expansion, and climate change, groundwater resource
management has become indispensable for ensuring both the quantity and quality of supply, especially in
urban and semi-urban regions (Ouedraogo et al., 2016).
This study highlights the significance of geophysics in evaluating groundwater potential. Groundwater is
located within cracks and pore spaces in subsurface, stored and flowing gradually through geologic formations
like sediment, sand and rocks known as aquifer (Alabi et al., 2010). These geologic formations are precisely
mapped or delineated using geophysical techniques for potential groundwater development. Among the
various geophysical methods utilized in groundwater hydrology, the Electrical Resistivity Method (ERM)
stands out to be most favoured approach (Alabi et al. 2010). Some of the techniques in ERM is the Electrical
Resistivity Tomography (ERT) are the 3-D ERT, 2-D ERT and 1-d ERT to name a few. For the purpose of this
research and based on availability, preference will be given to 1-D Electrical Resistivity Tomography (ERT)
over other methods. 1-D ERT can also referred to as Vertical Electrical Sounding (VES). VES is commonly
used due to its efficiency in mapping potential groundwater zones in comparison to other geophysical
techniques (Oyedele et al., 2013). VES measurements are valuable in groundwater studies as they do not
disturb the soil structure or dynamics (Adiat et al., 2009; Ariyo and Adeyemi, 2009). Various field
configurations (arrays) are used to achieve VES techniques. There are roughly one hundred independent geo-
electric arrays (Szalai and Szarka, 2008), but the Schlumberger array is set up to be more suitable and common
in groundwater delineation.
Among the various geophysical techniques, the magnetic method is considered one of the most adaptable since
it can be utilized for investigating both shallow and deep subsurface features (Dobrin and Savit, 1988). In
groundwater exploration, it has gained prominence as an effective approach for detecting structural features
such as faults, joints, and fracture zones, which often act as conduits for groundwater accumulation and storage
(Al-Gharni, 2005; Abdulkareem et al., 2018; Oni et al., 2020). Recent improvements in magnetic survey
acquisition and processing have further enhanced its application, particularly when integrated with Geographic
Information Systems (GIS) for better interpretation (Oni et al., 2020). In this study, derivative aeromagnetic
map (Lineament density) will be incorporated as thematic layers alongside other datasets to improve the
delineation of groundwater potential zones.
The employment of Geographic Information System (GIS) and Remote Sensing (RS) techniques in the field of
groundwater hydrology has proved excellent in the decision making process (Rahmati and Meselle, 2015
Rahmati et al., 2015; Manap et al., 2014). This is adduced to the fact that there is easy and quick access
obtainable from satellite data base archive (Zare et al., 2013). For the driver of the proposed models in this
study, the efficacy of geospatial techniques (RS and GIS) will be employed.
Various statistical decision-making models have been applied to interpret Vertical Electrical Sounding (VES)
data in groundwater studies. Among these, Multi-Criteria Decision Analysis (MCDA) methods have gained
prominence for assigning weights to parameters based on prior research (Chowdhury et al., 2009; Adiat et al.,
2012; Mogaji & Lim, 2016). The Analytical Hierarchy Process (AHP) is one of the most widely used MCDA
techniques, offering a structured framework for complex decision analysis through pairwise comparisons and
expert judgment (Saaty, 1987). Despite potential subjectivity and inconsistency, AHP has been effectively
employed in groundwater hydrology (Mogaji et al., 2017; Akinlalu et al., 2017).
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
www.rsisinternational.org
Page 4400
FAHP method systematically solves the selection problem that uses the concepts of fuzzy set theory and
hierarchical structure analysis. Basically, FAHP method represents the elaboration of a standard AHP method
into fuzzy domain by using Fuzzy numbers for calculating instead of real numbers.
In order to have a robust research, the FAHP based model will be applied in modeling the groundwater
potentiality of the study area. The FAHP model will be used to integrate the derived parameters from the
surface and subsurface source (lineament, lithology, slope, aquifer transmissivity, hydraulic conductivity,
overburden thickness, aquifer resistivity and recharge rate) with the view of obtaining potentiality index which
will help in categorizing the groundwater condition of potentiality the study area.
Methodology, study area and data used
METHODOLOGY
As indicated in Fig. 1, the study was carried out in four stages, employing geology, remote sensing, and
geophysical data. The first stage required collecting, processing, and interpreting remote sensing, geology and
geophysical characteristics for the groundwater potential evaluation. Following that, thematic maps of the
conditioning factors were created in ArcGIS, and uniformly spaced fishnet points were placed to extract pixel
values at those points. The third stage involved the use of FAHP and AHP for the development of groundwater
potential index which was synthesized in GIS environment to produce the groundwater potential model maps
of the study area. Finally, the models produced were validated using Receiver Operating Characteristics (ROC)
to determine the efficacy of FAHP and AHP models in groundwater potential prediction.
Fig. 1: The Flowchart for the Study Area
Description Of The Study Area
The study area is located at the southwestern part of Ado Ekiti, Ekiti State, Nigeria, as shown in Fig. 2. It falls
within geographic grids extending between 5°10´0´´ to 5°13´0´´ (Eastings) and 7°36´0´´ to 7°38´30´´
(Northings). Ado-Ekiti is bounded by Ilawe at the West, Gbonyin at the east, Ikere at the North and Iyin at the
South as shown in Fig. 2. The total area of the study area is about 6.6 square-km. The study area has the
surface elevation ranging from 415 to 536 m above sea level. The western part of the study area is exhibited
with high elevation while northern part exhibited low elevation.
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
www.rsisinternational.org
Page 4401
Ado Ekiti enjoys a tropical climate with two distinct seasons. These are the rainy season (April – October) and
the dry season (November – March). Temperature ranges between 21
o
C and 30
o
C with high humidity. The
guinea savanna predominates in the study area. The study area is underlain by the Precambrian basement
complex of South-western, Nigeria (Rahaman, 1988). The lithological/rock units recognized in the area
include Charnokite, Migmatite–gneiss and Quartzite. More than 45% of the area is underlain by Quatzite. Fig.
3 shows the geology map of the area.
