GIS and Landfill

8/11/2019 GIS and Landfill

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Int. Journal of Advances in Remote Sensing and GIS, Vol. 1, No. 2, 2012 www.jrsgis.com

Copyright Javeed Ahmad Rather et al [email protected]

Research article ISSN 2277 9450

---------------------------- 218

*Corresponding author (email: [email protected])

Received on August 2012; Accepted on September 2012; Published on November, 2012

Fuzzy Logic Based GIS Modeling for Identification of Ground

Water Potential Zones in the Jhagrabaria Watershed of Allahabad

District, Uttar Pradesh, India

Javeed Ahmad Rather1, Zameer AB Raouf Andrabi2

1Assistant Professor, Department of Geography, University of Kashmir, Srinagar, J & K2Research Scholar, Department of Geography, J.M.I. University, New Delhi-110025

Abstract:Water is the most vital requirement for life supporting system to mankind. Within the hydrologic

cycle, groundwater represents a major portion of the earths water circulatory system. Groundwater is an

important resource required for drinking, irrigation and industrialization purpose. Remote sensing and GISnow-a-days have become inevitable tools for the analysis of groundwater at local, regional and global level.

Fuzzy logic based concepts have found a very wide range of applications in different fields viz. soil

science, environmental science, earth science etc. Fuzzy Logic provides a very precise approach for dealing

with uncertainty which grows out of the complexity of human behaviour. The fuzzy membership functions

assessed for overlay maps were mainly extracted from the field data. The benefit is that they dont need to

conduct a new analysis, or change the rules, or the criteria, which saves time and effort. In fuzzy systems,

values are indicated by a number (called a truth value) in the range from 0 to 1, where 0.0 represents

absolute falseness and 1.0 represents absolute truth. While this range evokes the idea of probability, fuzzy

logic and fuzzy sets operate quite differently from probability. The research paper is planned to develop a

fuzzy logic based methodology for groundwater potential mapping for the study area. The study area is

covered by hard rock formations and faces acute water scarcity problem both for irrigation as well as for

drinking purposes. To demonstrate the efficiency of the GIS for groundwater study, the specific objective of

this study is to develop a spatial model using remote sensing and fuzzy techniques under GIS environment

to predict groundwater potential zones.

Keywords: Ground Water; Potential Zones; Watershed; Catchment area; Spatial model.

1. Introduction

Groundwater is a precious and the most widely distributed resource of the earth. It constitutes an important

source of water supply for various purposes, such as domestic, industrial and agricultural needs. In the

hydrological cycle, groundwater occurs when surface water (rainfall) seeps to a greater depth filling the

spaces between particles of soil or sediment or the fractures within rock. Groundwater flows very slowly in

the subsurface towards points of discharge, including wells, springs, rivers, lakes, and the ocean. It is the

largest available source of fresh water lying beneath the ground. It has become crucial not only for

targeting of groundwater potential zones, but also monitoring and conserving this important resource. The

expenditure and labour incurred in developing surface water is much more compared to groundwater,

hence more emphasis is placed on the utilization of groundwater which can be developed within a short

time. Besides targeting groundwater it is also important to identify suitable potential zones. Amongst high


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Fuzzy Logic Based GIS Modeling for Identification of Ground Water Potential Zones in the Jhagrabaria Watershed of

Allahabad District, Uttar Pradesh, India

Javeed Ahmad Rather, Zameer AB Raouf Andrabi

Int. Journal of Advances in Remote Sensing and GIS, Vol. 1, No. 2, 2012219

resolution satellite images are increasingly used in groundwater exploration because of their utility in

identifying various ground features, which may serve as direct indicators of presence of ground water

(Krishanmurthy, et al., 1996; Das et al., 1997; Pratap, et al., 2000; Sankar, 2002; Bahuguna, et al., 2003;

Jagadeeswara Rao, et al., 2004; Ratnakar Dhakate, et al., 2008). Indirect analysis of some directly

observable terrain features like geological structures, geomorphology and their hydrologic characteristics

using remote sensing enables to target groundwater (Basudeo Rai, et al., 2005; Lokesha, et al., 2005;

Samuel Corgne, et al., 2010 ).The geographic information system (GIS) has emerged as a powerful tool in

integration and analysis of multi thematic layers in delineating ground water prospect and deficit zones

(Carver, 1991; Hoogendoorn Goyal, et al., 1993; Rokade, et al., 2007, Thushan Chandrasiri Ekneligoda

and HerbertHenkel, 2010).

