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International Journal for Research in Engineering Application & Management (IJREAM)
ISSN : 2454-9150 Vol-07, Issue-08, NOV 2021
13 | IJREAMV07I0880004 DOI : 10.35291/2454-9150.2021.0579 © 2021, IJREAM All Rights Reserved.
Application of Multi Influencing Factor (MIF) for
Groundwater Potential Mapping in Varthur catchment
area of Dakshina Pinakini River Basin, Karnataka
1Mr.Munikrishna. L, 2Mrs. Saranya S., 3Dr. Vajrappa H.C
1,2Research Scholar, 3Professor, Department of Geology, Bangalore University, Bangalore,
Karnataka, India. 1krishna.geo21@gmail.com, 2sharnya4ks@gmail.com
Abstract: Groundwater is one of the most precious natural resources for the substance of life on the earth. The present
study aims to assess the groundwater potential zones in the Varthur Catchment of Dakshina Pinakini River Basin,
Karnataka using remote sensing, geographical information system (GIS) and multi influencing factors. The first step,
the groundwater influencing factors such as drainage density, geology, slope, lineament density, geomorphology, land
use / land cover and soil are generated using geospatial techniques. Each every theme is converted to raster format and
assigning scores and weights computed from multi influencing factor (MIF) technique. Then, all the parameters are
statistically computed and integrated to assess the groundwater potential zones in the study area. The output map is
classified into five categories like very low, low, moderate, high and very high. The demarcated groundwater potential
map is validated with the well location data using R-index method. The results will be useful for extraction and
management of groundwater in the study area.
Keywords — Geospatial technology, MIF, R-index, groundwater potential, Varthur Catchment
I. INTRODUCTION
Groundwater is a vital natural resource available in the
planet earth. Depending on its usage and consumption it can
be a renewable or a non renewable resource. It is estimated
that approximately one third of the world’s population use
groundwater for drinking (Nickson et al. 2005). Remote
sensing based integrated studies were carried out in drought
prone area of Dharmapuri district of Tamil Nadu
(Anbazhagan, 1993). Krishnamurthy et al. (1996) have used
remote sensing and GIS in diverse geological set up for the
demarcation of groundwater potential zones in Kochang,
Korea and Marudiyar river basin, in Tamil Nadu, India
respectively. The GWPI (groundwater potentiality index)
values are computed based on the corresponding GWFI
values of the groundwater controlling parameters. Subba
Rao et al. (1997) have used IRS -1B satellite data along
with Survey of India topographic maps to generate
hydrogeomorphology and to delineate groundwater
potential zones.
The use of Remote Sensing technology involves large
amount of spatial data management and requires an efficient
system to handle such data. GIS integration analysis has
successfully applied for mapping of fractured aquifer
system in central part of Tamil Nadu for charactering the
aquifer behaviors (Ramasamy and Anbazhagan, 1997).
Lillesand and Kiefer (1994) defined GIS as a computer base
system that can deal with virtually any type of information
about features that can be referenced by geographical
locations. This system is capable of handling both location
data and attribute data about such features. Manap et al
(2009) discussed the groundwater potential zone at upper
part of Langat basin using index overlay method of GIS
modeling. Geoinformatic technology has been successfully
adopted for groundwater studies and sustainable
development (Anbazhagan and Jothibasu, 2016a).
Anbazhagan and Jothibasu (2016b) have derived
groundwater sustainability indicators in the area of
overexploitation of groundwater zone in Southern India.
The main objective of the present study is to assess the
groundwater potential zones in the Varthur Catchment of
Dakshina Pinakini River Basin, Karnataka using remote
sensing, geographical information system (GIS) and multi
influencing factors.
II. STUDY AREA
The study area Varthur Lake is situated in the southern parts
of the Karnataka State, between 12°48′24.52″ and
12°53′59.85″ North latitude and 77°24′59.95″ to
77°30′6.72″ East Longitude and spreads over a region of
241 sq.km (Figure 1). It gets precipitation from both upper
east and the southwest storms with yearly aggregate
precipitation of around 900 mm. Bengaluru city is for the
most part depleted by part of the Arkavathi river catchment
toward the west and South Pennar River toward the east.
