ch

ttein

k*

nd Research, Asia Pacific Division. Published by Elsevier B.V. All rights

Keywords: Groundwater vulnerability map; GIS; Decision making; Fuzzy pattern recognition model; DRASTIC method; Nepal

oping countries, like Nepal has been deteriorating in recent relation to human health in recent years. The contamination ofgroundwater however is awidespread problem and requires hugeinvestments for remediation.Therefore, it is important to identifywhich aquifer systems and hydrogeological settings are most

* Corresponding author.

E-mail address: draj28@yahoo.com (D.R. Pathak).

Available online at www.sciencedirect.com

Journal of Hydro-environment Rese1. Introduction

Groundwater is a globally important, valuable and renew-able natural resource of water supply due to its relatively lowsusceptibility to contamination in comparison to surface waterand its large storage capacity; however, it is under threat ofdegradation both by inappropriate use and by contamination.For e.g. the quality of groundwater in urban areas of devel-

years mainly due to the high growth of population, unplannedgrowth of cities, excessive use of fertilizers and pesticides inagriculture land, no proper sewage system and poor disposal ofthe wastewater both from household as well as industrialactivities.

Therefore, contamination of groundwater has becomeamajoranxiety of planners, decision makers and water managersinvolved with managing the quantity and quality of water inreserved.

2010 International Association of Hydro-environment Engineering aGraduate School of Engineering, Osaka Sangyo University, 3-1-1 Nakagaito, Daito, Osaka 574-8530, Japan

Received 3 July 2009; revised 24 September 2009; accepted 30 October 2009

Abstract

This study highlights the computational technique of groundwater vulnerability index to identify the aquifers inherent capacity to becomecontaminated benefiting from fuzzy logic employing various hydrogeological parameters in the framework of Geographic Information Systems(GIS). This is usually carried out by using GIS based overlay index method. DRASTIC is one of the widely used popular overlay index methodto compute groundwater vulnerability index over the large geographical areas involving a variety of hydrogeological settings. DRASTIC methoduses linear model to calculate vulnerability index and factors that pertinent to the groundwater vulnerability should be divided into ranges toemploy rating value to each range. This system is unable to demonstrate a continuous output of vulnerability index from the easiest to bepolluted to the most difficult to be polluted that is fuzzy nature of the groundwater vulnerability to contamination. In this paper, integrated GISbased fuzzy pattern recognition model is developed to generate the continuous vulnerability function benefiting from the same input parametersof DRASTIC method. Moreover, vulnerability variation resulting from fuzzy and DRASTIC model with respect to any single input variable,making other parameters constant, is computed taking the characteristics of selected hydrogeological settings to compare the output of fuzzymodel with DRASTIC index. The ability of GIS based fuzzy pattern recognition model to generate continuous output of vulnerability index maybe considered as a pronounced advantage over DRASTIC method. Groundwater vulnerability map has been developed utilizing its output inshallow groundwater aquifer of Kathmandu, Nepal as a case study. Finally, output of vulnerability models are tested by nitrate data which weremeasured from ninety sources from shallow groundwater systems of study area. In large geographical areas with limited data, the groundwatervulnerability maps provide important preliminary information to decision makers for many aspects of the regional and local groundwaterresources management and protection.Resear

An integrated GIS based fuzzy pagroundwater vulnerability

Dhundi Raj Patha1570-6443/$ - see front matter 2010 International Association of Hydro-environment Enginedoi:10.1016/j.jher.2009.10.015paper

rn recognition model to computedex for decision making

, Akira Hiratsuka

arch 5 (2011) 63e77www.elsevier.com/locate/jherering and Research, Asia Pacific Division. Published by Elsevier B.V. All rights reserved.

ydrovulnerable to contamination prior to implementing groundwatermonitoring program in large geographical areas. In recognitionof the need for effective and efficient methods for protectinggroundwater resources from future contamination, scientists andresource managers have sought to develop techniques for pre-dicting which areas are more likely than others to becomecontaminated as a result of activities at or near the land surface(NRC, 1993). This concept has been widely termed to ground-water vulnerability to contamination. It is the sensitivity ofgroundwater quality to an imposed contaminant load, which isdetermined by the intrinsic characteristics of the aquifer. Thegroundwater vulnerability map based on aquifer vulnerabilityindex is the subdivision of the area into several hydrogeologicalunits with different levels of vulnerability which shows thedistribution of highly vulnerable areas, in which pollution is verycommon because contaminants can reach the groundwaterwithin a very short time. In general, numerical groundwatermodeling is an important predictive tool for managing waterresources in aquifers (El Yaouti et al., 2008), nevertheless suchmodels employ sets of extensive field measured data, which is infact, very costly and inefficient in large geographical areas forpreliminary groundwater resources management and protectionprogram. Due to the difficulty in mathematical formalism andlack of sufficient hydrogeologic and geochemical database atregional scale, the quite conceptual method like generalizedknowledge base (GKB) approach may be appropriate.