Remote sensing with its advantages of spatial, spectral and temporal availability of data covering large and

inaccessible areas within short time has become a very handy tool in assessing, monitoring and conserving

groundwater resources. Numerous advances in remote sensing by satellites have helped in delineating

water bearing entities such as fracture zones, springs and to a lesser extent aquifers. One of the most

significant tools developed in this century for the study of groundwater has been the digital computer. As a

consequence, numerical modeling of groundwater flow and contaminant transport has become a routine

effort in nearly all groundwater studies. The various thematic layers generated using satellite data provides

quick and useful baseline information on the parameters controlling the occurrence and movement of

groundwater like landuse / landcover, lineaments etc. These maps can be integrated with geology,

lithology, geomorphology, soils, slope, drainage and other collateral data in a Geographic Information

System (GIS) framework and can be analyzed by using logical conditions to derive groundwater potential

zones.

The study area Shankargarh block of Allahabad district, U.P. is a drought prone area and lacks adequate

water supply. The block is mainly rocky and is not capable to hold ample groundwater covered by hard

rock formations, facing acute water scarcity problem both for irrigation as well as for drinking purposes.

The groundwater in the area is confined to secondary permeable structures i.e. fractured and weathered

horizons and in the upper unconsolidated materials. The traditional methods of searching sites for drilling

of bore wells have not only a poor success rate but even the places where such efforts have succeeded, the

bore wells are known to dry up in a short period of time. Inclusion of subsurface information inferred from

the bore well data can give more realistic picture of groundwater potentiality of an area.

The present study attempts to delineate suitable locations for groundwater exploration using integrated

approach of remote sensing, bore well and GIS techniques. ArcGIS 8.3 and ERDAS Imagine 8.5 software

have been used for the generation and analysis of the thematic layers, such as- geomorphology, geology,

lineament, slope, soil and landuse / landcover, which are assigned fuzzy membership values according to

their relative contribution towards the groundwater. Finally, the layers were classified and prepared with

respect to main criteria and parameters. The fuzzy operators such as Fuzzy Product, Fuzzy Sum and Fuzzy

gamma are used for factor maps integration. The final water potential map generated has been classified

into six categories such as - excellent, very good, good, moderate, poor, and very poor based on the fuzzy

number obtained from map integration. The gamma value of 0.85 yielded the most reliable picture of

groundwater conditions in the study area. Villages of the study area with excellent ground water potentialhave also been identified on the basis of fuzzy analysis.


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2. Objectives of the Study

The main objectives of this work are

1. To create integrated GIS based geographic database for Jhagarbaria watershed of Shankargarh

block.

2. To generate landuse / landcover map through digital processing of remotely sensed data of IRS-1D

LISS-III sensor and correlate it with changes in ground water resources over the area.

3. To classify and prepare data layers with respect to main criteria and parameters.

4. To develop and implement the fuzzy logic based spatial model for prediction of groundwater

potential zones.

2.1 The study area

The study area is situated in Allahabad district of Uttar Pradesh State, India and is bounded by latitudes of

25012/N to 25020/ N and longitudes 81033/ E to 81044/ E falling in SOI topographical maps 63 G/11 and 63

G/12 which is shown in figure 1. Geologically the area comprises of upper Vindhayan formations

consisting of mainly sandstone and shale. Shankargarh block lies 45 km to southwest of the Allahabad

district on the Allahabad-Banda road and is situated on the bank of river Yamuna. Shankargarh is mainly

famous for silica, sand, quarry and washeries and is well connected by road and railway. Shankargarh

shows a nearly flat to a gently undulating topography with small hillocks. The minimum and maximum

elevations of this area are 90 m and 180 m above mean sea level respectively. Some portion of the area is

flat and showing very gentle slope.