The versatility, presence and aquifer refill of groundwater
International Journal for Research in Engineering Application & Management (IJREAM)
ISSN : 2454-9150 Vol-07, Issue-08, NOV 2021
14 | IJREAMV07I0880004 DOI : 10.35291/2454-9150.2021.0579 © 2021, IJREAM All Rights Reserved.
event are dominated by the measure of weathering, fracture
pattern, geomorphological setup and rainfall. The Bangalore
urban district contains crystalline storm cellar,
fundamentally gneisses and rocks meddled by essential
dykes. These arrangements have been modified to laterite
along the eastern edge of the city. The city is intensely
reliant on groundwater for its household and commercial
needs.
Figure 1: Location of the Varthur Catchment of Dakshina
Pinakini River Basin, Karnataka
III. METHODOLOGY
The topographical map along with Landsat TM data, SRTM
satellite image and secondary data is used for the
preparation of groundwater influencing parameters such as
drainage density, geology, slope, lineament density,
geomorphology, land use/land cover, and soil. These
thematic maps extracted through the digitization process
were converted into raster format using the feature to raster
tool in ArcGIS 10.2 software. The scores and weights for
each influencing factor and sub-classes were assigned by
using the multi-influencing factor (MIF) technique. Then
all the thematic maps were integrated using the weighted
overlay method in the GIS environment to identifying
groundwater potential zones of the study area. The resultant
map was validated with well location data using the R-index
method.
3.1 Groundwater influencing factors
3.1.3 Land use / land cover
Land use and land cover is one of the important parameters
for groundwater occurrences indirectly through infiltration,
runoff, and evaporation. The presence of vegetation cover
minimizes evaporation and runoff while it increases
infiltration. Varthur catchment areas have been classified
into water bodies, cropping land, built-up land and
fallow/grass land features (Fig.2). These features were
assigned rank and weight based on importance of
groundwater occurrences.
Figure 2: Land use / land cover in the study area
3.1.2 Geomorphology
Geomorphology is one of the essential components for
understanding the landforms evolutions that control the
movement and occurrence of groundwater (Raju thapa et al.
2017). The geomorphic features interpreted in the satellite
data such as deep pediment, structural hill, residual hill,
pediment inselberg complex, valley fill, moderate pediment
and shallow pediment (Fig.3). Among these valley fill and
pediments are good in groundwater potential.
Figure 3: Geomorphological features in the study area
3.1.3 Aquifer thickness
Lithology plays a vital role in the distribution and
occurrence of groundwater. The lithology affects both the
porosity and permeability of aquifer rocks (Ayazi et al.
2010; Chowdhury et al. 2010). The rocks became aquifers
through the development of weathering and fracturing and
secondary porosity (Sener et al. 2005). In the context,
aquifer thickness was taken from geophysical resistivity
method (Fig.4).
International Journal for Research in Engineering Application & Management (IJREAM)
ISSN : 2454-9150 Vol-07, Issue-08, NOV 2021
15 | IJREAMV07I0880004 DOI : 10.35291/2454-9150.2021.0579 © 2021, IJREAM All Rights Reserved.
Figure 4: Aquifer thicknesses in the study area
3.1.4 Topographic Wetness Index (TWI)
There is a vital role for topography in the spatial variation
of hydrological conditions such as soil moisture and
groundwater flow. Therefore, the secondary topographic
indices have been used for describing spatial soil moisture
patterns (Moore et al. 1991). It has been widely used to
explain the impact of topography conditions on the location
and size of saturated source zones of surface runoff
generation. Recently, TWI has been used for groundwater
potential mapping (Davoodi Moghaddam et al. 2013;
Nampak et al. 2014) and describing spatial wetness patterns
(Pourghasemi et al. 2012a; Pourtaghi and Pourghasemi
2014). It is defined as (Moore et al. 1991):
where, AS is the cumulative upslope area draining through a
point (per unit
contour length) and
β is the slope gradient (in degree).
In this study, TWI map is grouped into four classes using
quantile classification scheme (Tehrany et al. 2014) (Fig.5).
Figure 5: Topographic wetness index of the study area
3.1.5 Drainage density
Drainage density is the closeness of the spacing of stream
channels. It is a measure of the overall length of the stream
segment for all orders per unit area. The drainage map was
interpreted from the survey of India’s topographical map.
From the drainage, the density was prepared using line
density in spatial analysis tools in ArcGIS. The drainage
density of the study area varies from 0 to 4.12 km/sq.km
and reclassified into five classes by equal interval method
and shown in Fig.6. The higher drainage density represents
the good potential for groundwater prospects.
