MULTICRITERIA DECISION MAKING AHP BASED GROUNDWATER ... · Remote sensing data combined with...

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MULTICRITERIA DECISION MAKING AHP BASED

GROUNDWATER POTENTIAL MAPPING FOR

GUMMIDIPOONDI DISTRICT

Dr. S. Vidhya Lakshmi Y. Vinay Kumar Reddy

Associate Professor Undergraduate Student

Department of Civil Engineering Department of Civil Engineering

Saveetha School of Engineering Saveetha School of Engineering

[email protected] [email protected]

Saveetha Institute of Medical and Technical Sciences

Chennai-602105

ABSTRACT

Groundwater is the most important resource that demands all purposes in daily life.

Due to the increase in population there may be increase in demand of water. The main

objective of the study is to identify the groundwater potential zones using Geographical

Information System (GIS) and Remote Sensing (RS). Analytical Hierarchical process (AHP)

method is used to delineate groundwater zones. Various thematic layers such as Geology,

Geomorphology, Soil, Land Use and Land cover (LULC), Slope, Aspect and rainfall maps

were prepared. The weights are assigned to each thematic layer in AHP method and the final

groundwater map was prepared. Therefore the final groundwater map was classified into five

classes: very poor, poor, moderate, high, very high groundwater potential zones. The derived

groundwater map was overlaid with lineament density map for the validation.

Keywords: GIS, Remote Sensing, Groundwater zones, lineament density, Soil.

1. INTRODUCTION

Groundwater is the source for irrigation and domestic purpose. In which 80% of the

rural areas are use groundwater for domestic purpose and 50% of the urban areas use the

groundwater for domestic purpose. Due to more dependent on usage of groundwater for

domestic purpose and irrigation and for other sectors may results in exploitation of

groundwater resources (Shakak, 2015). The exploitation and exploration of groundwater

resources needs to understanding geology, geomorphology of that area. The data and

thematic maps such as satellite images, soil data, geology data, geomorphology data and

International Journal of Pure and Applied MathematicsVolume 119 No. 17 2018, 41-58ISSN: 1314-3395 (on-line version)url: http://www.acadpubl.eu/hub/Special Issue http://www.acadpubl.eu/hub/

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rainfall data, are helpful for mapping of groundwater potential zones(Giri & Bharadwaj,

2012).

Groundwater recharge demonstrates the collection of water from the retarded area

below the water table surface, composed with the connected flow from the water table within

the permeable region. Groundwater recharge happens when water flows previous the

groundwater level and penetrates into the drenched region. The results from different

thematic layers helps to identify the potential zones. Soil map helps to identify the soil types

and the infiltration capacity, geological map helps to understand different layer of geology,

land use and land cover map helps to identify different classes in the region of interest,

rainfall map helps to find the availability of water, slope map helps to understand the

direction of flow of water.

Water is one of the most essential commodities for mankind and the largest available source

of fresh water lays underground. It is one of the most significant natural resources which

support both human needs and economic development. Demand of increase in the

agricultural, industrial and domestic activities in recent years has increased the demand for

groundwater. The occurrence of groundwater at any place on the earth is not a matter of

chance but a consequence of the interaction of the climatic, geological, hydrological,

physiographical and ecological factor. The movement of groundwater is controlled mainly by

porosity and permeability of the surface and underlying lithology. The same lithology

forming different geomorphic units will have variable porosity and permeability thereby

causing changes in the potential of groundwater.

2.REMOTE SENSING AND GIS TECHNIQUES

Remote sensing and GIS plays a vital role in developing of water and land resources

management. The advantage of using remote sensing is to develop information on spatial

technology which is useful for analysis and evaluation (A. Sciences, 2017).Remote Sensing is

the science of acquiring information about the earth surface without being contact with it.

This is done by sensing, recording, analysing and applying the information. GIS is a

collection of computer hardware, software and geographic data for capturing, storing,

analyzing, and manipulating data for geographical information (Tiwari & Shukla, 2015). For

getting the soil, land use and land cover, geology, geomorphology, rainfall, drainage density

data high resolution satellite images are taken for mapping of groundwater zones (E.

International Journal of Pure and Applied Mathematics Special Issue

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Sciences, 2013). National Remote Sensing Agency (NRSA) was first identify the remote

sensing and GIS information for mapping of groundwater potential zones. GIS technique is

used to classify the results of remote sensing, assign the appropriate weights to the related

maps.

3. DESCRIPTION OF THE STUDY AREA

The Selected study region of interest is based on E-waste dumping site in Gummidipoondi,

Tamil Nadu. Gummidipoondi is located in Tiruvallur District, Tamil Nadu with population

of 32665. The study region is located between the latitude of 13.4327° N and longitude of

80.1062° E.

