Journal of Geographic Information System, 2010, 2, 152-162
doi:10.4236/jgis.2010.23022 Published Online July 2010 (http://www.SciRP.org/journal/jgis)
A Hybrid Approach towards the Assessment of
Groundwater Quality for Potability: A Fuzzy Logic and
GIS Based Case Study of Tiruchirappalli City, India
Natarajan Venkat Kumar, Samson Mathew, Ganapathiram Swaminathan
Civil Engineering Department, National Institute of Technology, Tiruchirappalli, India
Received February 28, 2010; revised April 7, 2010; accepted April 15, 2010
Abstract
The present study aims to develop a new hybrid Fuzzy Simulink model to assess the groundwater quality
levels in Tiruchirappalli city, South India. Water quality management is an important issue in the modern
times. The data collected for Tiruchirappalli city have been utilized to develop the approach. This is illus-
trated with seventy nine groundwater samples collected from Tiruchirappalli city Corporation, South India.
The characteristics of the groundwater for this plain were monitored during the years 2006 and 2008. The
quality of groundwater at several established stations within the plain were assessed using Fuzzy Logic (FL)
and GIS maps. The results of the calculated FL and GIS maps with the monitoring study have yielded good
agreement. Groundwater quality for potability indicated high to moderate water pollution levels at Srirangam,
Ariyamangalam, Golden Rock and K. Abisekapurm zones of the study area, depending on factors such as depth
to groundwater, constituents of groundwater and vulnerability of groundwater to pollution. Fuzzy logic simu-
lation approach has shown to be a practical, simple and useful tool to assess groundwater quality assessment for
potability. This approach is capable of showing and updating the water quality assessment for drinking.
Keywords: Groundwater quality, Fuzzy Logic Model, GIS, Potability, Tiruchirappalli City
1. Introduction
Over few decades, competition for economic developm-
ent, associated with rapid growth in population and urba-
nization, has brought in significant changes in land use,
resulting in more demand of water for domestic activities.
Groundwater is one among the Nation’s most important
natural resources. Very large quanta of ground water are
pumped each day for industrial, agricultural, and com-
mercial use. Groundwater is the drinking-water source
for about one-half of the nation’s population, including
almost all residents in rural areas. Information on the
quality and quantity of ground water is important be-
cause of the nation’s increasing population and depend-
ency on this resource. The population dependent on pub-
lic water systems that used groundwater for drinking
water supplies increased during last fifty years. The es-
timated withdrawal increased about five-fold during last
half century. The quality and availability of ground water
will continue to be an important environmental issue.
Long-term conservation, prudent development and man-
agement of this natural resource are critical for preserv-
ing and protecting this priceless national asset.
As per the International norms, if per capita water avai-
lability is less than 1700 m3 per year then the country is
categorized as water stressed and if it is less than 1000 m3
per capita per year then the country is classified as water
scarce. India is water stressed and is likely to be water
scarce by 2050 [1].
Continued research, guidance and regulations by gov-
ernment agencies and pollution abatement programmes
are necessary to preserve the Nation’s groundwater qual-
ity and quantity for future generations. The impact of
Industrial effluents is also responsible for the deteriora-
tion of the physico-chemical and bio-chemical parame-
ters of groundwater [2]. The environmental impacts on
the groundwater contaminations may seriously affect the
socio-economic conditions of the country. Knowledge on
water chemistry is important to assess the quality of aqu-
atic resources for understanding its suitability for various
needs [3]. Information on the status and changing trends
in water quality is necessary to formulate suitable guide-
Sponsor by M.N.S.Gold Technologies Madurai, India.

N. V. KUMAR ET AL.
153
lines and efficient implementation for water quality as-
sessment, water quality monitoring and enforcement of
prescribed limits by different regulatory bodies [4].
Various methods discussed in literature on drinking wa-
ter quality revealed that deterministic approach in deci-
sion making by comparing values of parameters of water
qua- lity with prescribed limits provided by different
regulatory bodies could be used without considering un-
certainties involved [5].
