Vol.:(0123456789)
Environment, Development and Sustainability (2022) 24:2315–2344
https://doi.org/10.1007/s10668-021-01535-5
1 3
Comparison of multi‑influence factor, weight of evidence
and frequency ratio techniques to evaluate groundwater
potential zones of basaltic aquifer systems
Nitin L. Rane1
· Geetha K. Jayaraj2
Received: 11 September 2020 / Accepted: 19 May 2021 / Published online: 25 May 2021
© The Author(s), under exclusive licence to Springer Nature B.V. 2021
Abstract
Groundwater is the largest available reservoir of freshwater. But the rapid increase in the
population and urbanisation, has led to over exploitation of groundwater which imposed
tremendous pressure on global groundwater resources. Because of the hidden and dynamic
nature of groundwater, it requires appropriate quantification for the formulation of ground-
water planning and management strategies. The present study evaluates the efficacy of
geospatial technology based Multi Influence Factor (MIF), Weight of Evidence (WofE)
and Frequency Ratio (FR) technique to evaluate groundwater potential using a case study
of basaltic terrain. The thematic layers influencing the groundwater occurrence viz. rain-
fall, slope, geomorphology, soil type, land use, drainage density, lineament density, and
elevation were prepared using satellite images, hydrologic, hydrogeologic and relevant
field data. Based on the conceptual frameworks of MIF, WofE and FR techniques these
thematic layers and their features were assigned with appropriate weight and then inte-
grated in the ArcGIS platform for the generation of aggregated raster layer which portray
the groundwater potential zones. The results of validation showed that the groundwater
potential delineated using MIF technique has a prediction accuracy of 81.94%, followed by
WofE technique (76.19%) and FR techniques (71.43%). It is concluded that for evaluation
of groundwater potential, the MIF technique is most reliable, followed by the WofE tech-
nique. The evaluated groundwater potential zones are useful as a scientific guide to identify
the suitable location of wells and recharge structure in a cost-efficient way and also for the
development of structured and pragmatic groundwater management strategies.
Keywords Groundwater potential zones · Multi influence factor · Weight of evidence ·
Frequency ratio · Geospatial techniques · Basaltic aquifer
* Nitin L. Rane
nitinrane33@gmail.com
Geetha K. Jayaraj
jayaraj.geetha@gmail.com
1
Department of Civil Engineering, Pillai HOC College of Engineering and Technology,
New Mumbai, India
2
Shivajirao S Jondhle College of Engg & Technology, Asangaon, Thane, India
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2316 N. L. Rane, G. K. Jayaraj
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1 Introduction
Groundwater is an essential freshwater source for agriculture, human survival, industrial
development, and ecosystem conservation, so it must be managed prudently. The mis-
management of this treasured resource leads to the negative effect on the sustainability of
groundwater, causing decreases in groundwater level and triggering environmental prob-
lems such as groundwater quality deterioration, land subsidence, and seawater intrusion
for the present as well as for future generation (Aksever et al., 2015; Bear et al., 1999;
Katpatal et al., 2014; Parisi et al., 2018; Vasanthavigar et al., 2010; Voudouris, 2006; Wada
et al., 2010; Yesilnacar et al., 2008). Many river basins in the world have experienced
severe groundwater stresses (Ghasemi et al., 2017; Palmer et al., 2008; Tsanis & Aposto-
laki, 2009). Agriculture in India is demographically the large economic sector and ranks
second in the world in terms of agriculture production (Ghude et al., 2014; Shah, 2010).
Groundwater is the major freshwater resource of livelihood because more than 60% irriga-
tion in agriculture relying on groundwater and therefore has an important role in the overall
socioeconomic structure of India (Ghude et al., 2014; CGWB 2017). In recent years, due
to increase in the demand of groundwater, causing considerable groundwater depletion in
India (Selvakumar et al., 2018). The groundwater demand in future may increase due to
insufficient storage capacity of surface water resources and unpredictable monsoon. Fur-
thermore, climatic change and socioeconomic factors are likely to increase water issues
(Asoka et al., 2017; Gurdak, 2017; Shah, 2009). These water conditions have severe impli-
cations for the agricultural sustainability, economic development, energy and food security,
ecosystem conservation and industrial development of the country. Therefore, it is required
to use modern tools and techniques to develop a comprehensive database of the quality and
quantity of groundwater, to retrieve the declined trend of groundwater level.
Integrated use of Remote Sensing (RS) and Geographic Information System (GIS)
technology is becoming a useful and powerful tool for identifying and delineation of
groundwater potential (Arulbalaji et al., 2019; Mahmoud, 2014; Singh et al., 2018; Zhu
& Abdelkareem, 2021). The application of RS in hydrogeologic monitoring and investiga-
tion provides useful information in spatio-temporal scales, which is significant to evaluate,
predict, and validate the groundwater models effectively (Kaur et al., 2020; Kim et al 2019;
Singh et al., 2014;). The capabilities of satellite images to cover large spatial scales is cru-
cial for mapping the hydrogeographic characteristics of the basin, such as geomorphology,
drainage density, slope, land use, lineament, and elevation (Devi et al., 2001; Roy et al.,
2019). Such characteristics are the main requirement for assessment and exploration of
groundwater resources (Raju et al., 2019). On the other hand, GIS provides a distinct work-
ing environment that can effectively process and store georeferenced data gathered from
various sources, such as land surveys, maps, and satellite images etc. (Adimalla & Taloor,
2020; Yeh et al., 2009, 2016).