Fig. 2: Base Map of the Study Area
Fig. 3: Geologic Map of the Study Area
DATA USED
Lithology
The lithology map used in the research was derived from geological survey conducted in the study area. An
important hydrologic factor that affects the groundwater quantity in a specific region is lithology. In areas with
hard rock terrain, the underlying rocks are often brittle and prone to fracturing, leading to increased water flow,
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
www.rsisinternational.org
Page 4402
accumulation, porosity, and permeability in the weathered and fractured basement of the rock units. The
Euclidean function available on the spatial analyst of the ArcGIS software was used to produce the proximity
maps of the lithologic units using a buffer of 50 m. The weight, calculated using the FAHP was multiplied with
class scores of the proximity map of the lithologic units. The weighted proximity map was subsequently
integrated using fuzzy sum operator. The resulting map was thereafter fuzzified using the fuzzy membership
function large to produce the fuzzified lithologic map of the study area. This tool converts the crisp lithology
map into a fuzzy lithology map by selecting the lithology class with the highest degree of membership at each
pixel. Fig. 4 shows the fuzzified lithologic map of the study area.
Remote Sensing
A Digital Elevation Model (DEM) was downloaded from the United States Geological Survey USGS through
(https://asterweb.jpl. nasa.gov/gdem.asp, last access: 20 November 2019) with a spatial resolution of 15 m.
The Digital Elevation Model (DEM) data was imported into the ArcGIS environment (ArcGIS 10.3). The
ArcTool box on the ArcGIS 10.3 has various spatial tools for producing various groundwater conditioning
factors from the DEM. In this research work, the slope was extracted from the DEM and the lineament was
extracted from the Landsat Imagery.
Slope Degree
The slope degree indicates the extent of surface runoff, which varies across different locations. This gradient
significantly impacts potentiality, areas with lower slope degrees experience reduced runoff and enhance water
infiltration, whereas it is vice versa for areas with high slope degree (Ouedraogo et al., 2016). This factor will
be derived from the ASTER digital elevation model (DEM) data of the study area using the slope analysis tool
in ArcGIC 10.3 software package.
Lineament Density
In this study, remote sensing was utilized to extract lineaments from LANDSAT 8 imagery covering the study
area. These extracted lineaments were then integrated with aeromagnetic lineament data, and both datasets
were superimposed in ArcGIS to produce a composite lineament map. A common approach for analyzing such
features is through the development of lineament density maps (Zakir et al. 1999).Equation (i) expresses the
Ld definition mathematically:
Ld =


(Km
-1
) i
Where ΣLi = total length of all the lineaments (km) and A = area of the grid (km
2
).
Geophysical investigation
Data acquisition and interpretation
The geophysical data in the study area were collected using the electrical resistivity techniques. The
Schlumberger array was utilized to collect 55 vertical electrical soundings (VES) data from 1 to 200 m
utilizing half-electrode spacing (AB/2). This approach takes into account vertical differences in the apparent
resistivity of the ground, which were measured with a fixed centre of the array. The survey was carried out by
increasing the electrode spacing around a fixed centre of the array. Electrodes are positioned in a straight line,
with a pair of potential electrodes placed between two pairs of current electrodes. In this work, the Global
Positioning System (GPS) was utilized to spatially identify VES sites for spatial analysis in a GIS setting. The
apparent resistivity of the VES data is the product of the resistance and the matching geometric factor (G) of
the electrode spacing for each spread length (AB/2). On a log–log graph sheet, these apparent resistivity values
were plotted against the electrode spacing. The VES curves that were generated were displayed and divided
into types. These classifications demonstrate the qualitative character of subsurface lithology. In addition,
quantitative interpretations of the partial curve matching findings, which are the layer thickness and layer
resistivity, were determined. The results were entered into the WinResistTM Software as model parameters
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
www.rsisinternational.org
Page 4403
(Vander-Velper 2004). The theoretical model curve, the primary geoelectric parameters (layer resistivity, layer
thickness), and the depth to the top of each layer provide good insight into the aquifer's subsurface
information, which is vital for groundwater potential research. Fig. 4 and Table 1 show typical curves
depending on underlying geology and a summary table representation of geoelectric characteristics
respectively.
Fig. 4: Typical resistivity model curves obtained in the study area; a. charnockite, b. quartizite series and c.
migmatite gneiss rock unit.
Table 1: Summary of the interpreted results Geoelectric parameters.
VES
Pt
Curve
Layer
Apparent Resistivity
(Ωm)
Depth
(m)
Layer Description
1
AA
1
64
0.5
Top soil
2
37
2.3
Clayey layer
3
534
5.5
Weathered basement
4
748
…..
Fresh Basement
2
HA
1
152
0.6
Top soil
2
72
3.2
Clayey layer
3
432
8.4
Weathered basement
4
719
….
Fresh Basement
b
a
C
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
www.rsisinternational.org
Page 4404
3
A
1
43
1.4
Top soil
2
251
9.6
Weathered basement
3
700
----
Fresh bedrock
4
HA
1
238
0.6
Top soil
2
55
2.5
Clayey layer
3
571
7.0
Weathered basement
4
953
Fresh bedrock
5
HA
1
212
0.4
Top soil
2
56
2.6
Clayey layer
3
567
5.5
Weathered basement
4
863
----
Fresh bedrock
6
KH
1
204
1.0
Top soil
2
453
6.4
Sandy Layer
3
281
11.1
Weathered basement
4
509
……
Fractured basement
7
KH
1
169
0.9
Top soil
2
508
8.5
Sandy Layer
3
167
12.3
Weathered basement
4
570
…..
Fresh basement
8
KH
1
143
0.7
Top soil
2
534
6.5
Sandy Layer
3
257
10.8
Weathered basement
4
503
…..
Fractured basement
9
?
?
?
?
?
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
www.rsisinternational.org
Page 4405
55
KH
1
169
1.1
Top soil
2
1261
8.4
Laterite
3
403
10.9
Weathered basement
4
641
---
Fresh bedrock
The derived secondary geoelectric parameters
The primary geoelectric parameters, layer resistivity and layer thickness, were utilised to determine the
secondary geoelectric parameters, which are important conditioning variables in delineating groundwater
potential zones in the research region. The validation method takes into account hydraulic conductivity (K),
aquifer transmissivity (T), recharge rate (R), aquifer resistivity (AQR) and overburden thickness (OVT). The
primary geoelectric characteristics in Table 1 were analysed using Eqs. ii - iv, to generate the aforementioned
groundwater conditioning factors.
Table 2 displays the values of the calculated K, T, R, AQR and OVT parameters. Hydraulic conductivity, K
(m/day), is given by:
K = 0.0538e
-0.0072ρ
ii
Where, ρ is the resistivity of the aquifer.