There are two areas in which the literature is far from
complete and has the gaps which are to be bridged and
these are: 1) The decision on the water quality assessm
ent (desirable, acceptable or not acceptable) using fuzzy
logic and 2) The sets of the monitored data and limits
should not be as crisp set, but as fuzzy sets. One way of
avoiding the difficulty in uncertainty handling in water
quality assessment is to introduce a margin of safety or
degree of precaution.
Before applying a single value to drinking water quali
ty standards as the same technique was also used by oth-
er researchers in the field of environmental science [6-8]
Keeping the importance of uncertainty handling in the
potable water quality assessment and versatility of the
fuzzy set theory in decision-making in the imprecise en-
vironment, an attempt has been made to classify the
groundwater from Tiruchirappalli City Corporation of
Tamilnadu, South India for the potable use [9].
2. Study Area, Materials and Methods
2.1. Study Area
The Base map of Tiruchirappalli city was drawn from Su-
rvey of India Topo sheets Nos. 58 J/9, 10, 13 and 14 and
satellite imagery (IRS -1C and LISS III) is lies between
10°48'18'' North: 78°41'7'' East. The general topology of
Tiruchirappalli is flat and lies at an altitude of 78 m
above sea level. Tiruchirappalli is fed by the rivers Cau-
very and Kollidam. There are reserve forests along the
river Cauvery. Golden Rock and the Rock Fort are the
prominent hills. The southern/south-western part of the
district is dotted by several hills which are thought to be
an offset of the Western Ghats Mountain range and the
soil is considered to be very fertile. For the sample col-
lection, seventy nine bore well locations were identified.
These locations were identified in such a way that the
bore wells were evenly distributed over the study area
and have used for potability.
The water samples were collected for periods between
March 2006 and December 2008. The water from these
bore wells were used for drinking, house hold utilities
and bathing by the residents. The Laboratory tests were
conducted on these samples for 16 different physico-che-
mical potable water quality parameters as per the stan-
dard procedure [10-12] criteria were adopted for testing
these samples.
2.2. Thematic Maps
The base map data was used for the study included digi-
tized data sets originally developed by Survey of India,
and the Tiruchirappalli city corporation. The work maps
were prepared from 1:20,000 scale topographic paper
maps using AutoCAD, Arc GIS 9.2 and Surfer V.8.
The groundwater hydrochemistry records of the study
area were used for the preparation of the maps. These
maps are obtained by geostatistical (Kriging) methodol-
ogy and the results were presented in the form of equal
ion concentration lines [13].
The groundwater quality data were used as the hidden
layer for the preparation of base maps. These features
were the boundary lines between mapping units, other
linear features (streets, rivers, roads, etc.) and point fea-
tures (bore well points, etc.). The contours were devel-
oped for pH, EC, Cl-, Na+, Ca2+, Mg2+, Total Hardness,
Alkalinity, F, SO4
2-, Coli form and NO3- for the seasonal
conditions of the study period between 2006 and 2008.
The monitoring and sampling program was initiated in
2006 and finalized the year 2008. A total of seventy nine
monitoring stations were established of them represented
groundwater conditions. The groundwater stations had
different depths to groundwater. The sampling locations
of all the stations are shown in Figure 1. A total of sev-
enty nine separate ground water quality monitoring ses-
sions were realized during the study period during the
months of June, August, October and November of the
year 2006-2008 and March, June and October of the year
2006-2008.
2.3. Potable Water Quality Maps
The data used for the mapping water quality assessment
for potability were developed from the laboratory water
quality analysis. Data for these studies were based on the
sampling conducted by the first author for groundwater
samples collected from predetermined locations of exist-
ing bore wells in Tiruchirappalli city. The data were
linked to the sampling bore well locations using geodata
base creation of Arc GIS 9.2 and Surfer 8 software.