Groundwater exploration using traditional methods, namely field-based surveys, stra-
tigraphy analysis and test drilling are very expensive, time-consuming and laborious
(Chowdhury et al., 2009; Das et al., 2019; Lee et al., 2012; Shahinuzzaman et al., 2021).
Moreover, the groundwater resources planning and development need long term data-
base that is generally not available in numerous regions, especially in developing coun-
tries. The use of RS and GIS overcomes this restriction to some extent and becomes
an efficient tool to monitor, assess and manage the groundwater resources (Achu et al.,
2020; Fashae et al., 2014; Gnanachandrasamy et al., 2018; Jha et al., 2007; Machiwal
et al., 2011; Mahmoud, 2014; Tolche, 2021; Waikar & Nilawar, 2014). The groundwater
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Comparison of multi‑influence factor, weight of evidence and…
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occurrence is controlled by various factors such as drainage density, lineament density,
slope, soil type, elevation, lithology, geomorphology, land use and interrelation among
these factors (Jenifer & Jha, 2017; Magesh et al., 2012; Murthy, 2000; Razandi et al.,
2015; Sahoo et al., 2017; Thapa et al., 2018). The application of RS and GIS to delin-
eate the groundwater potential comprises the integration of hydrological as well as
geological factors, which influence the groundwater occurrence (Gupta & Srivastava,
2010; Pande et al., 2019; Shahid et al., 2000). Several researchers across the world have
used RS and GIS techniques with or without Multi Influence Factor (MIF) technique to
delineate groundwater potential zones in various hydrogeological settings (e.g., Fashae
et al., 2014; Ganapuram et al., 2009; Gupta & Srivastava, 2010; Gumma & Pavelic.,
2013;Ghorbani Nejad et al., 2017; Kumar et al., 2007; Machiwal et al., 2011; Magesh
et al., 2012; Pinto et al., 2017; Pande et al., 2019; Srinivasa and Jugran 2003;).
In addition to conventional Geospatial technology-based MIF technique in the
last few years, the Geospatial technology-based Weight of Evidence (WofE) and Fre-
quency Ratio (FR) technique have been used for evaluating the groundwater potential.
The WofE technique has been employed to water quality evaluation (Lee & Jones-Lee,
2004; Sanderson et al., 2006), assessment of landslide vulnerability (Hong et al., 2017;
Kayastha et al., 2012; Mohammady et al., 2019; Xu et al., 2012), delineation of soil
erosion susceptible zones (Gayen & Saha, 2017; Hembram et al., 2019) prediction of
flood prone zones (Hong et al., 2018; Tehrany et al., 2014) and groundwater potential
zones mapping (Corsini et al., 2009; Tahmassebipoor et al., 2016). Moreover, another
Geospatial technology-based Frequency Ratio (FR) technique has attracted the research-
er’s attention from different disciplines such as landslide hazard mapping (Akgun et al.,
2008; Lee & Pradhan, 2007), prediction of flash flood hazard susceptibility (Cao et al.,
2016). Also, it has been used to evaluate the groundwater potential (Das & Pardeshi,
2018; Razandi et al., 2015; Sahoo et al., 2015). The results show that both the WofE and
FR techniques having the good ability to reliably delineate groundwater potential.
The literature shows that most of the evaluations based on RS and GIS techniques to
delineate the groundwater potential have assessed single MIF or WofE or FR technique.
Thus, identification of appropriate technique is required to provide a higher prediction
accuracy for evaluating the groundwater potential. The primary objective of the study
is to comparatively evaluate the applicability of MIF, WofE and FR technique to iden-
tify and delineate the groundwater potential zones in the study area. The findings and
described framework in this study helpful to identify the efficacy and usefulness of MIF,
WofE and FR technique which gives higher prediction precision to assess the ground-
water potential.
1.1 Study Area
The Kadva river, a tributary of Godavari River is bounded by latitude 20°1′6.27N to
20°26′44.78N and longitude 73°36′43.10E to 74°11′34.02E and encompasses an area
of 1705.24 ­
km2
in Nashik district, India, as shown in Fig. 1. The average annual pre-
cipitation in study area is about 700 mm, in which 80% predominates from the monsoon
winds from the South-West. The climate in the study area is semi-arid with temperature
ranging from 5 to 42 °C in winter and summer season (CGWB 2014). A major part of
basin is covered by agriculture land. The area is primarily irrigated with rivers, canal
water and groundwater (Wagh et al., 2017).
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2318 N. L. Rane, G. K. Jayaraj
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1.2 Hydrogeology
Geologically the study area is covered by basaltic lava flows from Upper Cretaceous to
Eocene age and contains aa and pahoehoe lava flows of basaltic structure (GSI, 2001).
Weathered and fractured units underlain by massive basalt units serve as the main aquifer
system in study area. The aquifer has lack of primary porosity but possesses secondary
Fig. 1 Location of the study area with rain gauge station and elevation
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Comparison of multi‑influence factor, weight of evidence and…
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porosity due to fractured and weathered basalt. The upper weathered and fractured units
comprise the unconfined aquifers, and the occurrence of groundwater is in unconfined con-
dition. The groundwater occurrence at a deep level is under the semi-confined to confined
condition (CGWB 2014). The unconfined aquifer is mainly limited to fractured basalt, and
moderately weathered basalt and the main groundwater source to the large diameter wells.
The basaltic aquifer possesses high heterogeneity in nature and varies over the small dis-
tance because of difference in structural features, texture and lithology. The semi-confined
to confined aquifer is primarily composed of fractured jointed amygdular and vesicular
basalt of considerable thickness and it has developed into semi-confined to confined con-
dition. Groundwater exists in the pore spaces of interconnected vesicular units and in the
jointed and fractured units of a massive basalt of individual flow (Rane Jayaraj, 2021).