In GKB approach, decision is made on the basis of generalhydrogeological knowledge of the contaminant transport inaquifer media (Afshar et al., 2007). In order to tackle thegroundwater pollution and to protect its quality in a morescientific and efficient way, many different methods based onGKB approach have been developed to evaluate the ground-water vulnerability to pollution such as GOD method (Foster,1987), DRASTIC (Aller et al., 1987), SINTACS (Vrba andZaporozec, 1994), EPIK technique (Doerfliger and Zwahlen,1997). The most typical and popular method based on GKBapproach is the DRASTIC method, developed by the UnitedStates Environmental Protection Agency (EPA) (Aller et al.,1987).

2. DRASTIC method

The DRASTIC acronym stands for the seven hydro-geological parameters; depth to water, recharge, aquifer media,soil type, topography (slope), impact on the vadose zone mediaand hydraulic conductivity of the aquifer. Overlay and indexmethods, such as DRASTIC, are relatively easy to implementand require little data, but the result can be questioned becausethese methods rely more on the judgment of the analyst than onthe actual hydrogeological processes (Frind et al., 2006). Goguet al. (2003) reported that different overlay and index methodslike DRASTIC applied to same hydrogeological system cangenerate dramatically dissimilar results. Despite these criti-cisms due to the lack of proper validation, this method has beenadopted in the various part of world (Barber et al., 1994; Lynch

64 D.R. Pathak, A. Hiratsuka / Journal of Het al., 1997; Babiker et al., 2005; Rahman, 2008). Thismethod isoften modified to better address local issues or better representa local hydrogeologic setting (Merchant, 1994). Further,different researchers modified this methodology for mappingthe intrinsic vulnerability of aquifers to better represent a localhydrogeological setting (Zhang et al., 1996; Thirumalaivasanet al., 2003; Denny et al., 2007; Gomezdelcampo andDickerson, 2008). Thirumalaivasan et al. (2003) developedAHP-DRASTIC model to derive ratings and weights of modi-fied DRASTIC model parameters for use in specific aquifervulnerability assessment studies. They applied Analytic Hier-archy Process (AHP) to compute the ratings and weights of thecriteria and sub-criteria of all parameters used in the DRASTICmodel.

Recently, this popular GIS based overlay index method wasintroduced in Nepal to estimate the vulnerability index ofshallow groundwater aquifer of Kathmandu (Pathak et al.,2009). Further, sensitivity analysis was utilized to evaluatethe relative importance of model parameters and then revisedtheir weights, what is different from original DRASTICmethod, to better address local hydrogeological settings andterrain characteristics of Kathmandu (Pathak et al., 2009). Itwas the first attempt to develop intrinsic groundwatervulnerability map of Kathmandu Valley that provided onlya preliminary relative evaluation tool because of the somelimitations of adopted approach (DRASTIC method) andinsufficient input parameters. Therefore, it is noted that themethod and input parameters to produce the groundwatervulnerability index should be improved to make reliable toolfor groundwater quality protection and decision making in thisregion. In this context, this study focuses to develop morereliable groundwater vulnerability map improving computa-tional technique and input parameters. Moreover, output ofvulnerability model was validated by nitrate data measuredfrom ninety sources of shallow groundwater systems inKathmandu. In general, DRASTIC index uses linear model tocalculate the final vulnerability index cumulating the productof rating value (r) with its corresponding weight (w) of sevenhydrogeological parameters given by following relation:

Vi X7i1

wiri 1

DRASTIC elected to use weight for each parameter basedon its relative significance contributing to the pollutionpotential (depth to groundwater 5; net recharge 4; aquifermedia 3; soil type 2; topography 1; impact on vadosezone 5; and hydraulic conductivity 3). Either each factorhas been divided into ranges or media types and assigneda rating from 1 to 10 based on their significance to pollutionpotential as shown as Tables 1 and 2.

Although, DRASTIC is one of themost widely used standardgroundwater vulnerability method, it is unable to describea continuous transition from the easiest to be polluted to themostdifficult to be polluted that is fuzzy nature of the groundwatervulnerability to contamination. In this method, the nature of thevulnerability is taken into account by dividing the values of each

-environment Research 5 (2011) 63e77affecting factor into ranges and then giving to rating value toeach range. However, it should be noted that if a factor value can

be measured numerically, unlike the function of DRASTICindex, the fuzzy system generates a continuous vulnerabilityfunction. Hence, fuzzy approach can be used to assess the

model and further, Chen and Fu (2003) developed the gener-alized fuzzy pattern recognition model to evaluate ground-water vulnerability, taking only the standard value matrix of

bility

Table 1

DRASTIC standard ranges and ratings for DRASTIC factors that can be measured directly.