Figure 1. Location of study region in tiruvallur District, with study site obtained from Google maps

The above map fig no.1 shows that the location of the study area where groundwater potential zones

have to identify located in Gummidipoondi, tiruvallur district.

4. LITERATURE SURVEY

Remote sensing data combined with Geographical Information System (GIS) technique is

very efficient in identification of groundwater potential of any region. The study results that

the integration of thematic maps prepared from conventional and remote sensing techniques

using GIS gives more and accurate results (Jose et al., 2012). Groundwater is available when

water infiltrates below the earth surface and soil beneath the earth surface is porous (Sayeed,


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Hasan, Hasnat, & Kumar, 2017). Groundwater table reduces when pumping rate is more than

the rate of usage. Hence, it can be concluded that areas of high withdrawal rates may lead to

reduction of groundwater zones. This may lead to reduced water level in wells, lakes and

streams (Senanayake, Dissanayake, Mayadunna, & Weerasekera, 2016).

Remote sensing is one of the major source for surface feature information of

groundwater such as land use, land forms and drainage density. These data can be easily

input in GIS to identify the groundwater zones (Oh, Kim, Choi, Park, & Lee, 2011).

According to World Bank report, India may be in water stress zone by 2025 and water scarce

zone by 2050 [give reference]. This is due to improper education in groundwater exploitation,

improper maintenance of water, failure of government schemes for rural areas may lead to

groundwater and drinking water problems in India. The advantage of GIS and remote sensing

of spatial, spectral and manipulation of earth surface and subsurface data cover with a short

time having a great groundwater potential for accessing, processing and monitoring the

groundwater resources. The conventional methods such as geophysical resistivity surveys,

field based hydrogeological are time consuming methods and very cost effective (Ahmed,

2016).

Ground water is the subsurface water which fills the pore space and geological

formation under the water table. The water flows through the aquifer towards the point of

discharge that includes wells, oceans, lakes etc. In the world scenario there may be 60% of

groundwater in which there may be 0.6% of fresh water. The use of remote sensing and GIS

techniques increases for identifying groundwater zones gives with accurate results. Many

methods are available for mapping of potential zones such as Weighted Linear Combination

(WLC) (Vijith 2007;Madrucci et al. 2008; Dar et al. 2011), Analytical Hierarchical Process

(AHP) (Chowdhury et al. 2009; Pradhan 2009), and Index Overlay Method (Muthikrishnan

and Manjunatha 2008).

5. METHODOLOGY

Indian Remote Sensing system (IRS 1D) are used to create the land use map. The

Geology map was prepared from the Geological survey of India district resource map. The

maps were prepared within a scale of 1: 20000. The soil map and geomorphological map


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were prepared from district resource map. The analyses of the maps were prepared with the

QGIS 2.6.0 software. The rainfall data map was prepared using the minimum and maximum

values of the rainfall data for the two years and digitized using QGIS 2.6.0. The integration

method of analytical hierarchical process is used to assign the weights of each thematic layers

and classified the groundwater map into different classes.

While using the Analytical hierarchical method, firstly all the layers such as geology,

geomorphology, soil, rainfall, LULC are opened. The available layers should be TIF file.

Then using AHP plugin all the raster layers are added, in first step of pairwise comparison

matrix the values are given for each layer with different layers and calculate the values. If the

value obtained is greater than 0.1 then repeat the values. In second step of weighted linear

combination the weights are obtained. The obtained weights for each layer are based on the

factors of each feature. Then final step is to run the AHP method the output will generated.

Then in layer properties in band rendering the render type is selected as single band pseudo

colour and dividing into equal intervals with five classes. Finally classification is done. For

the various geomorphic units, weight factors are decided based on their capability to store

groundwater. This procedure is repeated for all the other layers and resultant layers are

reclassified. The groundwater potential zones are classified into five categories like very

poor, poor, moderate, good & excellent.

Analytical hierarchical process analysis different datasets into a pairwise matrix which is

used to calculate geometric mean and normalized weight of parameters (Chowdhury et al.

2010).

Geometric mean

The geometric mean is calculated from different parameters based on defined score

(0.5-1 scale). The geometric mean is derived from the total score weight divided by the total

number of parameters (Rhoad et al. 1991).

Geometric mean = total score weight/total number of parameters

Normalized weight

It is the indicator of multi parameter analysis of groundwater mapping. Normalized

weight is calculated by assigned weight of parameter feature class to the geometric mean (Yu

et al. 2002)


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Normalized weight = Assigned weight of parameter feature class/geometric mean

In Analytical Hierarchical process, the thematic layers such as geology,

geomorphology, slope, land use and land cover, soil, aspect and rainfall are analysed by AHP

approach including geometric mean and normalized weight calculation to explore the

potential zone for groundwater recharge.