Groundwater levels in the study area vary from 0.85 to 13.36 m below ground level (bgl)
in the weathered residuum which is tapping by the hand dug wells, whereas deep fractured
basalt is tapping by the borewells.
1.3 Water issues
From this current study, it is observed that 986.07 ­
km2
area falls under the agricultural land
which is coming as 57.83% of the whole area taken up for the study and it is apparent that
agricultural practices are supported by groundwater and surface water. In many cases, the
supply of surface water is associated with precipitation leading to excessive availability of
water in monsoon period and shortage in the subsequent dry period. Moreover, the ground-
water level in the dry period ranges 2.40–13.36 m (bgl), and post-monsoon groundwater
level ranges 0.85–10.36 m (bgl). The seasonal fluctuations in groundwater level indicate
substantial aquifer recharge during the monsoon season. In dry season, canal network is
unable to supply sufficient water for intensive crops; therefore, water scarcity issues are
severe in dry season, because groundwater is only feasible water resource in such a sit-
uation. This situation results in the increase in the number of wells that exacerbate the
groundwater depletion in the study area. In addition, groundwater availability is limited
in the study area due to the basaltic aquifer, that has low storage capacities. According to
CGWB (2014), the groundwater development stage for two talukas located in the study
area, namely Niphad and Chandwad, are classified as semi-critical areas in which the stage
of groundwater development is 84% and 89%, respectively. This indicates the study area
is using 84% and 89% of the groundwater resources in the Niphad and Chandwad talukas
respectively. In addition, the demand for groundwater increased in recent years because of
the change in population and expansion of agriculture in the study area (Wagh et al., 2017).
These situations illustrate the need of sustainable groundwater management in the study
area.
2 Material and methodology
2.1 Geospatial database preparation
Groundwater potential is controlled by various surface parameters, such as geomorphol-
ogy, anthropogenic activities, lineament, slope, soil type, land use, drainage density, eleva-
tion, rainfall, etc. and subsurface properties such as infiltration capacity, geology, storage
coefficient of aquifer, hydraulic conductivity of aquifer, etc. According to the availability
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2320 N. L. Rane, G. K. Jayaraj
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of field observed and geospatial data and also by consideration of effects of the factors, the
factors which influence the groundwater potential were chosen to evaluate the groundwater
potential through geospatial technology-based MIF, WofE and FR techniques. In the pre-
sent study, eight hydrologic and hydrogeologic factors were selected to evaluate groundwa-
ter potential, and for each factor, the thematic layer was prepared. The daily rainfall data of
six rain gauge station were collected from Department of Water Resources, Maharashtra
and used to prepare the map of rainfall. The annual precipitation data of 18 years were
averaged and assigned to each rain gauge station for the preparation of rainfall map. The
30 m Shuttle Radar Topography Mission (SRTM) DEM data were used for the generation
of slope, drainage density and elevation maps through the ArcGIS spatial analyst tool. The
toposheets acquired from Survey of India were used to digitize the lakes and rivers in the
study area as well as verified using Landsat-8 imagery. The soil type map was acquired
from FAO global soil data map (http://​www.​fao.​org). The land use map was prepared in
ERDAS Imagine 2015 software using supervised classification. In addition to above geo-
spatial data, discharge data were collected from the 72 pumping wells. Figure 4 shows the
location of the pumping wells in study area.
2.2 Delineation of groundwater potential zones
In order to identify and delineate the groundwater potential zones, thematic layers of soil
type, drainage density, rainfall, elevation, lineament, geomorphology, land use, and slope
were used which influence the groundwater occurrence. In the present study, three tech-
niques, namely Multi Influence Factor (MIF) technique, Weight of Evidence (WofE) and
Frequency Ratio (FR) technique were used and comparatively evaluated to identify and
delineate the groundwater potential zones with high prediction accuracy in the study area.
The prediction accuracy is found out by using the number of wells agreed for the actual
groundwater yield data divided by the total number of wells. These three techniques briefly
described in the following sections.
2.2.1 MIF technique
Evaluating the influence of factors separately on groundwater potential cannot portray
the real scenarios. Thus, it is required to use the MIF technique where all input factors
are integrated by taking into consideration of all possible interactions between each fac-
tor. As each factor has a different degree of influence on groundwater occurrence, a
weighted approach is used so that all factors will be incorporated interactively. Flowchart
of groundwater potential delineation using MIF technique is depicted in Fig. 2. In order
to estimate weights of different factors, the influence between all factors should be deter-
mined, and that was carried out according to the schematic interrelation depicted in Fig. 3.
The interrelation is carried out on the basis of prior understanding of the influence factors
for groundwater occurrence from the past research and literature review (Das Pardeshi,
2018; Fashae et al., 2014; Ganapuram et al., 2009; Gumma Pavelic, 2013; Jenifer Jha,
2017; Krishnamurthy et al., 1996; Kumar et al., 2007; Mahmoud, 2014; Pande et al., 2019;
Razandi et al., 2015; Sahoo et al., 2015; Thapa et al., 2018). The factors with a major influ-
ence are assigned a weight of 1.0, while, a minor influence is assigned with a weight of
0.5 and the factor with no effect on groundwater occurrence is assigned a weight of zero.
Then the total relative effect of each factor is calculated by adding values of both major and
minor effect as shown in Table 1.