Depth to water table(D) Recharge (R) Topography (T) Hydraulic Conductivity (C)

Range (m) Rating Range (mm) Rating Range (%) Rating Range (m/d) Rating

0e1.5 10 0e51 1 0e2 10 0e4.1 1

1.5e4.6 9 51e102 3 2e6 9 4.1e12.2 2

4.6e9.1 7 102e178 6 6e12 5 12.2e28.5 49.1e15.2 5 178e254 8 12e18 3 28.5e40.7 6

15.2e22.5 3 >254 9 >18 1 40.7e81.5 8

22.5e30 2 >81.5 10

>30 1

ctly.

65D.R. Pathak, A. Hiratsuka / Journal of Hydro-environment Research 5 (2011) 63e77groundwater vulnerability to contamination.

3. Fuzzy approach

Basically, this contribution aims to identify the aquifersinherent capacity to become contaminated based on DRASTICsystem benefiting from fuzzy concept in the frameworks ofGIS. As an example of vulnerability linguistic evaluation ofvulnerability, the more shallow the water table, the higher thegroundwater pollution potential and less the recharge rate, thesmaller the groundwater pollution potential. First introducedby Zadeh (1965), fuzzy logic and fuzzy set theory have beenextensively used in ambiguity and uncertainty modeling indecision making. The basic concept in fuzzy logic is quitesimple; statements are not only true or false but alsorepresents the degree of truth or degree of falseness for eachinput. Fuzzy sets are characterized by membership functions.Several approaches have been used to apply fuzzy set theory togroundwater contamination problems, including fuzzy patternrecognition and optimization technique (Zhou et al., 1999;Chen and Fu, 2003), fuzzy rule-based systems (Uricchioet al., 2004; Dixon, 2005; Gemitzi et al., 2006; Afshar et al.,2007), fuzzy hierarchy model (Nobre et al., 2007). Zhouet al. (1999) used a multi objective fuzzy pattern recognition

Table 2

DRASTIC standard ratings value for parameters that cannot be measured dire

Aquifer media (A) Soil type (S)Range Rating Range

Massive shale 2 Thin or absent

Metamorphic/Igneous 3 Gravel

Weathered Metamorphic/Igneous 4 Sand

Glacial Till 5 Peat

Bedded Sandstone, Limestone and

Shale Sequences

6 Shrinking and/or

Aggregated Clay

Massive Sandstone 6 Sandy loam

Massive Limestone 6 Loam

Sand and Gravel 8 Silty Loam

Basalt 9 Clay Loam

Karst Limestone 10 Muck

Nonshrinking/

Nonaggregated Clay3.1. Fuzzy pattern recognition model

Groundwater vulnerability assessment can be regarded aspattern recognition problem in which, identification of thevulnerability level to which a sample belongs according tothe seven factor values of the sample when compared with thestandard values obtained from DRASTIC method. Standardvalues of two levels with regard to each factor are presented onthe basis of data in the DRASTIC system as shown as Table 3.According to Table 3, standard value matrix of the factors isgiven by

Impact of Vadose Zone (I)bility models integrated to develop groundwater vulneramap using DRASTIC parameters.five samples of the study area. However, the previous studieslack to incorporate the continuous input parameters benefitingfrom fuzzy concept to generate continuous vulnerability indexfor mapping of actual aquifer systems in watershed scaleutilizing the powerful spatial and visual capability of GIS.Hence, in this study, GIS based two level fuzzy patternrecognition model is developed to evaluate the degree ofvulnerability by means of natural language; the easiest to bepolluted to most difficult to be polluted. The flowchartpresented in Fig. 1 clearly illustrates how GIS and vulnera-Rating Range Rating

10 Confining Layer 1

10 Silt/Clay 3

9 Shale 3

8 Limestone 6

7 Sandstone 6

6 Bedded Limestone, Sandstone, Shale 6

5 Sand and Gravel with significant

Silt and Clay

6

4 Metamorphic/Igneous 4

3 Sand and Gravel 8

2 Basalt 9

1 Karst Limestone 10

ydro-environment Research 5 (2011) 63e7766 D.R. Pathak, A. Hiratsuka / Journal of HF

0 25:4 10 10 0 10 81:530:5 0 2 1 18 1 0

T fi;h 2

where fi,h is the standard value of level h with regard to factori; i 1, 2.7 and h 1, 2. The level 1 and level 2 correspondto easiest to be polluted and most difficult to be polluted interm of linguistic variables respectively. According to Table 3,higher the standard value, higher the level h for parameters; D

Fig. 1. Flow chart of methodology adopted to develop groundwater contamination potential map using DRASTIC and fuzzy pattern recognition model in

framework of GIS.