Transmissivity, T=K×h iii
Where K is the hydraulic conductivity as shown in equation 3.
Recharge rate, R = 34.41log10 (ρ) + 1.05 (D) + 128.38 iv
Where D is the depth (m) to the aquifer.
Table 2: Summary of interpreted geo-electric parameters.
Ves No
Northing
Easting
TR
K
AQR
R
OVT
1
843014.4
739710
0.00267
0.001151
534
189.4053
5.5
2
843015
739893
0.007675
0.002399
432
202.2536
8.4
3
842738
739803
0.012361
0.008829
251
204.4274
9.6
4
843200
843200
0.002204
0.000882
571
205.4804
6.9
5
842770.9
740171
0.02467
0.009488
241
204.3037
5.5
6
843554.6
743202
0.04553
0.007114
281
221.694
11.1
7
8436615
739615
0.137406
0.016165
167
224.3471
12.2
8
843783
739951
0.054964
0.008456
257
222.2471
10.8
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
www.rsisinternational.org
Page 4406
9
843848
740594
0.076545
0.011776
211
223.5768
10.2
10
844065
740961
0.086678
0.012747
200
219.3848
10.2
11
844060
739950
0.00326
0.002138
448
185.0819
4.3
12
844062
740256
0.033528
0.02794
91
184.7669
2.9
13
843355
740352
0.02992
0.0136
191
195.0103
5.6
14
843171
740383
0.008392
0.001785
473
199.3505
8.2
15
843295
740659
0.008141
0.001661
483
194.46
8.3
16
843574
740994
0.005278
0.001056
546
194.565
9.2
17
843151
742499
0.041911
0.02794
91
192.5655
8.4
18
843205
740996
0.001974
0.000581
629
185.9118
6.8
19
842992
741427
0.009335
0.001638
485
195.6752
9.4
20
843416
740290
0.025895
0.019919
138
165.5793
3
21
843384
739984
0.007605
0.001358
511
196.0562
10.5
22
842376
741092
0.005888
0.004206
354
207.7009
4.3
23
842741
740477
0.001563
0.000489
653
196.2609
6.2
24
842742
740600
0.001321
0.000426
672
194.5813
6.6
25
842775
740998
0.029871
0.018669
147
171.4938
3.7
26
842654
741428
0.03241
0.019077
144
174.1669
4.2
27
842963
741733
0.001518
0.000399
681
200.0249
7.1
28
842435
740632
0.002352
0.001176
531
198.7676
5.4
29
842315
741338
0.001781
0.000775
589
203.1279
6.2
30
842472
741981
0.003177
0.001513
496
199.3301
5.8
31
842718
741980
0.001351
0.000675
608
203.2208
5.3
32
842784
742807
0.001339
0.000638
616
203.5539
5.5
33
842107
742566
0.000747
0.000356
697
203.5539
5.6
34
843545
741332
0.013055
0.010879
222
179.9611
3.3
35
843208
741548
0.001085
0.000329
708
211.1721
6.9
36
843391
741425
0.001164
0.000388
685
211.1139
6.5
37
843485
741731
0.001203
0.000388
685
214.4147
7.4
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
www.rsisinternational.org
Page 4407
38
843485
741731
0.001787
0.000577
630
208.516
6.6
39
843364
742099
0.002105
0.000726
598
209.5591
6.5
40
843155
743357
0.026745
0.003222
391
236.0358
11.9
41
843492
743018
0.055988
0.006912
285
236.545
12.5
42
843458
742497
0.148678
0.022191
123
224.3159
12.7
43
844441
742308
0.087321
0.012655
201
226.4954
11.1
44
844376
741421
0.110649
0.018139
151
221.3562
12.3
45
843916
741790
0.12449
0.0211
130
218.3118
10.8
46
844378
742063
0.024873
0.003316
387
209.4487
11.1
47
844903
742521
0.005312
0.000435
669
243.5218
14.3
48
844535
742707
0.04185
0.004267
352
236.97
13.3
49
844228
742800
0.037767
0.004392
348
237.1551
12.7
50
845058
742827
0.018811
0.002138
448
237.0794
12.1
51
844875
743011
0.061076
0.006863
286
236.606
13.9
52
844507
743228
0.138785
0.015594
172
225.1593
17.2
53
844139
743322
0.118125
0.013897
188
224.7177
16.5
54
843710
743692
0.043847
0.00522
324
236.0309
16.7
55
843404
743908
0.024826
0.002956
403
235.4167
11
Groundwater potentiality conditioning factors
Groundwater potentiality conditioning factors and production of their thematic layers in the GIS environment.
The groundwater potential of the research was evaluated using eight (8) factors: lithology, hydraulic
conductivity, lineament, aquifer transmissivity, recharge rate and slope. Thematic maps of these factors were
generated using Arc- GIS 10.3's inverse distance weighting (IDW) approach, and data from Table 2 were
utilized to develop the geo-electrically linked thematic layers; hydraulic conductivity, overburden thickness,
aquifer transmissivity, aquifer resistivity and recharge rate are displayed in Figs. 8, 9, 10, 11, and 12,
respectively.
The groundwater potential conditioning factors (GPCFs) thematic maps shown in Figs. 5, 6, 8, 9, 10, 11 and
12. These factors were used as decision making to create AHP the FUZZY AHP groundwater potentiality
model of the research region. The fishnet point map was created for ease of computation to ensuring uniformly
dispersed fishnet points (Fig. 13) over the study area.
MODELS REVIEW
The Analytical Hierarchy Process
Thematic map weighting was performed using the Analytic Hierarchy Process (AHP), a widely applied GIS-
based multi-criteria decision analysis (MCDA) technique for delineating groundwater potential zones
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
www.rsisinternational.org
Page 4408
(Arulbalaji et al., 2016). Eight factors were considered in this study: lithology, slope, recharge rate, lineament,
aquifer transmissivity, hydraulic conductivity, overburden thickness, and aquifer resistivity. These parameters
directly influence groundwater storage and movement in the area. AHP, introduced by Saaty (1987), remains
the most widely adopted MCDA approach, with successful applications reported in geological and
groundwater investigations (Adiat, 2013; Fashae et al., 2014; Mogaji et al., 2014).