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Comparison of multi‑influence factor, weight of evidence and…
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The weights for influencing factors are computed as:
where Ej is major interrelation between two factors and Ei is minor interrelation between
two factors.
(1)
� �
Ej + Ei
�
∑ �
Ej + Ei
�
�
X100
Fig. 2 Flowchart of groundwater potential delineation using MIF technique
Fig. 3 Interrelation among the multiple influence factors of groundwater potential in the study area
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2322 N. L. Rane, G. K. Jayaraj
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The calculated relative weights are considered as the weights of corresponding fea-
tures. After computation of weights, rating classification for each feature was performed
by dividing the weight (Wi) by the number of features in each factor, as well as based on
heuristic approach of information on the conditions influencing the groundwater potential.
Table 1 Influence factors, their relative effect and corresponding weight
Factor Major effect (Ej) Minor effect
(Ei)
Relative
effect (Ej + Ei)
Weight of
influence
factor
(Wi)
Land use 1+1 0.5+0.5 3 16
Rainfall 1 0.5+0.5 2 11
Elevation 1 0.5+0.5 2 11
Lineament density 1+1 0 2 11
Soil type 1 0.5 1.5 8
Geomorphology 1+1+1 0.5 3.5 19
Drainage Density 1+1 0.5 2.5 13
Slope 1 0.5+0.5 2 11
∑ 18.5 ∑ 100
Fig. 4 Location map of training and validation wells in the study area
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Comparison of multi‑influence factor, weight of evidence and…
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The influencing factors assigned with weights and ranks were aggregated through follow-
ing formula.
where,—LUland use, RF—average annual rainfall, EL—elevation, LI—lineament, SO
—soil, G—geomorphology, DD—drainage density, SL—slope. In Eq. 2, w—layer weight
computed using MIF technique and, r refer to feature rank respectively.
2.3 WofE technique
The WofE is a quantitative data-driven technique concerning to Bayesian approach for
integrating data and used for the prediction of occurrence of events (Armas, 2012). This
technique calculates the weights for the presence or absence of groundwater influence fea-
tures based on the well existence in study area. The negative weight and positives weight
are the weights of evidence when a feature is absent and present, respectively. WofE tech-
nique requires data on pumping well’s location, as well as the thematic layers that influence
the groundwater potential. The location map of wells over the study area was prepared rep-
resenting 72 pumping wells, of which 51 pumping wells were utilized as training wells and
21 as validation wells as shown in Fig. 4. The verification wells were dedicatedly utilized
to verify results. The thematic layers which influence the groundwater occurrence were
overlaid on the training wells map. Based on this overlap, weights and WofE values of
probability were computed for each feature and employed for the demarcation of ground-
water potential zones.
The weights for each class of a layer were computed as:
where A—number of wells in feature, B—number of wells in study area, C—number of
pixels in feature, and D—number of pixels in study area.
WofE probability ­
(W+
P) for each feature were computed as:
where B—number of wells in study area, and D refers to number of pixels in study area.
2.3.1 FR technique
FR technique is a representative statistical approach used as a spatial mapping technique
for evaluating the association among observed and independent variables, together with
multiple features mapping (Oh et al., 2011). FR technique is based on the observed rela-
tionships, the correlation among each groundwater influencing factor and the location
of wells. FR technique requires the wells’ location and the thematic layers that influence
the groundwater occurrence. The location map of 72 pumping wells located in the study
(2)
Groundwater potential index = (LUW × LUr) + (RFW × RFr)
+(ELW × ELr) + (LIW × LIr)
+(SOW × SOr) + (GW × Gr)
+(DDW × DDr) + (SLW × SLr)
(3)
W+
= 1n
[
A∕B
C − A∕D − B
]
(4)
W+
P = exp
[∑
W+
1n
B
D
]
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2324 N. L. Rane, G. K. Jayaraj
1 3
area was prepared in which 51 were utilized as training wells and 21 wells to verify
results, as shown in Fig. 4. The thematic layers which influence groundwater occurrence
were superimposed over the training wells map, and FR value in each feature was com-
puted using the Eq. 5:
where P—number of wells present in each feature of the influencing factor; Q—num-
ber of total wells in study area; R—number of pixels present in each feature of the influ-
encing factor and, S—number of total pixels in study area. (Fig. 5).
After calculating FR, the groundwater potential map was created by summing up FR
values of each influencing factor and their features using the Eq. 6.
where FR—final weight for the FR technique and n refer to the number of total factors.
(5)
FR =
P∕Q
R∕S
(6)
Ground water potential index =
n
∑
i=1
FR
Fig. 5 Land use in the study area
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Comparison of multi‑influence factor, weight of evidence and…
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3 Results and discussion
3.1 Characterization of spatial variation of the hydrologic and hydrogeologic
conditions
The characterization of thematic layers was performed by classifying their features into appro-
priate groups that helpful for interpretation of their influence in the occurrence of groundwa-
ter. The features of eight influencing factors are described below, and the area occupied by
each feature is shown in Table 2. The study area experiences an average annual rainfall rang-
ing from 600 to 786 mm. The study area was clustered into five zones based on average annual
rainfall. Figure 6 revealed that the East-South portion of the basin receive lowest amount of
rainfall and western part experiences the highest rainfall. It is apparent from the slope map
that there is a steep slope (15.62°) present in the Northern mountains of the basin. As far as
concerned the ground water occurrence, this portion is considered as having poor groundwa-
ter potential because the water flows downward quickly and there is no time for infiltration.