Figure 6: Drainage densities in the study area
3.1.6 Lineament density
Groundwater availability and flow directions depend upon
the linear features such as drainages, linear vegetation,
weaker plain, secondary porosity, and permeability.
Remote sensing data provides the synoptic view of the large
surface area which helps to understand the occurrence of
lineament. Linear features spatial map prepared from
Landsat TM data. The higher the lineament density is
suitable for high groundwater prospecting. In the study
area, lineament density varies from 0 to 186 km/sq.km
shown in Fig.7. It is reclassified into five classes by equal
interval method and given higher weight is assigned to
94.22-186.29 km/sq.km and least weight to 0-15.64
km/sq.km.
Figure 7: Lineament densities in the study area
International Journal for Research in Engineering Application & Management (IJREAM)
ISSN : 2454-9150 Vol-07, Issue-08, NOV 2021
16 | IJREAMV07I0880004 DOI : 10.35291/2454-9150.2021.0579 © 2021, IJREAM All Rights Reserved.
3.1.7 Slope
The slope is one of the important terrain parameters which
affect the occurrence and movement of groundwater.
Infiltration of the surface is directly influenced by the slope
gradient. In the lower slope area, the surface runoff is slow
and the infiltration rate is higher than the steeper slope. The
slope map of the study area was prepared from an SRTM
satellite image. The majority of the study area falls under
the gentle slope category with a slope less than 0.77°. On
the basis of the slope angle, the entire study area is divided
into five classes such as 0-0.77, 0.77-1.37, 1.37-1.99, 1.99-
2.80, and 2.8-6.59° and shown in Fig.8.
Figure 8: Slope (degree) in the study area
3.1.8 Elevation
The elevation is also one of the deciding factors which
affect the availability of groundwater in any area. Elevation
was created directly from the SRTM satellite image and
classifies into five categories (854-879, 880-892, 893-905,
906-920, 921-959 m) based on an equal interval method
and shown in Fig.9. Higher elevation areas had a higher
runoff, whereas lower elevation area has more recharge and
high infiltration.
Figure 9: Elevation in the study area
3.1.9 Well location
The well location data were collected from the central
groundwater board (CGWB) reports and extensive field
surveys. The total numbers of well locations in the study
area are 29 (Fig.10). These wells will be assigned for the
validation purpose of the study results.
Figure 10: Well locations in the study area
3.2 Multi influencing factors of groundwater potential
zones
A multi influencing factor is one of the multi-criteria
decision-making techniques which are useful for analyzing
unbiased decision making. The weights of each factor were
determined using MIF techniques. According to the multi
influencing factor technique, each influencing factor has
some major and minor effects based on groundwater
prospect. The interrelationship between these factors and
their effect is shown in Fig.11. Factors having a major
influence were marked as major effect and assigned a
weight of 1.0 while minor influence was marked as minor
effect and a weight of 0.5 was assigned as shown in Table 1
(Magesh et al. 2012). The combined proposed score of each
influencing factor of the major and minor effects is
computed out using Eq.2.
Proposed score = (2)
Where X represents the major effect of factors and Y
represents the minor effect of factors. The proposed score
of each influencing factor reclassified to the subclasses
(Table 2). For groundwater potential, weights were assigned
for subclasses of each individual factor and are shown in
Table 2. The proposed score of each individual factor is
multiplied by the weights of subclasses.
Figure 11: Multi influencing factors for groundwater
potential zone mapping
International Journal for Research in Engineering Application & Management (IJREAM)
ISSN : 2454-9150 Vol-07, Issue-08, NOV 2021
17 | IJREAMV07I0880004 DOI : 10.35291/2454-9150.2021.0579 © 2021, IJREAM All Rights Reserved.