5.1 ASPECTS PROMPTING GROUNDWATER:

5.1.1 SLOPE MAP

Slope is a significant feature for the identification of groundwater potential sectors.

Slope determines the rate of infiltration and runoff of surface water, the flat surface areas can

hold and drain the water inside of the ground, which can increase the ground water recharge

whereas the steep slopes increase the runoff and decrease the infiltration of surface water into

ground. Slope map is extracted from SRTM-53-10 data by using the shape file in the QGIS

software. Slope is used to know infiltration capacity of the soil and helps to select suitable

location. Saga Software => Geo-Processing => Terrain Analysis => Basic Terrain Analysis

Figure 2 Slope map of the study region

Fig no.2 shows the topographic map with different levels in the study area. The SRTM 53-10

data is used for creating slope map in QGIS. Hence the slope map is resultant from the digital


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elevation model. The zones having fewer slopes measured as good for groundwater storage,

since less run off and high penetration.

5.1.2 ASPECT MAP

Aspect identifies the downslope direction of the maximum rate of change in value

from each cell to its neighbors. The values of each cell in the output raster indicate the

compass direction that the surface faces at that location. It is measured clockwise in degrees

from 0 (due north) to 360 (again due north), coming full circle. Flat areas having no

downslope direction are given a value of -1. The STRM-53-10 data is used to extract the

aspect map by using SAGA software. Saga Software => Geo-Processing => Terrain

Analysis => Basic Terrain Analysis

Figure 3. Aspect Map of the study region

Fig no.2 shows the different elevations in the study region. The aspect map is also used to

identify suitable sites.


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5.1.3 GEOLOGY MAP

Geology is the one of the natural resource which affect the occurence of groundwater.

From the minerals and geology map of tamilnadu we have created a geology map of

gummidipoondi area in QGIS by digitizing the shape file of my study area and converting

into raster layer with a scale of 1: 2000000. Geology and minerals map from geological

survey from India is opened in QGIS the map was geo-referenced. The shape file is inserted

on the geological map then the map was digitized and the map was clipped by using clipping

tool.

Figure 4. Geology Map of the study region

The study area is of sediments and alluvium in which the silt, sand and clay deposits are

available which will penetrate the water under the surface was distributed in Fig no.4. Thus

due to sediments available in the area will have groundwater more..

5.1.4 SOIL MAP

Soil map is derived from district resource map which is used to understand the type of

soil and helps to identify the infiltration capacity. District resource map is opened in QGIS


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the map was and the shape file is inserted on the map then the map was digitized, the map

was clipped by using clipping tool.

Figure 5. Soil Map of the study region.

Three types of soils, i.e, typic plinthustalfs, vertic ustochrepts, typic fragiochrepts are

present in the study area Fig no.5. These soils are comes under cateogory of inceptisols and

alfisols. Inceptisols are quite young soils and are found in steep slopes and flood plains.

Alfisols are clay-enriched subsoils with relatively high fertility. The inceptisols have a high

infiltration rate due to the presence of sand and gravel. whereas alfisols contain clays, the

infiltration rate in these soils is less.

5.1.5 GEOMORPHOLOGY MAP

The identification of geomorphologic features is very important for demarcating

groundwater potential. The hydro-geomorphological features of this area were mapped using

district resource map.


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Figure 6. Geomorphology of the study region

The major geomorphologic features are shallow buried plain and shallow alluvial plain are

presented in the study area fig no.6. This thematic layer is very important due to the presence

shallow buried plain and shallow alluvial plain, which are potential zones for groundwater

storage.

5.1.6 RAINFALL INTERPOLATION

Rainfall is a primary source of water for groundwater recharge in this area. The possibility of

ground water is high if the rainfall is high and it is low if rainfall is low. The rainfall data for

two year is taken from Indian Meteorological Department (IMD) of Thiruvallur district.

Table 1. Monthly rainfall values for the study region during the years 2016 and 2017

year Jan Feb Mar Apr May Jun Jul Aug Sept Oct Nov Dec

2016 1.2 0 0 0 158.6 149.2 54 18.1 185.4 5.3 28 214.4

2017 6 0 1.7 0 14.1 60.8 111.9 227.3 103.3 279.9 335.5 62.4

The maximum and minimum rainfall data in mm for two years were taken and is shown in

Table 1. Rainfall data was then digitised as point data in different locations and a rainfall

layer of the study area was created.


