appraising the accuracy of gis-based multi-criteria decision making technique for delineation of...

Appraising the accuracy of GIS-based Multi-criteriadecision making technique for delineation of Groundwaterpotential zones

Tarun Kumar & Amar Kant Gautam & Tinu Kumar

Abstract Increased demand of water in different sectors and restriction of water re-sources, necessitate the planning of groundwater recharge. In this study, groundwaterpotential zone are delineated by combining remote sensing, geographical informationsystem (GIS) and multi-criteria decision making (MCDM) techniques in the Durg district,Chhattisgarh. Groundwater potential zones prepared using various thematic layers viz.geology, slope, land-use, lineament, drainage, soil, and rainfall. The thematic layers andtheir features were assigned suitable weights on the Saaty’s scale according to theirrelative significance for ground water occurrence. The assigned weights of the layersand their features were normalized by using Analytic Hierarchy Process (AHP) andeigenvector method finally; the selected thematic maps were integrated using weightedlinear combination method to create the final ground water potential zone map. Eachcriterion/factor was assigned an appropriate weight based on Saaty’s 9 point scale and theweights were normalized through the analytic hierarchy process (AHP). The process wasintegrated in the GIS environment to produce the groundwater potential prediction map ofthe study area. The groundwater potential map of the Durg district was found to be 75 %and 56 % accurate for seven and four factors respectively. The ground water potentialzone map was finally validated using the groundwater depth data from 16 pumping wellsrespectively in the study area.

Keywords AHP . Groundwater potential zone . Multi-criteria decision analysis . Remotesensing and GIS

DOI 10.1007/s11269-014-0663-6

T. Kumar (*)Indian Institute of Remote Sensing, Dehradun, Indiae-mail: [email protected]

A. K. GautamDepartment of Hydrology, IIT, Roorkee, Indiae-mail: [email protected]

T. KumarShri Guru Ram Rai PG College, Dehradun, Indiae-mail: [email protected]

Water Resour Manage (2014) 28:4449–4466

Received: 24 April 2013 /Accepted: 5 May 2014 /Published online: 29 July 2014# Springer Science+Business Media Dordrecht 2014

1 Introduction

Remote sensing and GIS are playing a remarkable role in the field of water resourcesdevelopment and management. Remote sensing provides multi-spectral, multi-temporal andmulti-sensor data of the earth’s surface. The lack of effective water resources managementapproaches in a region usually brings about adverse effects including decrease of reserveaquifers, decline of groundwater levels, saline water intrusion, land degradation, water pollu-tion and other social, economic and environmental problems (Brown et al., 1999). Theimportance of water is felt in all sectors as the demand and needs of the populace is growing.Increasing demands for fresh water in different sectors, especially in drinking and agriculturepurpose, so that there is need have to identify the groundwater potential zones (Saraf andChoudhury 1998; Jaiswal et al. 2003; Srinivasa Rao and Jugran 2003; Machiwal et al., 2011;Singh et al. 2011; Mukherjee et al. 2012; Adiat et al., 2012;). A systematic planning ofgroundwater development using modern techniques is essential for proper utilization andmanagement of this valuable but shrinking natural resource. As groundwater is a dynamicand interdisciplinary in nature and integrated approach of remote sensing (RS) and GIStechniques is a very useful in groundwater development and management studies. Remotesensing and GIS has been widely used for the preparation of different types of thematic layersand integrating them for the different purposes .Remote sensing can provide diverse datasetover a large inaccessible area that can be efficiently handled and analyzed in a GIS frame work(Eastman 1999; Jha et al. 2007; Chowdhury et al. 2010) . Integrating of these two techniqueshas proved to be an efficient tool in groundwater potential zonation and several studied havebeen conducted in various parts of the world (Chowdhury et al. 2010; Meshram et al. 2010). Ithas been observed that moderately high resolution data and GIS techniques are very useful foridentifying the groundwater potential zones (Agarwal et al. 2013) . Application of remotesensing and GIS in groundwater management such as potential zones have been reported bymany researchers, Ghayoumian et al.2007. Researchers considered varying number of differ-ent thematic layers such as geology, slope, land-use, lineament, drainage, soil, and rainfall etc.(Solomon and Quiel 2006; Mallick et al. 2014; These different thematic layers were integratedin the GIS to identify the suitable groundwater potential zones. In the present study remotesensing and GIS techniques were used based on the various thematic layers to delineate thegroundwater potential zones.