The decision on the water quality assessment for pota-
bility gives that the water is desirable, acceptable and not
acceptable as per the guidelines from BIS and WHO
[9-11] regulatory bodies. But, in the border line cases of
water quality parameters, it becomes a Herculean task as
different types of uncertainties are involved at various
part of experimental and measurement process right from
sampling, sample storage, processing and analysis. The
sets of the monitored data and limits should not be as
crisp set, but as fuzzy sets. One way of avoiding the dif-
ficulty in uncertainty handling in water quality assess-
ment is to introduce a margin of safety or degree of pre-
caution before applying a single value to drinking water

N. V. KUMAR ET AL. 154
Figure 1. Study area and sampling location map of Tiruchirappalli city.
quality standards as the same technique was also used by
other researchers in the field of environmental science
[2,5,12-14]. These methodologies based on fuzzy set
theory were utilized with real environmental water qual-
ity assessment to handle the uncertainties in imprecise
environment in decision- making on the potability of
water quality can be handled. The concept of a class with
unsharp boundaries and marked the beginning of a new
direction by providing a basis for a qualitative approach
to the analysis of complex systems in which linguistic
rather than numerical variables are employed to describe
system behavior and performance [15]. Keeping the im-
portance of uncertainty handling in the drinking water
quality assessment and versatility of the fuzzy set theory
in decision-making. An attempt was made to classify the
under ground water from Tiruchirappalli city corporation,
South India for the drinking purposes.

N. V. KUMAR ET AL.
155
3. A Hybrid of Fuzzy Logic Model with GIS
Geographic Information Systems (GIS) have become im-
portant tool in efficiently solving many problems in wh-
ich spatial data are important. Natural re-sources and env-
ironmental concerns, including groundwater have bene-
fited greatly from the use of GIS. It is becoming power-
ful computer tools for varied applications ranging from
sophisticated analysis and modeling of spatial data to si-
mple inventory and management. GIS incorporates data
that describes population characteristics, socio-economic
conditions, landscape and analysis the spatial relation-
ship of these factors. In addition to integrating and ana-
lyzing health related data, this technology promotes data
sharing through the use of standard formats and act as a
highly efficient communication tool. Analysis of spatial
and attribute data in a GIS can be classified into five
main types of procedures.
1) Data transformation and restructuring
2) Data retrieval, classification and measurement
3) Overlay
4) Neighborhood and statistical measures and
5) Connectivity
The most significant difference between GIS and other
information systems, the databases is the spatial nature of
the data in a GIS. The analysis functions in a GIS allow
manipulation of multiple themes of spatial data to per-
form overlays, buffering and arithmetic operations on the
data with its spatial analysis capabilities, GIS technolo-
gy can play an important role in human services research
there by ensuring better service delivery for clients. Along
with other computer applications including word proces-
sors, spread sheets, databases, statistical packages and
the internet, GIS is thus a versatile tool for human ser-
vice professionals providing a competitive edge particu-
larly in the areas of planning and evolution and commu-
nity development. Hence the GIS methodology has been
adopted in the presented study and illustrated in Figure 2.
Data collected from the study area for various seasons
were used as the input for simulation model. The simula-
tion was used for the collected data for all the samples of
the studied seasonal variations. Based on expert knowl-
edge [16,17] 66 rules were designed for physico-chemi-
cal water quality parameters in Group I Figure 3, where
as 73 rules were designed for Group II Figure 4. Results
from group one and two are combined with Group III
Figure 5 to assess the final classification of water. A
total of 27 rules were fired for the final assessment of
groundwater quality in the fuzzy logic model. The results
from all the three groups were aggregated to assess
Figure 2. Flow chart showing data flow and analysis of GIS in the present study.

N. V. KUMAR ET AL. 156
the final classification of water as shown in Figure 6.
The processes were applied to all the seasonal water
samples and the results obtained are as shown in Figure
7(a), (b) and (c).
The rule based decision on expert’s perception has
been fired using Mamdani implification of maximum and
minimum operator [18]. To assess the drinking water
quality of the groundwater samples, 181 rules are fired.