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Figure 1:Study area map

Figure 2:Topographic Map of the Study Area Figure 3: Remotely Sensed data of LISS-III of IRS-ID

2.2 Data Sources

In order to demarcate the groundwater potential zones in study area different thematic maps were prepared

from remote sensing data, topographic maps and bore welldata. The thematic map of landuse/landcover

and lineaments were prepared using IRS-1D LISS-III data by visual interpretation. Drainage and contour

maps were prepared from Survey of India toposheet no. 63G/ 11 and 63G/ 12 of 1:50000 scale. Geology

map was collected from Geological Survey of India (GSI) and Soil map was collected from Soil

Department of Allahabad (U.P). All the primary input maps (Geomorphology, Geology, Physiography,

Lithology, Lineament, Contour, Drainage and Water body) were digitized in ArcGIS 8.3 and Erdas Imagine

8.5. Slope map was prepared from digital elevation data. Data on existing ground water conditions for

bore wells, open wells and hand pumps were collected from the C.G.W.B. (Central Ground Water Board).

2.3 Methodology

In this paper GIS and Remote Sensing techniques have successfully been implemented for the zonation of

the ground water potential areas. For the above said benefit different data layers in the form of thematic

maps were combined together by the fuzzy logic theory and the final maps were prepared. Variousdepartments were visited for the collection of raw data for the analysis work. The figure below illustrates

the methodology adopted for the present work.

3. Method of Fuzzy Logic Implementation

The fuzzy approach, which enables handling of vague information, is regarded by experts as the most

realistic description. This research therefore focuses on development of fuzzy groundwater model. It must

be, however, emphasized, that in this research the fuzzy groundwater model is being developed for specific

purpose, which is supporting spatial analysis in different fields. Thus, the result will be new map layer,

containing fuzzy membership values to particular groundwater polygons from the original map. Fuzzy


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membership values are indicators of the uncertainty in the map. The improvement of the soil maps by

qualifying the uncertainty enables the reliability assessment of the analysis and leads to better usage of the

maps. The assessment of fuzzy membership values is crucial to proper fuzzy model (Ebadi, S.,

Valadanzoej, J., and Vafacinezhad, A., 2001). Because of the complexity of the problem, an expert system

for simulating decisions of geologists is developed. Rules defined with the help of experts depict the most

typical cases and are often expressed in linguistic terms. In order to handle all possible situations in the

reality, means of fuzzy expert systems are utilized Kremenova, Olga, 2004). The groundwater mapping

consists in location of groundwater polygon boundaries. The uncertainty is therefore mainly caused by

difficulties to assign the different thematic maps in the transition zone and to locate the boundary between

groundwater potential zones.

Figure 4: Methodology of the work

In classical set theory, the membership of a set is defined as true or false, 1 or 0. Membership of a Fuzzy

set, however is expressed on a continuous scale from 1 (full membership) to 0 (full non-membership).Very

high values of Fuzzy membership of 1; very low values at or below background have a fuzzy membership

of zero; between these extremes a range of possible membership values exist. Every value of x is

associated with a value of (x), and the ordered pairs [x, (x)] are collectively known as a Fuzzy Set. The

shape of the function need not be linear, it can take on any analytical or arbitrary shape appropriate to the

problem at hand. Fuzzy membership functions can also be expressed as lists or tables of numbers. The

classes of any map can be associated with fuzzy membership values in an attribute table. The level of


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measurement of the mapped variable can be categorical, ordinal or interval. Fuzzy membership values

must lie in the range (0, 1), but there are no practical constraints on the choice of Fuzzy membership

values. Values are chosen to reflect the degree of membership of a set, based on subjective judgment.

Values need not increase or decrease monotonically with the class number.

3.1 Combining Fuzzy membership functions

Given two or more maps with fuzzy membership functions for the same set, a variety of operators can be

employed to combine the membership values together. Zimmermann in 1985 discusses variety of

combination rules. It has been discussed that five operators were found to be useful for combining

exploration datasets, namely the fuzzy AND, fuzzy OR, Fuzzy algebraic product, Fuzzy algebraic sum and

Fuzzy gamma operator (Tangestani, Majad. H., 2001).