Multi-criteria analysis is one of the upcoming techniques. the most importantmethods of MCDM is AHP. AHP proposed (Saaty 1980) as a method of solvingsocio-economic decision making problems has been used to solve a wide range ofproblems. AHP is utilized when dimensions are independent. Saaty (1996) provides amethod for input judgment and measurement to derive ratio scale priorities for thedistribution of influence between the different thematic layers. Saaty (1996) suggestedthe use of AHP to solve the problem of independence on alternatives or criteria, andthe ANP to solve the problem of dependence among alternatives or criteria (Agarwalet al. 2013). The ANP prioritizes not just elements but also groups or clusters ofelements as is often necessary in the real world (Saaty 1999). ANP provides acontext-specific multi-criteria evaluation method that allows for the measurement ofone unique alternative in the face of general criteria (Ghayoumian et al. 2007). Theobjective of this study is to prepare different thematic layers that are geology, slope,land-use/land-cover, lineament, drainage, soil, and rainfall for the Durg District ofChhattisgarh using the Remote Sensing and GIS techniques, delineation of the groundwaterpotential zones and to develop a GIS model based on the various thematic layers that can beuseful for the identification of groundwater potential zones.

T. Kumar et al.4450

2 Study Area

The Durg district is located between 81°10’ to 81°36’ E longitude and 20°54’ to 21°32’ Nlatitude. The index map of the study area is shown in Fig. 1. The study area covers an areaof 2238.36 km2. The altitude of the Durg district varies from 412 m to 280 m above meansea level (MSL). The district has a subtropical climate characterized by hot summer andmonsoon rainfall followed by dry and cold winter season. The normal average rainfall ofthe district is 1,270 m.m. The annual temperature varies from 42.2ºC (summer) to 11ºC(winter). The relative humidity varies from 86 % (rainy season) to 36 % (winter). Thelandform plays a vital role in the occurrence and distribution of groundwater. Seven typesof geomorphic units are identified in the study area. Alluvial Plain Moderate and FloodPlain Shallow have higher suitable for groundwater recharging and hence it is the goodlandform for high groundwater potential.

3 Methodology

3.1 Development of Thematic Layers

Development of thematic layers involves digital image processing of remote sensingdata, digitization of existing maps and field data for extraction of pertinent informa-tion. To identify the groundwater potential zone in the study area, thematic layers of,geology, slope, land use/land cover, lineament, drainage, soil and rainfall were gen-erated using topographic maps, thematic maps, field data and satellite image in GISenvironment. Drainage layer is generated from Survey of India toposheets at 1:50,000scale. Subsequently, drainage is updated with the satellite image. The drainage densitymap is prepared using drainage layer. Slope map was prepared from Cartosat-1 DEM.The soil map was obtain from of the State Water Resource Department, Governmentof Chhattisgarh. Land Use map was generated using LISS - III image of the year2011. After the classification, accuracy assessment of the land use map was substan-tiated by correlating ground truth information. Daily rainfall data of 12 raingaugestations that is lying in the study area has been acquired from State Data CentreDepartment of Irrigation Raipur (C.G.). The mean annual rainfall was used to createrainfall map of the study area. The groundwater depth data of Durg district wascollected from Central Ground Water Board of Raipur (C.G.). The complete workflowof methodology is given in Fig. 2.

3.2 Selecting Criteria/Factors Influencing Groundwater Storage Potential in the Study Area

The factors considered in the study are: the geology, drainage density, the soil texture,the lineament density, the average annual rainfall and the slope. These factors arebelieved to be controlling the flow and storage of water in the area and henceinfluencing the groundwater storage potential of the area. The relationships of theseinfluencing factors are weighted according to their strength and expert opinion. Therepresentative weight of a factor of the potential zone is the sum of all weights fromeach factor. A factor with a higher weight shows a larger impact and a factor with alower weight value shows a smaller impact on groundwater potential interrelationship.Integration of these factors with their potential weights is computed through weightedoverlay analysis in GIS environment.