Table 1: Effect of influencing factor, relative rates and score
for each potential factor
Factors
Major
effect (X)
Minor
effect
(Y)
Proposed
relative
rates
(X+Y)
Proposed
score of each
influencing
factor
Land use / land
cover
1+1
1.5
3.5
13
Geomorphology
1+1+1
1
4
15
Aquifer thickness
1+1
0.5
2.5
10
TWI
1+1+1+1
0.5
4.5
17
Drainage density
1
1.5
2.5
10
Lineament density
1+1+1+1
0
4
15
Slope
1+1
1
3
12
Elevation
1+1
0
2
8
Total
Σ=26
Σ=100
Table 2 Assigning weightage of sub class of influencing
factors
Groundwater
Influence factors
Sub-class
Rank
Weight
Score
Land use / Land
cover
Water Bodies
Cropping land
Built-up land
Fallow/Grass land
13
10
1
5
13
169
130
13
65
Geomorphology
Deep pediment
Structural Hill
Residual Hill
Pediment
inselberg complex
Valley fill
Moderate
pediment
Shallow pediment
15
2
1
5
13
14
12
15
225
30
15
75
195
210
180
Aquifer thickness
6.80-8.05
8.05-8.87
8.87-9.84
9.84-11.42
11.42-14.58
10
8
6
4
2
10
100
80
60
40
20
TWI
Very low
Low
Moderate
High
Very high
6
9
11
15
17
17
107
153
187
255
289
Drainage Density
km/sq.km
Very low (0-0.43)
Low (0.43-0.99)
Moderate (0.99-
1.56)
High (1.56-2.24)
Very high (2.24-
4.12)
2
4
6
8
10
10
20
40
60
80
100
Lineament density
km/sq.km
Very low 0-15.64
Low
Low (15.64-
43.74)
Moderate (43.74-
69.53)
High (69.53-
94.22)
Very high (94.22-
2
3
5
7
10
15
30
45
75
105
150
186.29)
Slope
0-0.80
0.80-1.39
1.39-2.00
2.00-2.76
2.76-6.59
8
7
5
4
1
12
96
84
60
48
12
Elevation
819-879
879-892
892-905
905-920
920-959
7
5
4
3
1
8
56
40
32
24
8
IV. RESULTS AND DISCUSSION
In this study, identification of groundwater potential zones
in Varthur catchment all the thematic layers were assigned
score and weights by using multi influencing factor
techniques. All the layers were converted into raster format
with 30m×30m grid cell size and multiplied with derived
score and weights. The integrated output map produced
from the weighted overlay analysis method using the
equation in GIS software. Further, the result of overlay
analysis has been classified into five classes as very high,
high, moderate, low, and very low groundwater potential
zones and shown in Fig.12.
Figure 12: Groundwater potential zones in the study area
4.1 Validation
The potential map was verified using the distribution of well
locations. For the verification, the R-index method was
used. The R-index method is to evaluate the association
between well location points and the groundwater potential
map. The aim of validation is to evaluate the performance
of the potential of the study area. The index is given by
Baeza and Corominas (2001). The R-index as applied to the
groundwater potential map is defined as follows:
R = (ni / Ni) / ∑(ni / Ni) * 100 (3)
Where, ni is the number of wells in the potential class ‘i’
and
Ni is the number of cells occupying the same potential
class
International Journal for Research in Engineering Application & Management (IJREAM)
ISSN : 2454-9150 Vol-07, Issue-08, NOV 2021
18 | IJREAMV07I0880004 DOI : 10.35291/2454-9150.2021.0579 © 2021, IJREAM All Rights Reserved.
The R-index for each potential class is represented in Table
3, and the graphical representation (Fig.13) points out the
distribution of well locations observed in the classes,
indicating the consistency of groundwater potential classes.
Table 3: R-index in groundwater potential zone map
Groundwater
potential zones
Area
(Sq.km)
Ratio
No. of
Wells in
class
Ratio
R-
Index
Very High
6.62
2.35
2
6.90
30.21
High
61.53
21.81
11
37.93
17.88
Moderate
174.14
61.74
15
51.72
8.61
Low
38.68
13.71
1
3.45
2.59
Very Low
1.10
0.39
0
0
0
Figure 13 Graphical representation of R-index of
groundwater potential zones
V. CONCLUSIONS
In this study, GIS-based MIF techniques were chosen to
obtain spatially distributed groundwater potential zones in
the Varthur catchment area. High lineament density,
drainage density, and valley fill regions were also
associated with good groundwater potential. Nearly, 24%
of the total area falls from high to very high zones have
good groundwater potential. The method outcomes
acquired in this research were validated with the GW wells
data using the R-index method. The validation of the
results showed that the values of very high potential zones
of 30.21. It was observed that the R-index increases with
the level of the prediction rate. Therefore, based on the
result and accuracy the study suggests this method would be
suitable for exploring groundwater potential zones. The
results of groundwater potential maps can be useful for
planning for groundwater exploration, conservation, and
management in the study area.
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