These operators are briefly discussed below:

Fuzzy AND: This is equivalent to a Boolean AND (logical intersection) operation on classical set values

combination =MIN (A, B,N)

Fuzzy OR: This is equivalent to a Boolean OR (logical union) on classical set values

combination =MAX (A,B,..N)

Fuzzy Algebraic Product: The combined membership function is defined as

1

n

c o m b i n a t i o n i

i

=

=

where i is the fuzzy membership function for the I thmap, i= 1, 2, 3., n maps are to be combined. The

combined Fuzzy membership values tend to be very small with this operator, due to the effect of

multiplying several numbers less than 1. Nevertheless, all the contributing membership values have an

effect on the result, unlike the Fuzzy AND or Fuzzy OR operators.

Fuzzy Algebraic Sum: This Operator is complementary to the Fuzzy product, being defined as

( )1

1 1n

c o m b in a tio n i

i

=

=

The result is always larger (or equal to) the largest contributing fuzzy membership value. The effect is

therefore increasive. The increasive effect of combining several favorable pieces of evidence is

automatically limited by the maximum value of 1.0. Fuzzy algebraic product is an algebraic product but

Fuzzy algebraic sum is not an algebraic summation.

Gamma Operation:

This is defined in terms of the fuzzy algebraic product and the Fuzzy algebraic sum by the representation:-

combination = (FUZZY ALGEBRAIC SUM)*(FUZZY ALGEBRAIC PRODUCT)(1-)

Where, is a parameter chosen in the range (0 , 1). When is 1 the combination is same as the Fuzzy

algebraic sum, and when is 0 the combination is equal to the Fuzzy algebraic product. Judicious choice of

the produces output values that ensure a flexible compromise between the increasing tendencies of the


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Fuzzy algebraic sum and the decreasing effects of the Fuzzy algebraic product where is a parameter

chosen in the range (0 ,1). When is 1, the combination is the same as the fuzzy algebraic sum; and when

is 0, the combination equals the fuzzy algebraic product. Judicious chose of produces output values that

ensure a flexible compromise between the increasive tendencies of the fuzzy algebraic sum and the

decreasive effect of the fuzzy algebraic product. For example, if = 0.7, then the combination of (0.75,

0.5) is 0.875 0.7 * 0.375 0.3= 0.679, a result that lies between 0.75 and 0.5. On the other hand, if = 0.95

then the combination is 0.839, a mildly increasive result. If = 0.1, then the combination is 0.408, a result

that is less than the average of the two input function, and therefore decreasive. The effects of choosing

different values of are shown. Note that although the same tendencies occur, the actual value of for

which the combined membership function increasive or depressive vary with the input membership values

(Elias, K. M. Mohammed, 2003).

3.2 Fuzzy membership function and ranking assigned to thematic classes

The Fuzzy Membership has been assigned to the different thematic maps according to their classificationon the respect of ground water contribution. Different classes have been given the weightage by the

different experts. All the expert weightage has been converted in the fuzzy membership value according to

their ranks within the range of 0-1 (Delft, 2000,) .The following relief structures have been taken into

consideration in the present study.

3.2.1 Geology

It is another important aspect for the ground water delineation mapping. Geology of the study area contains

khader (younger alluvium), bhager (older alluvium), kaimur sandstone, kaimur sandstone with bijagarh

shale and colluvium with bijagarh shale. The attribute table of geology is defined below and the weighted

map of the geology is shown in figure 4. The weights have been assigned to these geological formations

and ranking according to the ground water prospect.

Table 1: Attributes of the Geology

Formation Fuzzy Number Ranking Area(km2 )

Khader (Younger

Alluvium),

0.74 Excellent 11.38

Bhager (Older Alluvium) 0.71 VeryGood

29.07

Colluvium with Bijagarhshale

0.33 Good 56.24

Kaimur sandstone with

Bijagarh shale

0.36 Moderate 50.06

Kaimur sandstone 0.66 Poor 8.08

The younger alluvium is ranked excellent because it has a very good percolation capacity of water due to

the presence of alluvial soils. Colluvium with bijagarh shale has been ranked good because it has more

porosity and permeability in respect to the ground water prospect. Kaimur sandstone has been ranked poor

because it is porous but not permeable.


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3.2.2 Geomorphology

The geomorphology of the study area contains alluvial plain, recent and older flood plain, denudational

hills, buried pediments, abandoned meandering channel and stony wasteland. The attribute table of

geomorphology is shown in the following table.