Delineation of Groundwater Potential Zones Using Remote Sensing and GIS 4451

3.2.1 Drainage

Drainage pattern depicts the history of the evolution of the earth's crust. The highest order ofstream found in the study area is of 6th order. The drainage density map reveals the densityvalue ranging from 0.016 to 3.36 km/km2. These are reclassified into four category i.e. > 2.5high, 2.5 – 1.5 medium, 1.5 – 1 low and 1 – 0 very low km/km2 for the analysis purposes.

India

N

Chhattisgarh

Durg District

Longitude 81° 10' 24.74” to 81° 36' 22.31” E

Latitude 20° 54' 40.95” to 21° 32' 57.10”N.

Total Area=2238.36 Sq. km.

Fig. 1 Location of the study area of Durg district

T. Kumar et al.4452

More weightages is assigned to very low drainage density regions whereas low weightagesassigned to very high drainage density considering recharge point of view (Figs. 3and 4). Low drainage density indicating high permeable surface stream frequency ofthe area is 2.17 which are calculated as number of streams per unit area is also lowshowing more.

3.2.2 Geology

This present study area under investigation as part of the Chhattisgarh basin comprisingcalcareous and argillaceous sediments represented by mainly limestone and shale under Raipur

Fig. 2 Flowchart of methodology used for estimation of groundwater potential zones


group. Recent alluvium occurs as thin discontinuous and an elongated patch along the smallstreams and Nala. A generalized stratigraphic sequence of the area is given below anddiscussed in the following sub-sections. Groundwater occurrence and its movement dependon the geological horizon. If it is porous and permeable, so it may store and permit easymovement of water. Visual interpretation of the satellite image has been used for delineation of

Fig. 3 Drainage map of the Durg district

Fig. 4 Drainage density map of the Durg district

T. Kumar et al.4454

geological features. The geologically study area has been classified into four classes which isshown in the Fig. 5.

3.2.3 Lineament Density Map

Lineaments are the linear, rectilinear, curvilinear features of tectonic origin, which caneasily observe in the satellite imagery. These lineaments normally show tonal, textural,soil tonal, relief, drainage and vegetation linearity and curvilinerities in satellite data.These lineaments are mapped with the help of satellite data and can be correlatedwith faults, fractures, joints, bedding planes and geological contacts which are usefulfor the groundwater potential study. Lineaments were interpreted from the satellitedata for Durg district. In the study area numbers of criss-crosed lineaments arepresent. The intersection of lineaments is considered as groundwater potential zones.Lineament density has been prepared and categorized into five classes as very poor,poor, moderate, high and very high is shown in the Fig. 6. High density lineamentsare favourable for ground water potential than less density lineaments thereforeweightages are assigned more for high density lineaments and less for low densitylineaments.

3.2.4 Soils

Soil texture map of the Durg district prepared with the help of the District adminis-tration soil map. Soil texture map of the study area is shown in Fig. 7. The soil in thestudy area reveals five main soil categories, namely clay loam, gravelly clay loam,gravelly sandy clay loam, gravelly sandy loam and sandy clay loam. Rank of soil hasbeen assigned on the basis of their infiltration rate. Sandy soil has high infiltration

Fig. 5 Geology map of the Durg district


rate, hence given higher priority, while the clayey soil has least infiltration rate henceassigned low priority.

3.2.5 Rainfall

The annual average rainfall of the study area is around 1,047 mm. The south-westmonsoon accounts 21 %, north-east monsoon 46 %, winter 6 % and summer 27 % of

Fig. 6 Lineament density map of the Durg district

Fig. 7 Soil texture map of the Durg district

T. Kumar et al.4456

the total rainfall is shown in Fig. 8. The study area depends mainly on north-eastmonsoon rains. Rainfall distribution along with the slope gradient directly affects theinfiltration rate of runoff water hence increases the possibility of groundwater poten-tial zones.

3.2.6 Slope Map

Overall the topography in the district varies between 256 m to 335 m above MSL. The highestelevation recorded in the district is 335 m above MSL and the lowest point is 256 m abovemean sea level. The slope of the study area mainly varies between zero and 5 %. Class havingless value is assigned higher rank due to the almost flat terrain while the class havingmaximum value is categorized as lower rank due to relatively high runoff. Slope map shownis shown in Fig. 9 of study area.