Mathematical Model of AHP
If there are n elements which are compared, the comparison results create matrix form A with dimension n x
m.
v
The elements of matrix, or ratio between compared criteria are expressed by the formula:
ij
=


vi
Considering the first axiom for reciprocal we have:
a
ij
=

vii
The next step is to obtain a normalized matrix B = [b
ij
]. The elements of the matrix B are calculated
as:
viii
The calculation of the weights i.e. eigenvector w = [w
i
] form the normalized matrix B is performed by
calculating the arithmetic mean for each row of the matrix according to the formula:
ix
Fuzzy–Analytical Hierarchical Process (FAHP)
Despite its wide range of applications, the conventional AHP approach may not fully reflect a style of human
thinking. One reason is that decision makers usually feel more confident to give interval judgments rather than
expressing their judgments in the form of single numeric values. As a result, FAHP and its extensions are
developed to solve alternative selection and justification problems. The FAHP is a popular technique which
has been applied for MCDM problems (Abedi et al., 2013; Wu et al., 2013). This method was proposed by
Van Laarhoven and Pedrycs (1983). In the fuzzy extension of AHP, the weights of the nine-level fundamental
scales of judgments are expressed via the triangular fuzzy numbers (Table 3) in order to represent the relative
importance among the hierarchy criteria (Karimi et al., 2011 ).
Weighting of the thematic maps was carried out using the Fuzzy Analytical Hierarchical Process (FAHP). This
method helps in integrating all the eight (8) different thematic factors for this research; the thematic factors
include lithology, slope, recharge rate, lineament, aquifer transmissivity, hydraulic conductivity, overburden
thickness and aquifer resistivity. The eight thematic factors influence the movement and storage of water in the
area. Thus equation (xi) is used to weight the importance of each criteria.
The weight vector is then given by:
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
www.rsisinternational.org
Page 4409
󰇡
󰇛
󰇜

󰇛
󰇜
󰇢
x
Where Bi(i=1,…,m) has m elements.
Calculate normalized weights
Via normalization, the normalized weight vectors are:
󰇛
󰇜

󰇛
󰇜
xi
Where W is a non-fuzzy number. As pointed out by Wang et al., (2008).
Table 3: Fuzzy linguistic scale (Radionovs and Užga-Rebrovs, 2016)
Linguistic scale
Triangular fuzzy scale
Reciprocal
Just equal
1, 1, 1
1, 1, 1
Equally important
1/2, 1, 3/2
2/3, 1, 2
Weakly more important
1, 3/2, 2
1/2, 2/3, 1
Strongly more important
3/2, 2, 5/2
2/5, 1/2, 2/3
Very strongly more important
2, 5/2, 3
1/3, 2/5, ½
absolutely more important
5/2, 3, 7/2
2/7, 1/3, 2/5
Table 4: Ratings, groundwater storage potential type- classification, AHP and FAHP weight of the GPCF
produced thematic layers.
Lithology
High
Quatzite
0.2623
0.19655
Medium
Charnockite
Low
Migmatite Gneiss
Aquifer
Resistivity
Very High
91 - 258Ωm
0.1958
0.16501
High
258 - 345Ωm
Medium
345- 441Ωm
Low
441- 543Ωm
Very Low
543 - 708Ωm
Lineament
Very High
0.0063- 0.0084
0.1566
0.14362
High
0.0046- 0.0063
Medium
0.0029- 0.0046
Low
0.0009 – 0.0029
Very Low
0- 0.00099
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
www.rsisinternational.org
Page 4410
Hydraulic
Conductivity
Very High
0.0164- 0.0279m/day
0.1203
0.12453
High
0.0112- 0.0164m/day
Medium
0.0076- 0.0112m/day
Low
0.0044- 0.0076m/day
Very Low
0.0003- 0.0044m/day
Overburden
Thickness
Very High
13.5- 17.2m
0.0884
0.11445
High
10.5- 13.5m
Medium
8.4- 10.5m
Low
6.4- 8.4m
Very Low
2.9- 6.4m
Recharge Rate
Very High
223.96- 243.52Ltr/d
0.0684
0.10218
High
210.52- 223.96Ltr/day
Medium
200.43- 210.52Ltr/day
Low
191.57- 200.43Ltr/day
Very Low
165.57- 191.57Ltr/day
Aquifer
Transmissivity
Very High
0.0889- 0.1487m
2
/day
0.0633
0.09103
High
0.0610- 0.0889m
2
/day
Medium
0.0396- 0.0610m
2
/day
Low
0.0193- 0.0396m
2
/day
Very Low
0.0007- 0.0193m
2
/day
Slope
Very High
0- 2.79
o
0.0335
0.06260
High
2.79- 5.29
o
Medium
5.29- 8.56
o
Low
8.56- 13.57
o
Very Low
13.57- 24.54
o
RESULTS AND DISCUSSION
Discussion of groundwater potential conditioning factors
Lithology
Lithology plays a crucial role in groundwater accumulation, influencing both its quality and quantity. The
study area comprises three major rock units: migmatite gneiss, charnockite, and quartzite schist (Fig. 3).
Among them, quartzite exhibits the highest degree of fracturing, whereas charnockite shows the least. The
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
www.rsisinternational.org
Page 4411
extent of rock weathering significantly affects groundwater potential, rocks with higher weathering capacity
generally yield greater groundwater potential. According to the fuzzified lithologic map (Fig. 5), quartzite-
dominated zones are associated with high weightage values, migmatite gneiss with medium weightage, and
charnockite with medium to low weightage value. Quartzite occupies about 42% of the study area, while
migmatite gneiss and charnockite cover the remaining portions.
Fig. 5: Fuzzified lithologic map of the study area
Slope Degree
Haujie et al. (2016) reported that groundwater flow is primarily governed by surface forces, with terrain
boundaries often coinciding with shallow aquifer limits. Fig. 6 illustrates the slope map generated from remote
sensing data and classified into five categories using ArcGIS. Very low (0°–2.79°) and low slopes (2.79°–
5.29°) dominate the north-central, central, southern, south-western, and parts of the north-eastern and eastern
sectors. Moderate slopes (5.29°–8.56°) occur in the northern, central, eastern, and north-western zones, while
high to very high slopes (8.56°–24.54°) are concentrated in the north, north-east, east, and localized pockets of
the central and southern regions. Areas with low slope gradients constitute potential groundwater accumulation
zones.