Table 2: Attributes of Geomorphology

Formation Fuzzy Number Ranking Area (Km2 )

Alluvial Plain 0.78 Excellent 29.07

Recent Flood Plain 0.71 Very Good 0.079

Older Flood Plain 0.75 Moderate 11.306

Buried Pediments 0.69 Good 54.266

Denudational Hill/Hillocks 0.18 Poor 53.87

Stony Wasteland 0.10 Very Poor 4.27

In these features the Alluvial plain assigned a high weight because the ground water prospect are higher

towards these areas .The second weight assigned to the flood plain and the other features are assigned to the

categories relative towards the ground water prospect. Alluvial plain is ranked excellent because this

feature has good capacity of percolation of water. Recent flood plain is ranked very good due to collection

and presence of water and the rate of percolation are better than other feature. The buried pediments and

abondoned meandering channels are ranked good because these features are marginal to the recent flood

plain and the rate of percolation of water is less than alluvial plain recent and older flood plain.The

denudational hill and stony wastelands are ranked poor and very poor due to very less capacity of

percolation of water.

3.2.3 Slope

It is another important aspect for ground water conditions of any area. In the study area of Jhagrabaria

watershed we can categorize the slope in Level (00-20), Gentle (20-50), Moderate (50-150) and Steep (150-

300) slope categories. The slope in our study area ranges from 0-15 so the classes for the slope are three.

The attributes for the slope are given in Table 3.

Table 3: Attributes of Slope

Class Fuzzy Number Ranking

Level (00 - 20) 0.85 Excellent

Gentle (20 -50)

0.75 Good

Moderate (50 -

100)

0.56 Moderate

Steep (100 -

150)

0.42 Poor

Very Steep (< 0.20 Very Poor


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- 150)

3.2.4 Landuse / Landcover

The landuse effectively plays an important role in the ground water prospect mapping. So the parameters

that are directly and indirectly affecting the ground water occurrence and movement are sandstone,

settlement, scrub, water, forest, vegetation (Agricultural) and sand. These parameters are mentioned in the

following table.

Table 4:Attributes of Landuse / Landcover

Landuse/ Landcover Fuzzy Number Rank Area(Km2 )

Forest 0.44 Excellent 8.6

Vegetation (Agricultural) 0.47 Very Good 60.33

Water 0.42 Excellent 3.82

Settlement 0.39 Good 1.64

Scrub 0.48 Moderate 32.79Loose Sand 0.80 Very Poor 39.4

Stony Wasteland 0.41 Poor 8.80

The forest and water are ranked excellent because the runoff water is slow and high percolation due to the

presence of trees and water. The vegetation and agriculture have the good percolation capacity of water so

it has been ranked in very good category. These are present in sufficient amount covering the study area.

The open scrub is ranked moderate because the surface is undulating. Sandstone is ranked very poor

because there is no possibility of holding the water.

3.2.5 Soil

The soil is a basic natural resource of agricultural production of any region. Apart from providing stability

to roots and stems of plants, soil also acts as reservoir of plant-nutrient which is provided to them in the

form of watery solution. According to the ground water prospect the soil plays an important role in the

ground water percolation and holding capacity.

Table 5: Weight and ranking of Soil

Soil Type Fuzzy Number Rank Area(Km2 )

Newaria Loamy Soil 0.32 Excellent 60.95Dewaria Clayey Soil 0.13 Very Good 33.06

Lohgara Silty Loam 0.45 Moderate 20.82

Jarkhori Sandy Loam 0.60 Good 27.89

Stonyland 0.03 Poor 14.91

The soils in the study area are distributed as regionally and have the broad range of local units. These are

the jarkhori sandy loam, lohgara silty loam, newaria loam, dewaria clayey loam and stony land that lie in

the study area. The attributes of the soil are described in Table 5. Newaria loam has the deepest to good

percolation with the subtle weathering capability and 0-1% slope thus here we gave the highest weight and

rank for the following class. Dewaria clayey loam covers the largest part of the study area and the least toaverage percolation with the little weathering capability and 3-5 % slope so, here we gave the next


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category to the following soil type. Jarkhori sandy loam has the deepest water percolation with the less

weathering capability and 0-1% slope so here we give the moderate category for the following class. Stony

land has no importance over soil so here we have given the least weightage to that category.