3.2.7 Land Use/Land Cover

Land use/land cover studies provides important indicators of the extent of groundwa-ter requirement and groundwater utilization, as well as being an important indicator inthe selection of sites for the groundwater potential zone. These maps are preparedfrom remotely sensed data (satellite images) at scale that are amenable to planning,environmental assessment and development studies. Twelve broad classes of land use/land cover were identified and demarcated are shown in the in the land use/land covermap of the study area is shown in Fig. 10. The different Land use/Land cover classesof the study area are built up land (town, village), Agricultural Kharif Crop, Agri-cultural Rabi Crop, Agricultural Zaid Crop, Agricultural Two crop area, fallow land,plantation, Forest-Deciduous, Tree Clad Area, Wastelands, Water bodies-River/Stream,Water bodies-Reservoir/Tanks. Landuse are interpretable by satellite images. Various

Fig. 8 Rainfall map of the Durg district


types of landuse pattern are identified in the study area which includes vegetation,sandy area, fallow land, water body, wasteland and urban area. Vegetation areexcellent sites for groundwater exploration, and hence given the highest rank. Sandyareas are considered to have good groundwater prospects, while the water bodies andurban area have poor groundwater potential.

Fig. 9 Slope map of the Durg district

Fig. 10 Land use/Land cover map of the Durg district

T. Kumar et al.4458

4 Result and Discussion

4.1 Deriving the Weights Using AHP

AHP for decision making in which a problem is divided into various parameters, arrangingthem in a hierarchical structure, making judgments on the relative importance of pairs ofelements and synthesizing the results (Saaty 1999). Each thematic layer has more than fiveclasses, which indicates the relationships between these interrelated classes are too complex.Hence, the relationship between these seven thematic layers has been derived using ANP andrelationship between their various classes has been identified using AHP. The methodology forderiving the weights to the thematic layers and their corresponding classes using AHPrespectively, involves the following steps:

Step 1 Construction of model: On the basis of literature review, many models have beenidentified for mapping groundwater potential. In the construction of model, theproblem should be clearly defined and then decomposed into various thematic layerscontaining the different feature/classes of the individual themes.

Step 2 Generation of pairwise comparison matrices: The relative importance values aredetermined by Saaty’s 1–9 scale Table 1, where a score of 1 represents equal impor-tance between the two themes, and a score of 9 indicates the extreme importance of onetheme compared to the other one Table 3 shows a matrix for comparing the classes inorder to achieve the priority. A pairwise comparison matrix is derived using Saaty’snine-point importance scale based on thematic layers used for delineation of ground-water potential. The AHP captures the idea of uncertainty in judgments through theprincipal eigenvalue and the consistency index (Saaty 2004) (Tables 2 and 3).

T1=Slope, T2=Rainfall, T3=Drainage density, T4=Lineaments density, T5=Soil texture,T6=Geology, T7=LULC.

Saaty gave a measure of consistency, called Consistency Index (CI) as a deviation or degreeof consistency was calculated using the formula.

CI ¼ λmax−nn−1

ð1Þ

Where n=number of factors (i.e. 7) and =average value of the consistency vector.

λ ¼ 7:4075þ 8:1504þ 7:708þ 7:831þ 10:391þ 8:528þ 7:889ð Þ7

¼ 8:27

CI ¼ 8:27−7ð Þ7−1ð Þ ¼ 0:067917781

Table 1 Saaty’s 1–9 scale of relative importance

Scale 1 2 3 4 5 6 7

Importance EqualImportance

Weak ModerateImportance

ModeratePlus

StrongImportance

StrongPlus

Very StrongImportance


Consistency Ratio (CR) is a measure of consistency of the pairwise comparison matrix.

CR ¼ CI

RIð2Þ

Where RI is the Ratio Index. The value of RI for different n values is given in Table 2.For n=7, RI=1.32

CR ¼ 0:0679

1:32

=0.0514Since 0.0514<0.1, it implies that there is a reasonable level of consistency in thepairwise comparison and hence the weights 0.05, 0.22, 0.10, 0.15, 0.12, 0.20, and 0.14 (i.e.5 %, 22 %, 10 %, 15 %, 12 %, 20 % and 14 %, respectively) can be assigned to Slope,Rainfall, Drainage density, Geology, Lineaments density, Soil texture and LULC, respectively.The values for different thematic are shown in Table 4.