From Fig. 11 it is discernible that, the East-South portion of the basin has almost flat topog-
raphy having 0–2.64% slope that indicates a favourable condition for groundwater potential.
The remaining part has an undulating topography which contributes to a slope of 2.6415.62°.
The thematic layer of drainage density in Fig. 10 indicates that about 66% of the area falls in
the drainage density of 0–0.77 km/km2
and considered as moderate and good for groundwater
potential. High drainage density results in the high runoff, indicating reduced infiltration and
therefore poor groundwater occurrence.
Lineament is associated with the secondary permeability in the basin. In order to determine
the lineament concentration in the study area the lineament density analysis was performed.
From Fig. 7 it is apparent that the lineament density varies from 0–0.09 km/km2
in the study
area whereas the density varies from 0.09–1.40 km/km2
, found in patches in north-western
portion. The higher lineament density is associated with the good groundwater occurrence.
The land use in study area is agricultural land (57.83%), vegetation (4.81%), water bodies
(3.70%), barren land (26.79%), and settlements (6.87%) (Fig. 5). Areas with a large num-
ber of concrete constructions and built-up areas are poor for groundwater occurrence due to
higher surface runoff, whereas agricultural land is good for groundwater occurrence because
of the availability of loose soil on the land. The clayey soil dominating in the study area, is
shown in Fig. 8 and is mostly seen on the land having gentle slope. Loam soil is present in the
higher elevations and inter hill basins. The loam soil is considered good for the groundwater
occurrence.
The topographic elevation in the study area is shown in Fig. 1. The study area has elevation
less than 615 m above mean sea level (AMSL) in the East-South parts, whereas the elevation
more than 733 m AMSL in the Northern portion as shown in Fig. 1. The high-altitudes results
in the poor groundwater potential since water flows towards the low altitude. The thematic
layer of geomorphology is shown in Fig. 9. The study area predominantly occupied by plateau
encompassing 90% of study area. The alluvium is observed along the middle course of river
covering about 1.5% of the area and structural and denudational hills occupying about 8.5% of
study area. (Figs. 10, 11).
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2326 N. L. Rane, G. K. Jayaraj
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Table 2 Statistics of factors with their classes and area
Sr. No Thematic layer Feature of thematic layer Area ­(km2
) Area (%)
1 Land use Agricultural land 986.07 57.83
Vegetation 82.00 4.81
Water bodies 63.15 3.70
Barren land 456.86 26.79
Settlements 117.10 6.87
2 Rainfall (mm) 600–637 103.45 6.07
637–674 205.36 12.04
674–712 546.90 32.07
712–749 428.50 25.13
749–786 420.98 24.69
3 Elevation (m) 529–615 467.68 27.43
615–674 511.11 29.97
674–733 583.63 34.23
733–886 125.26 7.35
886–1,384 16.91 0.99
4 Lineament density (km/km2
) 0–0.09 1308.89 76.76
0.09–0.28 225.80 13.24
0.28–0.49 135.49 7.95
0.49–0.74 33.16 1.94
0.74–1.40 1.85 0.11
5 Soil Loam (Hydrologic soil group C) 144.80 8.49
Clay Loam (Hydrologic soil group D) 169.79 9.96
Clay Loam (Hydrologic soil group C) 449.09 26.34
Clay (Hydrologic soil group D: Clay 51%
wt.)
930.21 54.55
Clay (Hydrologic soil group D: Clay 52%
wt.)
12.30 0.72
6 Geomorphology Plateau 1544.63 90.58
Alluvium 20.73 1.22
Structural Hills 17.47 1.02
Denudational Hills 122.41 7.18
7 Drainage density(km/km2
) 0–0.46 378.01 22.17
0.46–0.77 746.80 43.80
0.77–1.06 504.10 29.56
1.06–1.38 72.34 4.24
1.38–2.50 3.93 0.23
8 Slope 0–2.64o
1014.73 59.51
2.64–6.88o
566.68 33.23
6.88–15.62o
89.55 5.25
15.62–29.66o
24.16 1.42
29.66–67.54o
9.52 0.56
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4
Groundwater potential zone maps of the study area
4.1 MIF technique
According to the interrelationship among factors influencing the groundwater potential,
each thematic layer and their features were assigned with weights. The highest value
of weight shows the highest possibility of factor or feature on groundwater occurrence,
whereas the factor or feature having the lowest influence on groundwater occurrence is
assigned a minimum weight. The derivation of weights for the individual thematic layer
based on the interrelationship between factors is shown in Table 1. Table 3 shows the
assigned weights of each thematic layer and corresponding normalized weights of the
features.
The relative weights of eight hydrologic and hydrogeologic influencing factors and
their features were integrated, and groundwater potential was calculated in ArcGIS
platform. On the basis of groundwater potential, the study area was clustered into four
zones, namely ‘poor’, ‘moderate’, ‘good’, and ‘very good’ zones as shown in Fig. 12.
The 9.27% of study area is falling under ‘poor’ groundwater potential because of steep
slopes and dissected mountains present in these areas, while about 47.72% area is fall-
ing under ‘moderate’ potential. The ‘good’ groundwater potential area is found about
30.17% of the study area. Further, about 12.84% of study area is falling under ‘very
good’ groundwater potential.
Fig. 6 Rainfall in study area
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2328 N. L. Rane, G. K. Jayaraj
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4.1.1 WofE technique
Table 4 presents the number of pixels and wells in each feature of the influencing factor.