3.2.6 Lineament

Lineament analysis for ground water exploration in vindhyan formations has considerable importance as

joints and fractures serve as conduits for movement of groundwater. It is not practical to map lineaments

solely on the basis of satellite data without a thorough knowledge of the structural conditions in an area. In

this study, lineaments derived from satellite data have been carefully matched with previously mapped

structural features. The attribute and weight of the lineament is given below.

Table 6:Attributes of Lineament

Lineament Type FuzzyNumber

Ranking Area(km2 )

200 m buffer zone of major

lineament

0.76 Excellent 4.31

200 m buffer zone of minorlineament

0.64 Excellent 1.08

Ex-lineament 0.20 Poor 149.98

The 200 m buffer zone of major lineament is ranked excellent because it is the area which has more

percolation of water. The 200 m buffer zone of minor lineament is ranked very good. The ex-lineament is

ranked poor because of no significance of lineament.

3.2.7 Physiography

Physiographical analysis for ground water exploration has been considered as an important factor which

plays a vital role. This map is generated with the help of the base map and satellite data. Physiographical

study provides enough assistance for ground water findings.

Table 7: Attributes of Physiography

Physiography Type Fuzzy Number Rankings

Northern Vindhayan

Uplands

0.34 Poor

Plain of Older Alluvium 0.77 Excellent

Riverine Ridges of

Yamuna

0.44 Moderate

3.2.8 Drainage

Drainage affects the ground water at any place. In the study area the drainage pattern shows that most of

the area is covered by perennial, semi perennial as well as streams. Most of the drainage flow is runningfrom North to East direction and forms the main river called the Jhagrabaria.


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Table 8: Attributes of drainage

Drainage Order Fuzzy Number Rankings

First Order 0.07 Poor

Second Order 0.52 Good

Third Order 0.67 Very Good

Fourth Order 0.87 Excellent

Figure 5: Geological Map of the Study Area Figure 6: Drainage Map of the Study Area

Figure: 7 (a), (b), (c), (d): (a) Geomorphological map of the study area (b) Lithological map of the study


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area (c) Physiographic map of the study area (d) Hydro-Geomorphology map of the study area

Figure 8(a), (b), (c), (d): (a) Slope Map of the Study Area (b) Landuse/Landcover Map of the Study Area

(c) Laplacian Filter operation of the Study Area (d) Lineament Map of the Study Area

The different polygons in the thematic layers were labeled separately and suitable fuzzy membership

function value has been assigned and then were overlaid with each other. The overlaying of the map is

done by the union of two different thematic maps at a time. The maps selected to be overlaid to form the

first union was on the basis of their maximum influence amongst themselves for the objective. The map

thus obtained is having not only the attributes summed up but also the Fuzzy memberships assigned to

each of them. By repeating this process for all the thematic layers a set of four layers were obtained, which

contained a combined Fuzzy weight assigned to them in each thematic layers and calculated by the applied

formula. The next overlaying was between the four maps obtained after combination in pairs. Theoverlaying process by union of two maps continues until the final map is generated which is the result of

union of all the thematic maps. The fuzzy membership functions of the thematic layers are combined using

the FUZZY GAMMA operation, shown below, to yield the desired water potential zone map.

combination = (FUZZY ALGEBRAIC SUM)*(FUZZY ALGEBRAIC PRODUCT)(1-)

The Fuzzy logic applied for the calculation of Fuzzy number, has two way of representation: Increasing

and Decreasing effects. After generating different final map on the basis of gamma value, the map

generated by the gamma value 0.85 has been used to give most closely resemble of the ground truth. Thus

the use of final map of Gamma value 0.85 was finalized by the concern of expert and ground realities. In

the final thematic layer initially each one of the polygons were qualitatively visualized into one of the


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categories like (i) very good (ii) good (iii) moderate and (iv) poor in terms of their importance with respect

to groundwater occurrence. Finally thematic layers were integrated and analyzed, using fuzzy method. The

grids in the integrated layer were grouped into different ground water potential zones by a suitable logical

reasoning and conditioning. Final generated map of ground water potential zones was verified with the

borewell data to ascertain the validity of the map developed.