4.2 Rating/Scoring of the Classified Thematic Layers

Thematic layer of each factor/parameter was classified as shown in Figs. 5–12. Rate/Score gives the ranges of groundwater storage potentiality within each factor. Rateswere assigned to each class according to the order of the influence of the class ongroundwater storage potential., Ratings of 1–5 were adopted where rates 1, 2, 3, 4and 5 respectively represent very low, low, medium, high and very high groundwaterstorage potential. It should however be pointed out that the thematic layers for slopeand lineament density have 5 and 5 classes respectively and were rated accordingly.Similarly, since only two rock types are present in the study area, it implies that thegeology thematic layer has only four classes which are also rated according to theinfluence of each rock type on the groundwater storage potentiality in the area. The classes ofthe thematic layers for all parameters and their corresponding ratings are shown in Table 5.

Table 2 Saaty’s ratio index fordifferent values of n N 1 2 3 4 5 6 7

RI 0 0 0.58 0.89 1.12 1.24 1.32

Table 3 Pairwise comparison ma-trix of 7 criteria for the AHPprocess

SuitabilityCriterion

T1 T2 T3 T4 T5 T6 T7

T1 1.000 0.167 0.200 0.250 0.222 0.500 0.667

T2 6.000 1.000 4.000 3.000 2.000 0.300 1.000

T3 5.000 0.250 1.000 0.400 0.500 0.769 1.000

T4 4.000 0.333 2.500 1.000 1.200 1.000 1.000

T5 4.500 0.500 2.000 0.833 1.000 0.500 0.714

T6 2.000 3.333 1.300 1.000 2.000 1.000 1.000

T7 1.500 1.000 1.000 1.000 1.400 1.000 1.000

Total 24.000 6.583 12.000 7.483 8.322 5.069 6.381

T. Kumar et al.4460

To demarcate groundwater recharge zones, all ten thematic layers after assigning weightswere overlaid step by step using ArcInfo v10 GIS software. The total normalized weights ofdifferent polygons in the integrated layer were derived from the following equation to calculatethe Groundwater Potential Index (GWPI):

GWPI ¼�LUwLUwi þ DDwDDwi þ SLwSLwiþ GMwGMwi þ RwRwi þ GGwGGwi

þ SOwSOwi þ GDwGDwi þ SDwSDwi þ LBwLBwi

ð3Þ

where, GWPI=Groundwater Potential Index, LU=Land Use Land Cover, DD=drainagedensity, SL=slope, GM=geomorphology, R=Rainfall, GG=geology, SO=Soil, GD=Ground-water Depth, SD=Soil Depth, LB=Lineament Buffer, ‘w’=normalized weight of a theme,‘wi’=normalized weight of the individual features of a theme.

4.3 Assessment of the Accuracy of the Prediction

Pumping test results from 16 boreholes drilled across the entire study area were used tovalidate the accuracy of the predicted map and hence of the adopted methodology. Thelocations, names and the actual yield descriptions of these boreholes are displayed on theprediction map shown in Fig. 11. The borehole locations, the expected borehole yielddescriptions from the prediction map, the actual yield descriptions obtained from the pumpingtests and the agreement/disagreement between the expected/actual borehole yields descriptionsare shown in Table 6.

The accuracy of the prediction is estimated as follows:Total number of boreholes=16The number of boreholes where there is an agreement between the expected and the actual

yield=13Number of boreholes where there is disagreement between the expected and the actual

yield=4The accuracy of the prediction=(12/16) *100=75 %.The prediction accuracy obtained showed that the method applied in this study produced

significantly reliable and accurate results.