The computation of WofE ­
(W+
) and WofE probability ­
(W+
P) for each feature of thematic
layer is computed using Eq. (3 and 4). The value obtained for ­
W+
and ­
W+
P for all the-
matic layers is tabulated and presented in Table 5. Finally, eight hydrologic and hydrogeo-
logic influencing factor were integrated in ArcGIS platform to delineate the groundwater
potential. Based on the groundwater potential, the study area was clustered into four zones,
namely ‘poor’, ‘moderate’, ‘good’, and ‘very good’ as shown in Fig. 13. According to the
groundwater potential delineated using WofE technique, 8.12% and 48.39% of area falls
under ‘poor’ and ‘moderate’ groundwater potential respectively. The 28.77% of study area
falling under ‘good’ groundwater potential while about 14.72% area falling under ‘very
good’ potential respectively.
4.1.2 FR technique
Referring Table 4, the number of pixels and wells in each feature of the influencing factor
are employed to compute FR value of each feature. The FR for each feature is computed
using Eq. 5 and presented in Table 5. Finally, eight hydrologic and hydrogeologic factors
Fig. 7 Lineament density in study area
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2329
Comparison of multi‑influence factor, weight of evidence and…
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which influence the groundwater occurrence were aggregated in ArcGIS platform to delin-
eate groundwater potential. The identified groundwater potential further clustered into four
zones (Fig. 14). The ‘poor’ and ‘moderate’ groundwater potential are covering 7.83%, and
49.05% respectively of study area. Nearly 29.08% and 14.04% of study area are coming
under ‘good’ and ‘very good’ potential respectively.
4.2 Validation and efficacy of MIF, WofE and FR techniques
The verification of MIF technique through the yield data of 72 pumping wells shows that
3 ‘very high discharge’ (6 lps) wells out of 4 wells falls under ‘very good’ groundwater
potential and 32 ‘high discharge’ (4–6 lps) wells out of 34 wells falls under ‘good’ ground-
water potential. Moreover, 24 out of 33 ‘medium discharge’ (2–4 lps) wells falling under
‘moderate’ potential and no well out of 1 ‘low discharge’ (2 lps) well falling under ‘poor’
potential. The number of wells that agreed with the actual yield are 59, and the number of
pumping wells that disagreed with the actual yield are 13. Thus, the accuracy of prediction
using MIF technique is 81.94%.
The verification of WofE and FR technique was carried out through the yield data of
21 verification wells. The verification of WofE technique reveals that 1 out of 1 ‘very high
discharge’ well falls under ‘very good’ potential zone and 7 out of 8 ‘high discharge’ wells
under ‘good’ potential zone. While 8 out of 11 ‘medium discharge’ wells falling under
Fig. 8 Soil type in the study area
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2330 N. L. Rane, G. K. Jayaraj
1 3
‘moderate’ potential, and no well out of 1 ‘low discharge’ well falls under ‘poor’ potential
zone. The number of wells that agreed with actual yield are 16 and the number of wells
that disagreed with actual yield are 5. Thus, the accuracy of prediction using WofE model
is 76.19%.
The verification of FR technique reveals that 1 out of 1 ‘very high discharge’ wells falls
under ‘very good’ groundwater potential and 8 out of 8 ‘high discharge’ wells falls under
‘good’ potential. While, 6 out of 11 ‘medium discharge’ wells fall under ‘moderate’ poten-
tial, and no well out of 1 ‘low discharge’ well fall under ‘poor’ potential. The number of
wells that agreed with the actual yield are 15 and number of wells that disagreed with the
actual yield are 6. Thus, the accuracy of prediction using FR model is 71.43%.
The groundwater potential identified and delineated using MIF technique indicated that
‘poor’ groundwater potential covering 9.27% of the study area due to steep slopes and dis-
sected mountains are present in these areas, ‘moderate’ potential 47.72%, ‘good’ poten-
tial 30.17%, and ‘very good’ potential encompassing 12.84% of the study area. While the
groundwater potential delineated using WofE technique revealed that 8.12% of area has
‘poor’ potential, 48.39% ‘moderate’ potential, 28.77% ‘good’ potential and 14.72% ‘very
good’ potential. Moreover, the groundwater potential delineated using FR technique indi-
cated that 7.83% of the area falling under ‘poor’ potential, 49.05% of the area falls in the
‘moderate’ zone, 29.08% of the area falling under ‘good’ potential and 14.04% ‘very good’
potential. The main difference in the geospatial prediction is identified in poor zone of the
study area because there were no pumping wells in this zone. It is worth to note, that the
absence and presence of pumping wells influence the prediction of groundwater potential
in WofE technique, while the MIF technique only depends on the hydrologic and hydro-
geologic factors. Thus, the reliability of MIF technique is higher than the WofE and FR
Fig. 9 Geomorphology map of study area
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2331
Comparison of multi‑influence factor, weight of evidence and…
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technique. Among the WofE and FR technique, the WofE technique outperformed than FR
technique. Therefore, it is recommended to use MIF technique to identify and delineate
groundwater potential zones. The WofE technique can be used as an alternate technique in
study area. Considering the requirement of pumping well data over the basin, the applica-
tion of WofE technique is limited, particularly in the regions where the data is scarce.