System Gr1: 4 inputs, 1 outputs, 66 rules
pH (5 CLASS)
Ec (4 CLASS)
Chloride (3 CLASS)
Sodium (3 CLASS)
Gr1a
(mamdani)
66 rules
FUZZY RULE BASED SYSTEM
INPUT VARIABLE FROM GROUP 1
classification (4)
not acceptable
acceptable
acceptable
desriable
not acceptable
not acceptable
desirable
not acceptable
not acceptable
desirable
not acceptable
not potable
certainly potable
potable
highly potable
acceptable
desirable
not acceptable
not acceptable
Figure 3. Block diagram for fuzzy logic model for First Group water quality parameters.
System Gr2 : 4 inputs, 1 outputs, 73 rules
TotalAlkalinity (4 class )
TotalHarness (4 class)
Calcium (4 class)
Magnesium (4 class)
WaterQualityClassification (4)
Gr2a
(mamdani)
73 rules
acceptable
desirable
not acceptable
not acceptable
acceptable
desirable
not acceptable
not acceptable
acceptable
desirable
not acceptable
not acceptable
acceptable
desirable
not acceptable
not acceptable
not potable
certainly potable
potable
highly potable
Figure 4. Block diagram for fuzzy logic model for Second Group water quality parameters.

N. V. KUMAR ET AL.
157
System Gr3a: 4 inputs, 1 outputs, 42 rules
FLUORIDE (4 class)
NITRATE (3 class)
COLIFORMS (3 class)
Gr3a
(mamdani)
42 rules
fuzzy rule based system
In put variables for group 3
GWQualityClassification (4)
not potable
certanly potable
potable
Highly potable
acceptable
<not acceptable
>not acceptable
acceptable
acceptable
<not acceptable
>not acceptable
acceptable
< not acceptable
> not acceptable
acceptable
< not acceptable
not acceptable
SULPHATE (3 class)
Figure 5. Block diagram for fuzzy logic model for Third Group water quality parameters.
System FIP : 3 inputs, 1 outputs, 27 rules
group1 (3)
group2 (3)
group3 (3)
Ground water quality classification (4)
fip
(mamdani)
27 rules
fuzzy rule based system
INPUT VARIABLE FROM THREE GROUPS
not acceptable
desirable
acceptable
not acceptable
desirable
acceptable
not acceptable
desirable
acceptable
not potable
certainly potable
potable
highly potable
Figure 6. Block diagram for fuzzy logic model Final groundwater quality assessment.
3.1 Fuzzy Logic and GIS Approach towards
Groundwater Classification
A fuzzy rule based system is generated in which users
classify the water according to given data in Desirable,
Acceptable, Not acceptable, Rejected quality with re
spect to different parameters, all connected using AND
operator. The user’s feedback is also taken with respect
to overall quality for different parameters connected by
AND operator. For example, one of the feedbacks taken
may be like this, If TDS = good AND pH = medium and
Sulphate = good then, overall water quality = What ?
After this, Delphi’s technique is applied to converge the

N. V. KUMAR ET AL. 158
010 20 3040 50 60 7080
30
40
50
60
70
80
90
100
Number of Samples
% of Groundwater quality Classification
FIP model out put for Premonsoon '06
OUTPUT(1:79, 1)
qty min
qty max
qty mean
(a)
010 20 3040 50 60 70 80
65
70
75
80
85
90
95
Number of samples
% of Groundwater quality classification
FIP model output for Premonsoon '08
OUTPUT(1:79, 1)
qty min
qty max
qty mean
(b)
010 20 3040 50 60 70 80
30
40
50
60
70
80
90
100
Number of samples
% of Groundwater quality Classification
FIP model output for Premonsoon '07
OUTPUT(1:79,1)
qty min
qty max
qty mean
(c)
Figure 7. Subsurface water potable frequency during pre-
monsoon periods. (a) 2006; (b) 2007; (c) 2008.
feedback of various users to a single value. A degree of
match has been computed between the user’s perception
and field data for different parameters and for every type
of water quality viz. good (Desirable) medium (Accept-
able) or bad (Not Desirable). The water quality for which
degree of match is the highest and was considered to
represent the quality of the water sample.