Table 4 Determining the relative criterion weights

SuitabilityCriterion

T1 T2 T3 T4 T5 T6 T7 Weight Consistencyratio

T1 0.042 0.025 0.017 0.033 0.027 0.099 0.104 0.050 7.408

T2 0.250 0.152 0.333 0.401 0.240 0.059 0.157 0.227 8.150

T3 0.208 0.038 0.083 0.053 0.060 0.152 0.157 0.107 7.708

T4 0.167 0.051 0.208 0.134 0.144 0.197 0.157 0.151 7.831

T5 0.188 0.076 0.167 0.111 0.120 0.099 0.112 0.125 10.391

T6 0.083 0.506 0.108 0.134 0.240 0.197 0.157 0.204 8.528

T7 0.063 0.152 0.083 0.134 0.168 0.197 0.157 0.136 7.889

Total 1 1 1 1 1 1 1 1


4.4 Examination of the Effect of Coherence of Criteria on the Efficiency of MCDA

The effect of coherence of criteria on the efficiency of MCDA as a prediction tool wasexamined. In order to do this, rainfall and slope factors were removed from the set of criteriaearlier used. These factors were selected because the study area is characterized by nearlyuniform annual rainfall distribution. About 79.41 % of the area is classified into the rainfallcategory of 770 to 1,185 mm/year is shown in Fig. 9. Since rainfall will have uniform effect onthis large part of the area, it is expected that its removal from the set of criteria will not havesignificant effect on the prediction. This suggests that the area is largely characterized by flat

Table 5 Relative weights of various thematic layers and their corresponding classes

Influencing Factors Category (Classes) Potentiality forgroundwater storage

Rating Normalized weight

Lineament density 50 meters Very good 5 0.151

100 meter good 4

150 meter Moderate 3

200 meter Poor 2

250 meter Very poor 1

Land use/Land cover Land with crop and Vegetation Very Good 5 0.136

Land without scrub and Wasteland Good 4

Forest Dense Moderate 3

Built up land and Reservoir Poor 2

Water bodies/River Very Poor 1

Drainage density 1.237-3.361 Very good 5 0.107

0.947-1.237 Good 4

0.672-0.947 Moderate 3

0.436-0.672 Poor 2

0.016-0.436 Very poor 1

Geology Chandi Sandstone Very good 5 0.204

Chandi Limestone Good 4

Tarnga Formation Poor 2

Chandi Sandstone Very Poor 1

Slope 0-2 Very good 5 0.050

2-3 Good 4

3-5 Poor 2

Soil types Sandy clay loam Very good 5 0.125

Gravelly sandy loam Poor 4

Gravelly sandy clay loam Moderate 3

Gravelly clay loam Poor 2

Clay loam Very poor 1

Rainfall (mm) 1,100-1,185 (mm) Very Good 5 0.227

1,050-1,100 (mm) Good 4

990-1,050 (mm) Moderate 3

880-990 (mm) Poor 2

700-880 (mm) Very Poor 1

T. Kumar et al.4462

topography. Consequently, the prediction map was produced using the remaining three factors.The matrix of pair-wise comparisons of the four criteria for the AHP process is shown inTable 7. The normalized weights estimated were 0.09, 0.50, 0.25, and 0.17 for the LULC,Rainfall, Drainage density and Geology respectively. The consistency ratio of 0.028 wasevaluated for the comparisons indicating that the comparison was consistent and henceacceptable. The GWPI was computed adopting the same procedure earlier described. Thecomputed values of GWPI were used to produce the map shown in Fig. 12. The accuracyassessment of the map is presented in Table 8.

From the table, the accuracy of the prediction is estimated as follows: Total number ofboreholes=16.

Number of boreholes where there is agreement between the expected and the actual yield=9.

Number of boreholes where there is disagreement between the expected and the actualyield=7.

The accuracy of the prediction=(09/16)*100=56.25 %.T1=LULC, T2=Rainfall, T3=Drainage density, T4=Geology.C. I =0. 025, R.I=0.900, C. RATIO=0.028.The results obtained show that the accuracy of the prediction was reduced by 25 %. This

suggests that the coherence of criteria has a significant effect on the efficiency of MCDA and itdetermines the degree of accuracy of the prediction. This further establishes that for theadopted methodology to produce accurate and reliable.

5 Conclusion

Delineation of the groundwater potential zone in Durg district of Chhattisgarh, using remotesensing and GIS techniques is found efficient to minimize the time, labour and money and

Fig. 11 Groundwater potential zone map of the area produced from seven criteria


thereby enables quick decision-making for sustainable water resources development andmanagement. Satellite imageries, topographic maps and conventional data were used toprepare the thematic layers of geology, lineament, lineament density, drainage, drainage

Table 6 Accuracy assessment of the groundwater potential model map produced from seven criteria

S.no.

BoreWellNo.