4. Results and discussion

In the present work, the delineation of groundwater prospect zones has been made by grouping the fuzzy

membership values of the integrated layer into different prospect zones for obtaining six categories, viz.

excellent, very good, good, moderate, poor and very poor. The final ground water prospect map is shown

in figure 15. Groundwater potential map generated by integrating lithology, geomorphology, geology,

lineament, soil and slope gives the more realistic picture. The area of each potential zone is compiled in

Table 9.

Table 9:Ground water prospect zone with area

S. No. Zone Fuzzy Number Area (KM2)

1 Excellent 0.94 0.96 19.438

2 Very Good 0.91 0.94 23.419

3 Good 0.87 0.91 30.941

4 Moderate 0.81 0.87 47.494

5 Poor 0.76 0.81 22.646

6 Very Poor 0.61 0.76 21.111

Total Area (KM2) 165.048

It shows the upper and lower limits of the weights assigned for ground water prospect and provides a broad

idea about the ground water potentiality of the study area. The upper and lower weight values are

aggregated to classify the potential map by weight values. The fuzzy membership values obtained are

classified in six different classes based on the fuzzy number derived from the overlay operations carried

out for the purpose of ground water potential zoning. The above table explains that the excellent class

ranges from 0.94 - 0.96 while the area covered by this category is 19.438 km2. The range is bifurcated in

this category as the value of gamma taken for the overlay analysis is 0.85 and the range of excellent zone

should be much above the gamma value and very close to the value of maximum membership. The very

good category ranges from the 0.91 - 0.94 and the area covered under this category is 23.419 km2. The

range of this zone is also greater than 0.85, again with the same concept of membership. The weight valuefor the good category is the 0.87 - 0.91 and the area covered by this category is 30.941 km 2. The good

category potential zone is just above the gamma value taken for the overlay analysis. The moderate

category has the fuzzy number ranging between 0.81 - 0.87 and the area under this category is 47.494 km2.

This category zone has fuzzy number in the range of the gamma value so it has been consider as moderate

category. It can also be inferred from the above table that fuzzy number for poor category varies from 0.76

- 0.81 and the area under this category is 22.646 km2. This particular range of water potential zone has just

less fuzzy membership value from considered gamma value. The last category zone for the water potential

map is the very poor zone and has the fuzzy number extremely low i.e., between 0.61 - 0.76 and the area

under this category is 21.11 km2. Further, twenty nine villages which are lying in the zone of excellent

ground water potential were identified and were found to be Amilia tarhar, Barhula, Basahara tarhar,


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Belamundi, Birwar, Chak Antri Pachwar, Chhatahara Ghuretha, Chhiri, Chilla Gauhani, Ghuri, Gidhar,

Gohra tarhar, Goisara, Gudhi Majhiari, Ichhaura, Jagdishpur, Kachara, Magra chaube, Mahera, Manpur,

Misirpur, Nagara, Nagarwar, Nagnapur, Othgi tarhar, Pachwar, Purakinner, Sonari and Sujauna. The

village level map having excellent groundwater potential has been generated and is shown in figure 16.

Figure 15:Groundwater zonation map of the study

5. Conclusion

The present study demonstrates the capabilities of remote sensing, GIS and fuzzy logic for demarcation of

different groundwater potential zones which may be used for groundwater development and management

programmes. Based upon the analysis of results, the following conclusions can be made. GIS technology

used in the present work is found to be suitable for the development of the ground water potential zonation.

Remote sensing techniques used in the present work is found to be suitable for generating the landuse/

landcover map through digital processing of LISS-III data of IRS-1D satellite which can be effectively

used and integrated under GIS environment for ground water investigation studies.

Twenty nine villages have been identified in the excellent ground water potential zone of the study area by

integrating the thematic layers viz. drainage, slope, lineament, lithology, physiography, landuse / land

cover, geology, geomorphology, soil and water body maps on the basis of fuzzy gamma operation analysis.

The study area is a good example of complex geological and geomorphological structures. Shankargarh

faces a water scarcity for about half of a year and groundwater is the only substitute water resource. Proper

zoning and estimation of the aquifers present in the study area will bring new lease of life in the area. The

fuzzy logic technique applied for the overlay analysis in the present work is found to be suitable to predict

the ground water potential zones in the region under GIS environment. The present fuzzy modeling

technique can effectively be applied in other regions for the generation and prediction of the ground water

potential of that site.

Figure 16:Excellent groundwater villages of

the study


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GIS and Landfill