Fig. 6 : Slope Degree map of the study area
Lineament Density
Lineament is defined as observable geomorphic linear features typified weak zones that have characteristic of
fissures/joint, fractures and probably weathered formation and can be attributed to geological structures,
notably fractures or lithologic contacts (Chowdhury et al., 2009). And it was extracted from aeromagnetic and
remote sensing data. The distribution of the lineament density map (Fig. 7) shows that eastern part of the study
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
www.rsisinternational.org
Page 4412
area is underlain by high to very high density of lineament. However, the zones with high groundwater
potential in the study area is due to the occurrence of secondary porosity and permeability developed
occasioned zones characterized with high lineament density (Olabode, 2019).
Fig. 7: Lineament Density Map of the Study Area
Hydraulic Conductivity
The hydraulic conductivity is a measure of how easily water can pass or flow through soil or rock. The
hydraulic conductivity (Fig. 8) values obtained for the investigated area generally varies from (0.00032-0.0279
m/day) using Eq. 2, and means value of 0.014 m/day. The hydraulic conductivity generated for the investigated
area was classified into very low (0.00032-0.0044 m/day), low (0.0044 -0.0076 m/day), moderate (0.0076 -
0.0112 m/day), high (0.0112-0.0164 m/day) and very high (0.0164-0.0279 m/day) hydraulic conductivity. The
areas with low and very low hydraulic conductivity characterize low rate at which water moves through the
aquifer which results to low groundwater potential and less resilience to droughts and fluctuation in
groundwater availability. According to Adeniji et al. (2017) areas with high hydraulic conductivity are most
likely to possess good aquifer recharge quality and hence the high groundwater potential. Therefore, in this
study, area with moderate, high and very high hydraulic conductivity values are more likely to possess
significant groundwater potential.
Figure 8: Hydraulic Conductivity map of the study area
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
www.rsisinternational.org
Page 4413
Overburden Thickness
Figure 9 reveals that, the overburden thickness values in the study area generally range from 2.9- 17.2 m
having a mean value of 10.05 m. Generally, areas with thick overburden values with low percentage of clay
content and characterized with expected pronounced inter-granular flow are expected to have high
groundwater potential, particularly in Basement Complex terrain (Okhue and Olorunfemi, 1991). Moreover,
the weathered layer, the partly weathered/fractured basement and the fractured basement constitutes the major
aquifer unit with significant hydrogeologic importance within the study area. Therefore, the zones that are
characterized by medium, high and very high overburden thickness values can be considered as prospective
zones for possible location of borehole in the study area.
Fig. 9: Overburden Thickness map of the study area
Aquifer Transmissivity
The Aquifer transmissivity is the discharge rate at which water is transmitted through a unit width of an aquifer
under a unit hydraulic gradient. The Aquifer transmissivity values obtained for the investigated area generally
varies from (0.00001644-0.029 m²/day) using Eq. iii, with a mean value of 0.014 m
2
/day. The aquifer
transmissivity map generated for the investigated area (Fig. 10) was classified into very low (0.000744-0.019
m²/day), low (0.0193-0.0396) moderate (0.0396-0.0610 m²/day), high (0.0610 -0.0889 m²/day), and very high
(0.0889-0.1487 m²/day). The areas that are characterized by very low and low characterize the area with low
groundwater potential. The regions with moderate high and very high aquifer transmissivity values can be
identified as area of high water bearing potential and aquifer materials are known to be relatively permeable to
fluid movement (Akintorinwa et al. 2020).
Fig. 10: Transmissivity map of the study area
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
www.rsisinternational.org
Page 4414
Aquifer Resistivity
The aquifer resistivity map was prepared by determining the resistivity of the aquifer layer. Clay has low
resistivity value but generally, areas with low values with low percentage of clay content and characterized
with are expected to have high groundwater potential, particularly in basement complex terrain (Okhue and
Olorunfemi, 1991). Moreover, the weathered layer the partly weathered/fractured basement and the fractured
basement constitutes the major aquifer unit with significant hydrogeologic importance within the study area.
Therefore, the zones that are characterized by very low, low and moderate aquifer resistivity values can be
considered as prospective zones for high groundwater potential.
Fig. 11: Aquifer Resistivity map of the study area
Recharge rate map
The recharge rate (R) of an aquifer denotes the quantity of water per unit area that infiltrates the subsurface,
either from surface infiltration or accumulated ponded water, and eventually contributes to groundwater
storage (Anderson et al., 2015). Aquifers with higher recharge rates are typically associated with greater
groundwater potential, as recharge directly influences groundwater availability. Using the values in Table 2,
derived from (eqn. iv) (Mogaji et al., 2015), the recharge rate within the study area was estimated to range
between 165.57 and 243.52 litres per day. A thematic map of recharge (Figure 12) illustrates the spatial
distribution of recharge levels, categorized into five classes: very low, low, medium, medium-high, and high.
According to the map, the northern and eastern sections of the study area fall predominantly within the high
recharge zone, indicating areas of substantial groundwater potential. Conversely, the southwestern, southern,
and parts of the north-central zones are characterized by low to very low recharge, which corresponds to areas
of limited groundwater potential (Mogaji et al., 2015).
Fig. 12: Recharge rate map of the study area
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
www.rsisinternational.org
Page 4415
THE AHP AND FAHP RESULTS
Groundwater Potential Map
In order to generate the Groundwater Potential Index Evaluation (GWPE) map for the study area. Eight
different thematic analyzed maps comprising Overburden Thickness, Aquifer Resistivity, Lineament Density,
Recharge Rate, Hydraulic Conductivity, Aquifer Transmissivity and Slope Degree. The groundwater index for
the evaluation for the study area was computed using Eq. xii. The fishnet ensures the evenly distributed of
points data on the study area (Fig.13) which the groundwater potential maps for AHP and FAHP models was
generated using ArcGis 10.3.
GWPI = Ʃ W
i
R
i
xii
where W is the weight of parameter ‘i’ and R is the rating score of parameter ‘i’.
Fig. 13: Fishnet template map of the study area
Groundwater Potential Map Obtained from AHP
The Groundwater Potential Map from Analytical Hierarchy Process is divided into five classes of very low,
low, moderate, high and very high in (Fig. 14). The groundwater potential index evaluation map of the study
area shows that the very low and the low index values are found to occupy the south, southwest, central and a
small portion of the northwestern part of the study area. The moderate class occupies the northwest, central and
small portions in the southeast of the study area. The high and very high class of groundwater potential index
evaluation is found to occupy the north, northeast and eastern part of the study area. Area with high to very
high groundwater potential index values is the probable area for good groundwater potential.