4. Results and Discussions
Physio-chemical groundwater quality assessment by det-
erministic method for drinking groundwater usage on the
basis of einght water quality parameters were compared
with the concentration in the water with point value pre-
scribed limits. In case Groundwater quality model ap-
proach, these 8 parameters were divided in the four
categories on the basis of expert opinion having their im-
portance with respect to drinking water quality criteria.
The hydro chemical analyses revealed that water sam-
ples in the study area was characterized by hard to very
hard, fresh to brackish and alkaline in nature. The highly
turbid water may cause health risk as excessive turbidity
can protect pathogenic microorganisms from the effects
of disinfectants and also stimulate the growth of bacteria
during storage. Characteristic by pH values, most of the
water samples were alkaline in nature which are well
within permissible limit (6.5-8.5) and some of the sam-
ples have been found acceptable for usage and the ranges
are between 6.5 and 9.2 meeting with BIS standards of
IS: 10500:1991 and WHO (2006) guidelines. Based on
Electrical Conductivity (EC) values measured all water
samples Zone-I (Srirangam) are desirable (< 1 mS/cm)
for potability. Potability maps for the Pre monsoon and
post monsoon period is shown Figures 8(a), 8(b), 8(c),
9(a), 9(b) and 9(c) for pre monsoon & post monsoon
years 2006, 2007 and 2008 respectively.
5. Conclusions
The saying “ A picture is worth a thousand words” is true
for GIS applications in the field of human services as
visual maps on client communities and their needs as an
alternative to tables of numbers, charts or anecdotes not
only make information easier to grasp but also provide
more dimensions to study human service data. Customiz-
ed maps created using GIS software can help human ser-
vice professional to gain a better understanding of the
client communities they serve, as illustrated by the needs
assessment project. Apart from querying information on
spatial and non spatial data, print output of the maps at
user defined scales and extent, making of an integrated
analysis on spatial and non spatial data, performing que-
ry on multiple themes simultaneously are some of the
features of the health GIS model. It is difficult to under-
stand the issues related to epidemic diffusion simply by
groundwater quality analysis as it lacks spatial informa-
tion. Therefore, combination of both groundwater quality
parameters and GIS methods is very useful to researchers

N. V. KUMAR ET AL.
159
78.64 78.66 78.6878.778.72 78.74
78.64 78.66 78.6878.778.72 78.74
10.74 10.76 10.7810.810.82 10.84 10.86
10.7410.7610.7810.810.8210.8410.86
Potable water contour Map - premonsoon'06 - Tiruchirappalli City
Zone I
Zone IV
Zone II
Zone III
76 - 86
86 - 96
66 - 76
56 - 66
46 - 56
36 - 46
< 36
Legend
00.02 0.04 0.06 0.08
Longitude (in degree)
Latitude (in degree)
(a)
78.64 78.66 78.6878.778.72 78.74
78.64 78.66 78.6878.778.72 78.74
10.74 10.76 10.7810.810.82 10.84 10.86
10.7410.7610.7810.810.8210.8410.86
Potable water contour Map - premonsoon'07 - Tiruchirappalli City
Zone I
Zone IV
Zone II
Zone III
76 - 84
84 - 92
68 - 76
60 - 68
52 - 60
44 - 52
< 44
(b)
78.64 78.66 78.6878.778.72 78.74
78.64 78.66 78.6878.778.72 78.74
10.74 10.76 10.7810.810.82 10.8410.86
10.7410.7610.7810.810.8210.8410.86
Potable water contour Map - premonsoon'08 - Tiruchirappalli City
Zone I
Zone IV
Zone II
Zone III
82 - 86
> 86
78 - 82
74 - 78
70 - 74
66 - 70
< 66
Legend
00.020.04 0.06 0.08
Longitude (in degree)
Latitude (in deg
r
ee)
(c)
Figure 8. (a) Potability map of pre-monsoon 2006; (b) Potability map of pre-monsoon 2007; (c) Potability map of pre-
monsoon 2008.
to model the health related issues as GIS provides effi-
cient capacity to visualize the spatial data [19].