5 Conclusions
The primary objective of the present study was to evaluate the efficacy of geospatial tech-
nology-based MIF, WofE and FR techniques to predict groundwater potential of the basal-
tic aquifer system. The study considered the eight hydrological and hydrogeological factors
which influence the occurrence of groundwater. According to framework of these three
geospatial based techniques, thematic layers and their feature were assigned with suitable
weights and then these thematic layers were integrated into the ArcGIS platform. The pre-
dicted groundwater potential using the MIF technique showed ‘poor’ groundwater poten-
tial encompassing about 9.27% of study area, ‘moderate’ potential zone 47.72%, ‘good’
potential zone 30.17%, and ‘very good’ potential zone covering 12.84% of study area. In
comparison, predicted groundwater prospect using WofE method revealed that 8.12% of
area falling under ‘poor’ groundwater potential, 48.39% ‘moderate’ potential zone, 28.77%
Fig.10
Drainage density in study area
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2332 N. L. Rane, G. K. Jayaraj
1 3
‘good’ potential zone and 14.72% ‘very good’ potential zone. Moreover, the predicted
groundwater potential using FR technique indicated that 7.83% of the area falling under
‘poor’ potential, 49.05% of area falling under ‘moderate’ potential, 29.08% of area fall-
ing under ‘good’ potential and 14.04% ‘very good’ potential. The validation showed that
the MIF technique has 81.94% prediction accuracy, followed by WofE technique (76.19%)
and FR technique (71.43%). Therefore, the MIF technique surpasses the WofE and FR
techniques.
It is concluded that the MIF technique is a most effective technique to evaluate the
groundwater potential zones on a large scale compared to WofE and FR techniques,
although the WofE technique performance somewhat comparable to the MIF technique.
Therefore, in order to obtain more reliable results, it is recommended to use MIF technique
to identify and demarcate the groundwater potential zones. If the sufficient data is available
to use WofE and FR technique, the use of WofE technique recommended over the FR tech-
nique. The delineated groundwater potential zones are useful for understanding the hidden
groundwater resource in study area and for identification of the suitable location of wells
and recharge structure in a cost-efficient way. Furthermore, it can be used to the hydrolo-
gists and planner to develop effective and pragmatic management strategies for addressing
uncertainties and risks associated with groundwater management. The results together with
the demonstrated framework can be reproduced in other basins for assessing the groundwa-
ter potential zones, regardless of hydrogeological conditions.
Fig. 11 Slope map of study area
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2333
Comparison of multi‑influence factor, weight of evidence and…
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Table
3 Assigned
weights
of
the
eight
influencing
factors
with
normalized
weight
of
each
feature
Sr.
No
Thematic
layer
Weight
Feature
of
thematic
layer
Ranking
Normalized
weight
Total
weight
1
Land
use
16
Agricultural
land
12
0.26
0.0326
Vegetation
8
0.17
0.0217
Water
bodies
16
0.35
0.0435
Barren
land
6
0.13
0.0163
Settlements
4
0.09
0.0109
2
Rainfall
(mm)
11
600–637
3
0.09
0.0107
637–674
5
0.14
0.0179
674–712
7
0.20
0.0250
712–749
9
0.26
0.0321
749–786
11
0.31
0.0393
3
Elevation
(m)
11
529–615
11
0.31
0.0393
615–674
9
0.26
0.0321
674-
733
7
0.20
0.0250
733–886
5
0.14
0.0179
886–1,384
3
0.09
0.0107
4
Lineament
density
(km/km
2
)
11
0–0.09
3
0.09
0.0107
0.09–0.28
5
0.14
0.0179
0.28–0.49
7
0.20
0.0250
0.49–0.74
9
0.26
0.0321
0.74–1.40
11
0.31
0.0393
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2334 N. L. Rane, G. K. Jayaraj
1 3
Table
3 (continued)
Sr.
No
Thematic
layer
Weight
Feature
of
thematic
layer
Ranking
Normalized
weight
Total
weight
5
Soil
type
8
Loam
(Hydrologic
soil
group
C)
8
0.31
0.0385
Clay
Loam
(Hydrologic
soil
group
D)
5
0.19
0.0240
Clay
Loam
(Hydrologic
soil
group
C)
6
0.23
0.0288
Clay
(Hydrologic
soil
group
D:
Clay
51%
wt.)
4
0.15
0.0192
Clay
(Hydrologic
soil
group
D:
Clay
52%
wt.)
3
0.12
0.0144
6
Geomorphology
19
Plateau
9
0.23
0.0281
Alluvium
19
0.48
0.0594
Structural
Hills
5
0.13
0.0156
Denudational
Hills
7
0.18
0.0219
7
Drainage
Density
(km/km
2
)
13
0–0.46
5
0.11
0.0139
0.46–0.77
7
0.16
0.0194
0.77–1.06
9
0.20
0.0250
1.06–1.38
11
0.24
0.0306
1.38–2.50
13
0.29
0.0361
8
Slope
11
0–2.64
o
11
0.31
0.0393
2.64–6.88
o
9
0.26
0.0321
6.88–15.62
o
7
0.20
0.0250
15.62–29.66
o
5
0.14
0.0179
29.66–67.54
o
3
0.09
0.0107
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2335
Comparison of multi‑influence factor, weight of evidence and…
1 3
Fig.12
Groundwater potential zones delineated using MIF Technique
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2336 N. L. Rane, G. K. Jayaraj
1 3
Table 4 Number of wells and pixels in each feature of the influencing factor
Sr. No Thematic layer Feature of thematic layer Number of
pixels in
domain
Number
of wells
1 Land use Agriculture 1,095,636 42
Vegetation 91,111 0
Water bodies 70,166 0
Barren land 507,625 0
Settlements 130,107 9
2 Rainfall (mm) 600–637 114,942 7
637–674 228,174 7
674–712 607,670 13
712–749 476,107 11
749–786 467,751 13
3 Elevation (m) 529–615 519,649 23
615–674 567,897 9
674–733 648,479 16
733–886 139,173 3
886–1,384 18,792 0
4 Lineament density (km/km2
) 0–0.09 1,454,325 35
0.09–0.28 250,884 8
0.28–0.49 150,544 5
0.49–0.74 36,839 3
0.74–1.40 2,053 0
5 Soil type Loam (Hydrologic soil group C) 160,884 3
Clay Loam (Hydrologic soil group D) 188,652 3
Clay Loam (Hydrologic soil group C) 498,986 14
Clay (Hydrologic soil group D: Clay 51%
wt.)