Fig. 14: Groundwater Potential map of the study area from the AHP Model
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
www.rsisinternational.org
Page 4416
Groundwater Potential Map Obtained from FAHP
The Groundwater Potential Map from Fuzzy Analytical Hierarchy Process is divided into five classes of very
low, low, moderate, high and very high (Fig. 15). The groundwater potential index evaluation map of the study
area shows that the very low and the low index are found to occupy the south, southwest, central and a small
portion of the northwestern part of the study area. The moderate class trend from the northwest to the south
through the central and also occupies small portions at the northeastern part of the study area. The high and
very high classes of groundwater potential index evaluation is found to occupy the north, northeast and eastern
part of the study area. Areas with high to very high groundwater potential index values are the probable area
for good groundwater potential.
Fig. 15: Groundwater Potential map of the study area from the FAHP Model
Validation and Comparative Analysis of the produced GPM map
In order to test the efficacy and reliabilities of the developed models in this study, validation was carried out on
the produced AHP and FAHP based groundwater potential model maps of the study area. The validation was
executed qualitatively and quantitatively. The quantitative validation was carried out, using the ROC (Receiver
Operating Characteristic) curve. The ROC curve in Fig. 16 is the plot of the false positive rate (FPR) against
the true positive rate (TPR). The ROC curve was applied on the AHP and FAHP based groundwater potential
model maps. Also, the binary cut-off which states (True (1) or False (0) cut off points (Atenidegbe et.al 2023),
with values between 2.9 and 11.3 as True and 1.29 and 2.9 as False, for the determined water column
parameter values of the study area. The corresponding values of the predicted groundwater potential index
(GPI) were extracted from GPM maps at different cut-point compared with the actual water column values of
the study area. The True Positive (TP), False Positive (FP), True Negative (TN), and False Negative (FN) were
determined by the comparison of the water column values with the corresponding GPI values. The TPR
(Sensitivity) and FPR (1-Specificity) was computed using eqn. xiii and xiv, respectively. The ROC curve was
plotted using python and the Area Under Curve (AUC). The ROC curve prediction accuracy was categorized
into five classes (Ekelund, 2011). The categorization is as follows: 0.9 – 1.0 (very good), 0.8 – 0.9 (good), 0.7
– 0.8 (fair), 0.6 – 0.7 (poor) and 0.5 – 0.6 (fail).
Based on the results obtained from this study as shown in figures 16, the AUC value of the prediction rate for
the FAHP based model is 0.81 which indicate 81% prediction accuracy while the prediction rate for the AHP
based model is 0.73 representing 73% prediction accuracy. It can be concluded that the performance of the
developed FAHP based model is ‘better’ compared to the conventional AHP model which performed below
the developed one.
The qualitative validation was carried out via correlation of produced groundwater potentiality model maps
(FAHP) with geology of the study area.
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
www.rsisinternational.org
Page 4417
Sensitivity = TPR =


xiii
Specificity=FPR=


xiv
Table 5: Confusion Matrix application in generating the ROC curves
Fig 4.16: ROC Curves for the FAHP and AHP Model Maps
CONCLUSION AND RECOMMENDATION
This study has demonstrated the application AHP and FAHP in multi criteria decision analysis (MCDA)
technique to geophysical, remote sensing and well parameters in establishing groundwater potential assessment
of the western part of Ado- Ekiti, Southwestern, Nigeria. The MCDA technique was implemented on the
produced thematic maps of the study area. The thematic maps were gotten through the application of GIS on
the processed data from geophysical, geological and remote sensing data. Fifty five Vertical Electrical
Sounding (VES) data were acquired and the data were interpreted qualitatively and quantitatively. The
qualitative interpretation reveals three to five layers with curve types A, AA, AKQ, AKH, KH, HA, and HKH
Curve.
Both the Fuzzy Analytical Hierarchy Process (FAHP) and Analytical Hierarchy Process (AHP) methods were
utilized to assign weights to the groundwater potential conditioning factors in the study area. These factors
include overburden thickness, aquifer resistivity, lineament, lithology, recharge rate, aquifer transmissivity,
hydraulic conductivity and slope degree. These factors were integrated to create the groundwater potential
index map for the study area. The results indicate that the lithology of the study area has the highest weight and
the slope degree having the lowest weight. The results obtained from FAHP address both the imprecision and
uncertainty that arise from the AHP approach.
The ROC curve was used quantitative validation, indicating an 81% AUC for the FAHP model, surpassing the
conventional AHP’s 73% AUC. Based on these validation results, it’s evident that the FAHP outperforms the
conventional AHP in prediction accuracy. The qualitative validation demonstrated that groundwater potential
model maps generated using Fuzzy- AHP, based on geological information of the study area, exhibit
favourable prediction accuracy.
This study effectively categorized the study area into high potential and low potential groundwater zones.
These findings have significant implications for guiding groundwater development within the study area. The
Cut-point
High Potential (1)
Low Potential (0)
High Potential (1)
True Positive(TP)
False Positive(FP)
Low Potential (0)
False Negative(FN)
True Negative
Predicted Values
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
www.rsisinternational.org
Page 4418
delineation of these zones provides valuable insights for making informed decisions about groundwater
resource utilization and management.
This study has been able to establish the reliability of FAHP MCDA technique vis-à-vis its performance when
applied to surface and subsurface geo- parameters for groundwater resources mapping and management.
The comparison between FUZZY AHP and FUZZY TOPSIS method would be really effective for decision
making in groundwater resource management.
AKNOWLEDGEMENTS
This research is not connected to any profitable agency.
Author contributions : KAM performed conceptualization, review and editing, and supervision. RBS
contributed to study conceptualization and design, data collection, data analysis, software and writing original
draft preparation. Both author read and approved the final manuscript.
Declarations; I affirm that this thesis is my original work and has not been submitted to any journal house or
article.
Conflict of interest: On behalf of other author, the corresponding author state that there is no conflict of
interest
REFERENCE
1. 20.Ekelund, S. (2011). Acute care testing. ROC curves- what are they and how are they used?
Acutecaretesting.org
2. Abdulkareem, M., Akrby, A., Fakhry, M. and Mostafa, M., (2018). Using of remote sensing and
aeromagnetic data for predicting potential areas of hydrothermal mineral deposits in the central eastern
desert of Egypt. Journal of Remote Sensing. 7(1), pp.1 – 13.