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Rainfall interpolation map is used find the average rainfall of the study area. Rainfall

interpolation map helps to find the availability of water. Rainfall plays major role for

selecting the suitable site. Rainfall interpolation is created by using monthly average rainfall

of two years.

Figure7. Interpolated rainfall map of the study region

According to the above values, will be pointed on the shape file and the average rainfall

values are entered in the attribute table. The highest mean annual rainfall of 1300 mm was

observed in the southern part of the study area. Here the dark blue colour receives the more

rainfall and sky blue colour receives the moderate rainfall and green colour receives low

raibfall showed in Fig no.7. Receiving more rainfall is favorable for the presence of more

groundwater.


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5.1.7 LANDUSE AND LANDCOVER

The landuse and land cover map was prepared from the LANDSAT 7 image data

using ENVI 4.7 software with supervised classification of maximum likelihood.

Landuse/Land cover patterns provide information about infiltration and runoff, which are

controlled by the nature of surface material. Landuse patterns such as dense forest, buildings,

vegetation, water bodies, exposure soil, clouds, rocks, agricultural land were identified. The

result of land use land cover is demonstrated either by decreasing runoff and expediting, or

by deceiving water on their leaf. The ASTER DEM data is used classify the landuse map. In

ENVI the ASTER DATA is used to create lulc map by using Region of interest tool and

locate the features like dense forest, water bodies, vegetation, clouds, shades, barren land, and

rocks. Here the red colour is classified lake water, blue colour as Barren land, cyan as

Agricultural land, magenta as road, orange as forest area, blue 2 as deep water, blue 3 as sea

water, yellow as building area, aquamarine as rock, sea green as water body, magenta as

brackish water showed in the land use and land cover map Fig no.8. Then in supervised

classification the maximum likelihood classification is used to create the landuse and

landcover map. The area with more vegetation and waterbodies will give more groundwater

due to infiltration is more in vegetation.

Figure 8. LULC Map of the study region


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5.1.8 GROUNDWATER POTENTIAL MAP

The relative importance of various thematic layers and their corresponding classes

were used for generating the groundwater potential zone map. The rank and weightage of

individual thematic layers and the mean and standard deviation of CSI for arriving at the

groundwater potential map. The Easy AHP method is totally based on the weight assign to

each layer. The weights are assigned to each layer according to its important. The CR should

be greater than or equal to 1. The weights are obtained to each layers using AHP method.

The weights are assigned to each layer based on the properties of the layer. By the assigning

the weights to each layer the output is obtained which helps to find the availability of water in

the study area.

The weights are weights are obtained from Easy AHP plugin in QGIS software given to the

layers are listed below in Table 2.

Table 2. Weights assigned for different thematic layers used in this study

Thematic layers Weights

Geology 0.280

Soil 0.229

Geomorphology 0.128

Rainfall 0.140

Slope 0.060

Aspect 0.042


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Figure 9. Groundwater Potential Map for the study region.

The study area was classified into very high, high, moderate, low and very low groundwater

potential zones. Groundwater potential zones contain geologic units of coastal sediments and

alluvium in which the silt, sand and clay deposits; geomorphological units of shallow buried

plain and shallow alluvial plain; soil units of inceptisols and alfisols; and high mean annual

rainfall. It is observed that in the very high and high groundwater potential zones, the

groundwater table is reasonably flat with widely spaced groundwater levels. The thematic

layers are like soil, geology, rainfall interpolation, geomorphology, slope and aspect are used.

the ground water map was created using the thematic layers by assigning weights to each

layers. the lineaments available in the study region the groundwater will br more in the study

area showed in Fig no.9.

6. CONCLUSION

This study was carried out to map the groundwater potential zones in the region of

Gummidipoondi taluk, where groundwater is being over exploited to meet the demand from

agriculture, industry and domestic users. Geographical information system and remote

sensing has demonstrated to be dominant and cost effective method for defining groundwater


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potential in Gummidipoondi region. Remote sensing and GIS is the significant tool for

managing water resources. Analytical Hierarchical process was adopted to prepare a map of

groundwater potential zones using thematic layers: geology, geomorphology, soil, slope,

aspect, rainfall and landuse. These thematic layers are combined and dispensed proper values

to prepare groundwater recharge zonation map. The groundwater recharge map demonstrates

the mixture of moderate and highly favourable zone due to shallow buried and alluvial buried

plain. The output map is generated with five different classes. The delineated zones within

this region were classified as very high, high, moderate, low and very low groundwater

potential zones on the basis of the ranks allocated to different structures of the thematic

maps. From the lineament density of Bhuvan software due to the dyke present in the study

area we compare that the groundwater will be more in that area. As the area goes away from

the dyke the groundwater is going to be reduced.

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