1,033,565 30
Clay (Hydrologic soil group D: Clay 52%
wt.)
13,662 1
6 Geomorphology Plateau 1,716,256 49
Alluvium 23,033 2
Structural Hills 19,411 0
Denudational Hills 136,011 0
7 Drainage Density (km/km2
) 0–0.46 420,008 4
0.46–0.77 829,776 27
0.77–1.06 560,106 17
1.06–1.38 80,383 3
1.38–2.50 4,372 0
8 Slope 0–2.64o
1,127,483 33
2.64–6.88o
629,649 15
6.88–15.62o 99,499 3
15.62–29.66o
26,839 0
29.66o
–67.54o
10,582 0
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2337
Comparison of multi‑influence factor, weight of evidence and…
1 3
Table
5 Spatial
relationship
among
each
influencing
factor
using
WofE
and
FR
technique
Sr.
No
Thematic
layer
Feature
of
thematic
layer
Area
%
Well
%
W
+
W
+
P
FR
1
Land
use/land
cover
Agriculture
57.83
82.35
0.3536
0.0000383
1.424
Vegetation
4.81
0.00
0.0000
0.0000269
0.000
Water
bodies
3.70
0.00
0.0000
0.0000269
0.000
Barren
land
26.79
0.00
0.0000
0.0000269
0.000
Settlements
6.87
17.65
0.9439
0.0000692
2.570
2
Rainfall
(mm)
600–637
6.07
13.73
0.8165
0.0000609
2.262
637–674
12.04
13.73
0.1308
0.0000307
1.140
674–712
32.07
25.49
−
0.2297
0.0000214
0.795
712–749
25.13
21.57
−
0.1528
0.0000231
0.858
749–786
24.69
25.49
0.0320
0.0000278
1.032
3
Elevation
(m)
529–615
27.43
45.10
0.4974
0.0000443
1.644
615–674
29.97
17.65
−
0.5297
0.0000158
0.589
674–733
34.23
31.37
−
0.0870
0.0000247
0.917
733–886
7.35
5.88
−
0.2221
0.0000216
0.801
886–1384
0.99
0.00
0.0000
0.0000269
0.000
4
Lineament
density
(km/km
2
)
0–0.09
76.76
68.63
−
0.1120
0.0000241
0.894
0.09–0.28
13.24
15.69
0.1695
0.0000319
1.185
0.28–0.49
7.95
9.80
0.2102
0.0000332
1.234
0.49–0.74
1.94
5.88
1.1071
0.0000814
3.025
0.74–1.40
0.11
0.00
0.0000
0.0000269
0.000
5
Soil
type
Loam
(Hydrologic
soil
group
C)
8.49
5.88
−
0.3671
0.0000186
0.693
Clay
Loam
(Hydrologic
soil
group
D)
9.96
5.88
−
0.5263
0.0000159
0.591
Clay
Loam
(Hydrologic
soil
group
C)
26.34
27.45
0.0415
0.0000281
1.042
Clay
(Hydrologic
soil
group
D:
Clay
51%
wt.)
54.55
58.82
0.0754
0.0000290
1.078
Clay
(Hydrologic
soil
group
D:
Clay
52%
wt.)
0.72
1.96
1.0004
0.0000732
2.719
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2338 N. L. Rane, G. K. Jayaraj
1 3
Table
5 (continued)
Sr.
No
Thematic
layer
Feature
of
thematic
layer
Area
%
Well
%
W
+
W
+
P
FR
6
Geomorphology
Plateau
90.58
96.08
0.0589
0.0000286
1.061
Alluvium
1.22
3.92
1.1713
0.0000868
3.226
Structural
Hills
1.02
0.00
0.0000
0.0000269
0.000
Denudational
Hills
7.18
0.00
0.0000
0.0000269
0.000
7
Drainage
Density
(km/km
2
)
0–0.46
22.17
7.84
−
1.0390
0.0000095
0.354
0.46–0.77
43.80
52.94
0.1897
0.0000325
1.209
0.77–1.06
29.56
33.33
0.1201
0.0000304
1.128
1.06–1.38
4.24
5.88
0.3268
0.0000373
1.386
1.38–2.50
0.23
0.00
0.0000
0.0000269
0.000
8
Slope
0–2.64
o
59.51
64.71
0.0838
0.0000293
1.087
2.64–6.88
o
33.23
29.41
−
0.1221
0.0000238
0.885
6.88–15.62
o
5.25
5.88
0.1135
0.0000302
1.120
15.62–29.66
o
1.42
0.00
0.0000
0.0000269
0.000
29.66–67.54
o
0.56
0.00
0.0000
0.0000269
0.000
W
+
weight
of
evidence,
­
W
+
P
weight
of
evidence
probability,
and
FR
frequency
ratio
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2339
Comparison of multi‑influence factor, weight of evidence and…
1 3
Fig. 13 Groundwater potential zones delineated using WofE Technique
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2340 N. L. Rane, G. K. Jayaraj
1 3
Funding No funding was received.
Declaration
Conflicts of interest None.
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