3. Abedi, M, Norouzi, G.H. and Fathianpour, N. (2013). Fuzzy Outranking Approach: a knowledge-
driven method for mineral prospectivity mapping. Int J Appl Earth Obs Geoinf 21:556–567.
4. Adeyemo, I. A., Omosuyi G.O. and Ojo B.T. (2017) Groundwater Potential Evaluation in Typical
Basement Complex Environment Using GRT Index - A C ase Study of Ipinsa – Okeodu Area, near
Akure, Nigeria. Journal of Geoscience and Evironmental Protection, Vol. 5 No. 3.
5. Adiat, K.A.N. (2013) Development of Models for Predicting Groundwater Resources Potential of
Alluvial Aquifer Using Artificial Intelligence Techniques. Unpublished Ph.d Thesis University Sains
Malaysia , p. 4
6. Adiat, K.A.N., Nawawi, M.N.M. and Abdullah, K. (2012) Assessing the accuracy of GIS-based
elementary multi criteria decision analysis as a spatial prediction tool–A case of predicting potential
zones of sustainable ground water resources. J Hydrol 440–441:75–89.
7. Adiat, K.A.N., Olayanju, G.M., Omosuyi, G.O. and Ako, B.D. (2009). Electromagnetic Profiling and
Electrical Resistivity Soundings in Groundwater Investigation of a Typical Basement Complex – A
Case Study of Oda Town South-western Nigeria. Ozean J. Appl. Sci. 2(4): 333-359.
8. Akinlalu A.A., Omosuyi G.O., Oni T.E. (2017). Groundwater vulnerability assessment using
hydrogeologic and geoelectric layer susceptibility indexing at Igbara Oke, Southwestern Nigeria.
Journal of Astronomy and Geophysics. http://dx.doi.org/10.1016/j.nrjag.2017.04.009.
9. Akintorinwa O.J., Atitebi M.O. and Akinlalu A.A. (2020). Hydrogeophysical and aquifer vulnerability
zonation of a typical basement. complex terrain: A case study of Odode Idanre southwestern Nigeria.
Heliyon 6 (2020) e04549.
10. Alabi, A.A., Bello, R. Ogungbe, A.S. and Oyerinde, H.O. (2010). Determination of groundwater
potential in Lagos State University, Ojo; Using geoelectric methods ( Vertical electrical sounding and
Horizontal profiling). Rep. Opin., 24: 68- 75.
11. Albayrak, E. and Erensal, Y. (2004). Using analytic hierarchy process (AHP) to improve human
performance: an application of multiple criteria decision making problem. J Intell Manuf 15:491–503.
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
www.rsisinternational.org
Page 4419
12. Al-Gharni M.A., (2005). Application of Magnetic and Electrical Geophysical Methods in the
Exploration of Groundwater Resources of Wadi Malakan, Saudi Arabia. Journal of King Abdulaziz
University: Earth Science. 16:67-93.
13. Ariyo, S.O. and Adeyemi, G.O. (2009). Role of Electrical Resistivity Method for Groundwater
Exploration in Hard Rock Areas: A Case Study from Fidiwo/Ajebo Areas of Southwestern
Nigeria. Pac. J. Sci. Technol. 10(1):483-486.
14. Arulbalaji, P., and Gurugnanam, B., (2016). An Integrated Study to Assess the Groundwater Potential
Zone Using Geospatial Tool in Salem District, South India. J. Hydrogeol. Hydrol. Eng.
15. Atenidegbe O.F. and Mogaji, K.A. (2023). Development of Promethee- Entropy data mining model for
groundwater potentiality modeling: a case study of multifaceted geologic setting in south-western
Nigeria. Acta Geophysica. http//doi.org/10.1007/s11600-023-01095-4.
16. Bojadziev, G., and Bojadziev, M. (1998). Fuzzy sets and fuzzy logic applications, Singapore: World
Scientific.
17. Carranza, E.J.M. (2008). Geochemical Anomaly and Mineral Prospectivity Mapping in GIS. Handbook
of Exploration and Environmental Geochemistry, vol. 11. Elsevier, Amsterdam, p. 351
18. Chowdhury, A., Jha, M.K., Chowdary, V.M., Mal, B.C., (2009). Integrated remote sensing and GIS-
based approach for assessing groundwater potential in West Medinipur district, West Bengal India. Int.
J. Remote Sensing. 30 (1), 231–250.
19. Clark, L. (1985) Groundwater Abstraction from Basement Complex Area of Africa. Quarterly Journal
of Engineering Geology and Hydrogeology, 18, 25-34.
20. Dobrin, M.B., Savit, C.H., (1988). Introduction to geophysical prospecting. McGraw-Hill Book Co,
Singapore.
21. Fashae, O.A., Moshood N.T., Talabi A.O,.Adedeji O.I. (2014). Delineation of Groundwater Potential
zones in the crystalline basement terrain of SW-Nigeria: an integrated GIS and remote sensing
approach. Appl Water Sci 4:19-38. https://doi.org 10.1007/s13201-013-0127-9.
22. Haujie, D., Zhengdong D. and Feifan D. (2016). Classification of groundwater potential in Chaoyang
area based on QUEST algorithm: International Geoscience and Remote Sensing.
23. Kaliraj, S. Chandrasekar, N., Magesh, N.S., ( 2013): Identification of potential groundwater recharge
zone in vagai upper basin,Tamil Nadu, using GIS- based analytical hierarchical process (AHP)
technique.
24. Karimi, A.R., Mehrdadi, N., Hashemian, S.J., Nabi-Bidhendi, G.R., and Tavakkoli-Moghaddam, R.,
(2011). Using of the fuzzy TOPSIS and fuzzy AHP methods for wastewater treatment process
selection. International of Academy Research, 3, 780–786.
25. Manap, M.A., Nampak, H., Pradhan, B., Lee, S., Sulaiman, W.N.A. and Ramli, M.F. (2014)
Application of probabilistic-based frequency ratio model in groundwater potential mapping using
remote sensing data and GIS. Arab J Geosci 7:711–724.
26. Moayeri M., Shahvarani A., Behzadi M. H. and Hosseinzadeh-Lotfi F. (2015). Comparison of Fuzzy
AHP and Fuzzy TOPSIS Methods for Math Teachers Selection. Indian Journal of Science and
Technology. Vol 8(13).