The quality of the groundwater of the Tiruchirappalli
city was monitored in 79 sampling wells for 3 years and
major recorded data revealed that the concentrations of
cations and anions were above the maximum, desirable
for human consumption. The Electrical Conductivity was
found to be the most significant parameter within input
parameters used in the modeling. The developed model
enabled well to test the data obtained from 79 samples of

N. V. KUMAR ET AL. 160
78.64 78.66 78.6878.778.72 78.74
78.64 78.66 78.6878.778.72 78.74
10.74 10.76 10.7810.810.82 10.84 10.86
10.7410.7610.7810.810.8210.8410.86
Potable water contour Map - posmon'06 - Tiruchirappalli City
Zone I
Zone IV
Zone II
Zone III
84 - 88
88 - 92
80 - 84
76 - 80
72 - 76
68 - 72
< 68
(a)
78.64 78.66 78.6878.778.72 78.74
78.64 78.66 78.6878.778.72 78.74
10.74 10.76 10.7810.810.82 10.84 10.86
10.7410.7610.7810.810.8210.8410.86
Potable water contour Map - postmonsoon'07 - Tiruchirappalli City
Zone I
Zone IV
Zone II
Zone III
78 - 85
85 - 92
71 - 78
64 - 71
57 - 64
43 - 57
< 43
(b)
78.64 78.66 78.6878.778.72 78.74
78.64 78.66 78.6878.778.72 78.74
10.74 10.7610.7810.810.82 10.8410.86
10.7410.7610.7810.810.8210.8410.86
Potable water contour Map - postmonsoon'08 - Tiruchirappalli City
Zone I
Zone IV
Zone II
Zone III
70 - 90
90 - 100
60 - 70
50 - 60
40 - 50
30 - 40
< 30
(c)
Figure 9. (a) Potability map of post-monsoon 2006; (b) Potability map of post-monsoon 2007; (c) Potability map of post-
monsoon 2008.
bore wells of Tiruchirappalli city.
 The groundwater in Tiruchirappalli meets all
WHO drinking water standards with in the range
of 67.5% to 92.5% for potable during pre mon-
soon condition of all the sampling durations.
 As the sampling station of 24 and 73 were found
in non potable condition due to vicinity of waste-
water discharging areas and solid waste dumping
sites.
 During post monsoon all the sampling stations

N. V. KUMAR ET AL.
161
satisfies WHO drinking water standards within in
the range of 67.5% to 92.5%.
 At the stations 59, 62 and 72 were reported with
non potablity of 32.5% due to unhealthy envi-
ronmental conditions of wastewater and local
waste dumps near by the sampling points.
 Solid wastes including sledges were disposed,
and without any pre treatment before dumping
and no protection towards the subsurface water
for potability.
 In the previous study by the authors the subsur-
face water quality in Ariyamangalam, Zone Ti-
ruchirappalli City Corporation was seriously un-
der threat by carbonates and sulphates near the
sampling points of Ariyamangalam zone. Also
contaminated by several pollutants as Ariyaman-
galam itself was currently polluted due to the
waste dumping site and improper waste water vi-
cinity.
 Without immediate response, the subsurface wa-
ter is currently degrading its consumption quan-
tity and will not be potable in near future if the
proper steps have not been taken care.
6. Acknowledgements
The first author gratefully acknowledges the support by
Fellowship under TEQIP, MHRD, and Govt. of India for
his Doctoral study. The authors wish to express their
sincere appreciation to the anonymous reviewers for their
valuable comments to enhance the quality of this paper.
Special thanks to Jay Krishna Thakur, Institute of Geo-
sciences, Martin Luther University, Halle (Saale), Ger-
many and Sundarambal Palani, Tropical Marine Science
Institute, National University of Singapore, Singapore
for their constant support in preparation of this manu-
script. Special thanks go to M. Kannan (SASTRA) in
GIS work, field activities and in the laboratory meas-
urement of this study. Sincere thanks to Prof. K. Pala-
nichamy, Civil Engineering Department, National Insti-
tute of Technology, Tiruchirappalli, India for providing
constant encouragement throughout his research study.
The authors wish to express their sincere thanks to the
anonymous reviewers for their valuable comments to
enhance the quality of this paper.
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