Coordinates Actualyield fromdrilledborehole(l/h)

Agreementbetweenexpected andactual yieldsdescription

Expectedyielddescriptionfrom thepredictionmap

ActualyielddescriptionLatitude Longitude

1 BW1 81° 14′ 31.121″ E 21° 21′ 19.800″ N 1,530 Disagree Medium–high

Medium

2 BW2 81° 18′ 42.580″ E 21° 24′ 53.231″ N 7,845 Agree Very low–low Low

3 BW3 81° 24′ 49.849″ E 21° 28′ 15.362″ N 27,750 Agree Low–medium

High


Low


Low

6 BW6 81° 29′ 58.948″ E 21° 17′ 58.753″ N 15,000 Disagree Low–medium

High


Medium

8 BW8 81° 20′ 40.802″ E 21° 14′ 37.669″ N 23,700 Agree Medium–high

High


Low


Medium


Medium

12 BW12 81° 26′ 37.786″ E 20° 52′ 22.526″ N 600 Agree Very low–low Very low


Medium


Medium


Medium

16 BW16 81° 16′ 31.720″ E 21° 26′ 37.937″ N 740 Agree Very low–low Very low

Table 7 Pairwise comparison ma-trix of four criteria for the AHPprocess

Suitability Criterion T1 T2 T3 T4 Weight

T1 1.00 0.17 0.33 0.50 0.09

T2 6.00 1.00 3.00 2.00 0.50

T3 3.00 0.33 1.00 1.00 0.25

T4 2.00 0.50 1.00 1.00 0.17

T. Kumar et al.4464

density, slope, rainfall, soil texture and land-use/land cover. The thematic layers were assignedproper weightages through AHP technique and then integrated in the GIS environment toprepare the groundwater potential zone map of the study area. The groundwater potential map

Fig. 12 Groundwater potential zone map of the area produced from four criteria

Table 8 Accuracy assessment of the groundwater potential model map produced from four criteria

S.no.

BoreWellNo.

Coordinates Actual yield fromdrilled borehole(l/h)

Agrerement betweenexpected andactual yieldsdescription

Expected yielddescriptionfrom theprediction map

Actualyielddescription

Latitude Longitude

1 BW1 81°14′ 31.12″ E 21° 21′ 19.80″ N 1,530 Disagree Medium–high Medium

2 BW2 81°18′ 42.58″ E 21° 24′ 53.23″ N 7,845 Agree Very low–low Low

3 BW3 81°24′ 49.84″ E 21° 28′ 15.36″ N 22,750 Disagree Low–medium Very high

4 BW4 81°22′ 48.54″ E 21°23′ 13.27″ N 2,300 Agree Low–medium Low

5 BW5 81°22′ 0.76″ E 21° 21′ 28.18″ N 2,300 Agree Low–medium Low

6 BW6 81° 29′ 58.94″ E 21°17′ 58.75″ N 15,000 Agree Medium–high High

7 BW7 81°23′ 41.82″ E 21°19′ 13.01″ N 9,050 Agree Low–medium Medium

8 BW8 81° 20′ 40.80″ E 21°14′ 37.66″ N 23,700 Disagree Medium–high Very high

9 BW9 81° 25′ 40.98″ E 21°12′ 0.78″ N 2,700 Disagree Medium–high Low

10 BW10 81° 16′ 54.91″ E 21°10′ 49.50″ N 15,750 Disagree Low–medium Medium

11 BW11 81° 32′ 49.18″ E 21°0′ 43.71″ N 16,000 Agree Medium–high Medium

12 BW12 81° 26′ 37.78″ E 20°52′ 22.52″ N 600 Agree Very low–low Very low

13 BW13 81° 16′ 29.25″ E 21°5′ 10.39″ N 14,055 Agree Medium–high Medium

14 BW14 81° 32′ 54.44″ E 21°11′ 58.47″ N 27,000 Disagree High-Very high High

15 BW15 81° 29′ 46.87″ E 21°9′ 10.19″ N 21,500 Disagree Medium–high Medium

16 BW16 81° 16′ 31.72″ E 21°26′ 37.93″ N 740 Agree Very low–low Very low


of the Durg district was found to be 75 % and 56 % accurate for seven and four factorsrespectively. Thus, the groundwater potential map of Durg District was developed in this studycan be very useful to the planners, and engineers for establishment of suitable locations forrecharge structure and groundwater exploration.

Acknowledgments We thank anonymous reviewers for comments and suggestions to improve the manuscript.We also thank to Central Groundwater Board, (CGWB) NCCR, Raipur (C. G.) and Data Centre, Raipur forproviding necessary data.

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