27. Mogaji, K.A. and Lim, H.S. (2016). Groundwater potentiality mapping using geoelectrical-based
aquifer hydraulic parameters: GIS-based multi-criteria decision analysis modeling approach. Terr.
Atmos. Ocean Sci. (2016). 10.3316/TAO.2016.11.01.02.
28. Mogaji, K.A. and Omobude, O.B. (2017). Modeling of geoelectric parameters for assessing
groundwater potentiality in a multifaceted geologic terrain, Ipinsa Southwest, Nigeria- A GIS-based
GODT approach. Journal of Astronomy and Geophysics. 6: 434-451.
29. Mogaji, K.A., Lim, H.S. and Abdullah, K. (2014). Modeling groundwater vulnerability prediction using
geographic information system (GIS)-based ordered weighted average (OWA) method and DRASTIC
model theory hybrid approach. Arab J Geosci. doi:10.1007/s12517-013-1163-3.
30. Mogaji, K.A., Lim, H.S. and Abdullah, K. (2015b). Modeling of groundwater recharge using a multiple
linear regression (MLR) recharge model developed from geophysical parameters: a case of
groundwater resources management. Environ Earth Sci (2015) 73:1217–1230. DOI 10.1007/s12665-
014-3476-2.
31. Okhue E.T. and Olorunfemi, M.O. (1991). Electrical resistivity investigation of a typical basement
complex area- Obafemi Awolowo University campus case study. J. Mining Geol. 27(2):66- 70.
INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue IX September 2025
www.rsisinternational.org
Page 4420
32. Omosuyi, G.O. (2010) Geoelectric Assessment of Groundwater Prospect and Vulnerability of
Overburden Aquifers at Idanre, Southwestern Nigeria. Ozean Journal of Applied Science, 3, 19-28.
33. Omosuyi, G.O. and Oseghale, A. (2012) Groundwater Vulnerability Assessment in Shallow Aquifers
Using Geoelectric and Hydrogeologic Parameters at Odigbo, Southwestern Nigeria. Journal of
Scientific and Industrial Research, 3, 501-512.
34. Omosuyi, G.O., Ojo, J.S. and Enikanselu, P.A. (2003) Geophysical Investigation for Groundwater
around Obanla-Obakekere in Akure Area within the Basement Complex of Southwestern Nigeria.
Journal of Mining and Geology, 39, 109-116.
35. Oni, A.G., Adediran, T.A., Olorunfemi, M.O., Eniola, P.J., (2020). Evaluation of the Groundwater
Potential of Modomo, Ile- Ife, Southwest Nigeria, using Integrated Geophysical Techniques.
Sustainable water Resources Management. DOI: 10.1007/s40899-02000467-8.
36. Ouedraogo I., Defourny P. and Vanclooster M. (2016). Mapping the groundwater vulnerability for
pollution at the pan African scale. Science of the Total Environment 544 939–953.
37. Oyedele, E. A. A., Oyedele, T. and Oyedele, K., (2013). Geo-electrical data analysis to demarcate
groundwater pockets in Ado – Ekiti, southwest, Nigeria. 5(11): 610-611.
38. Pazand, K., Hezarkhani, A. and Ghanbari, Y. (2014). Fuzzy analytical hierarchy process and GIS for
predictive Cu porphyry potential mapping: a case study in Ahar-Arasbaran Zone (NW, Iran). Arab J
Geosci 7: 241–251.
39. Radionovs, A. and Uzga- Rebrovs, O. (2016). Fuzzy Analytical hierarchy process for ecological risk
assessment. Information Technology and Management Science, 19(1): 16-22
40. Rahaman, M.A. (1988). Recent advances in the study of the basement complex of Nigeria. In:
Geological Survey of Nigeria (Ed) Precambrian Geology Nigeria, 11–43 pp.
41. Rahmati, O. and Meselle, A. M., (2015). Application of Dempster–Shafer theory, spatial analysis and
remote sensing for groundwater potentiality and nitrate pollution analysis in the semi-arid region of
Khuzestan, Iran. Science of the Total Environment 568 (2016) 1110 1123.
42. Rahmati, O., Nazari Samani A., Mahdavi, M., Pourghasemi, H. R. and Zeinivand, H. (2015).
Groundwater potential mapping at Kurdistan region of Iran using analytic hierarchy process and GIS.
Arab J Geosci. 8:7059–7071.
43. Saaty T. L. (1987). Rank generation, preservation and the reversal in the Analytical Hierarchy Process.
Decis. Sci
44. Szalai S. and L. Szarka (2008). Geoelectric Prospecting on the classification of surface geoelectric
arrays, 56: 159-160.
45. Van Laarhoven. P.J.M. and Pedrycs, W. (1983) A Fuzzy Extension of Saaty’s Priority Theory. Fuzzy
Set and Systems, 11, 229-224.
46. Vander-Velper BPA. (2004) WinRESIST Version 1.0 Resistivity Depth Sounding Interpretation
Software M.Sc. Research Project. ITC, Delft Netherland.
47. Wright, E.P. and Burgess, W.G. (1992) Hydrogeology of Crystalline Basement Aquifers in Africa.
Geological Society London Special Publications, 66, 1-27.
https://doi.org/10.1144/GSL.SP.1992.066.01.01.
48. Wu, J., Huang, H. and Cao, Q. (2013). Research on AHP with interval-valued intuitionistic fuzzy sets
and its application in multi-criteria decision making problems. Appl. Math. Modell. 37, 9898–9909.
49. Yousefi, H., Zahedi, S. and Niksokhan, M.K., (2018). Modifying the analysis made by water quality
index using multi-criteria decision making methods. Journal of African Earth Sciences 138 (2018) 309-
318.
50. Zadeh, L.A., “Fuzzy Sets,” Information and Control, vol.8, pp.338-353, 1965.
51. Zakir, F.A, Mohammed, H.T., Qari, M.E. Most (1999). A new optimizing technique for preparing
lineament density maps. International Journal of Remote Sensing 20(6): 1073-1085.
52. Zare, M., Pourghasemi, H.R., Vafakhah, M. and Pradhan, B. (2013) Landslide susceptibility mapping
at Vaz Watershed (Iran) using an artificial neural network model: a comparison between multilayer
perceptron (MLP) and radial basic function (RBF) algorithms. Arab J Geosci 6:2873–2888.