Table 3

Standard values of two levels with regard to each factor based on DRASTIC

system.

Factors D (m) R (mm) A S T (%) I C (m/d)

Level 1 0 254 10 10 0 10 81.5

Level 2 30.5 0 2 1 18 1 0

setting the simultaneous equation equivalent to zero, i.e.

vLuh;j;lj

vuh;j 0; vL

uh;j;lj

vlj 0 14

Solving Eq. (14), we get the formula for calculating themembership degree of sample j that belongs to level h is:

uh;j d2hjX2k1

d2kj

!115

when dhj 0, i.e. ri,j si,h, which shows that samplej completely belongs to level h, such that uh,j 1.

ydroand T, while higher the standard value, lower the level h forparameters; R, A, S, I and C. The membership degree of firstlevel standard value with regard to linguistic concept easiestto be polluted is supposed to be 1 and the membership degreeof the second level standard value i.e. most difficult to bepolluted in term of fuzzy concept supposed to be 0. Themembership degree of other levels varies from 0 to 1. Themembership degree, si,h of fi,h with respect to easiest to bepolluted is computed by:

si;h 0

fi;h fi;2fi;1 fi;2

1

fi;h fi;2fi;1 > fi;h > fi;2

fi;h fi;1; fi;1 < fi;h < fi;2 3

where fi,1 and fi,2 are the standard values of the easiest to bepolluted and most difficult to be polluted, respectively. Byusing Eq. (3), Eq. (2) can be transformed into membershipdegree matrix of standard values, which given by:

S1 1 1 1 1 1 10 0 0 0 0 0 0

T si;h: 4

Considering the factor values of the samples in study areafrom following factor value matrix:

X xij7xn 5where xij is the value of sample j with regard to factor i;i 1,2,.,7; j 1,2,.nand n is total number of samples to beevaluated. The factors in DRASTIC system can be classifiedinto two groups: A and B. In group A, the groundwatervulnerability increases with increasing the value of factors,whereas it is reverse in group B, the groundwater vulnerabilityreduces when factor value increases. For the group A and B,the membership degree of factors i.e. ri,j, can be calculated byusing the following Eqs. (6) and (7) respectively:

rij 8>:

1P7i1

wirijwi

2 1P7i1

wirij

29>>=>>;

1CCA

1

16Ultimately, we get,

ui;j

266641

8>>>:P7i1

wirij wi

2P7i1

wirij

29>>=>>;

377751

17

Eq. (17) is 2-level fuzzy pattern recognition model, which isused to evaluate the degree of groundwater vulnerability (that

represents the fuzzy concept easiest to be polluted) in eachsample of study area in the framework of GIS. According tothis model, higher the u1,j, the easiest to be polluted thesample j.

4. Case study

4.1. Study area

The groundwater vulnerability map of Kathamndu Valleywas prepared which includes three major cities: Kathmandu,Bhaktapur and Lalitpur. The total area of valley for the study isabout 350 square kilometers as shown as Fig. 2. The valleyconsists of gentle hills and flat lands at elevations of1300e1400 m. The surrounding hills rise to more than 2000 min elevation Phulchoki to the south of the Valley has thehighest elevation at 2762 m. Average annual precipitation inthe Kathmandu Valley is around 1400 mm, about 80% ofwhich falls in the monsoon period during June and July.

68 D.R. Pathak, A. Hiratsuka / Journal of Hydro-environment Research 5 (2011) 63e77Fig. 2. Location map of Kathmandu Valley (study area).

Within the valley, municipal and other water supplies dependon monsoon rains and the stream and groundwater systems fedby this precipitation. Surface runoff is high during themonsoon and recharge to the shallow aquifers occurs mostlyalong the basin margins, directly from precipitation and bysupply from a number of small rivers. However, recharge tothe deeper aquifers is considered to be limited, due to thepresence of clay beds that significantly restrict downwardpercolation. Because the Kathmandu Valley is a closed basinwith gentle slopes toward the center, groundwater flow isassumed to be slow, particularly in the deeper aquifers.

The surface of the Kathmandu Valley is almost flat but it hasburied bedrock surfacewith irregular shapes and high relief. Thedepth of the Precambrian bedrock from the ground surfaceranges from tens of meters to more than 500 m. The thickquaternary deposits consist of lacustrine and fluvial deposits,which have been eroded, however the original thickness of thedeposits is unknown. The basin fill sediments of KathmanduValley are mainly divided into two formations; Quaternary andPlio-pleistocene formation, each with different lithologic,geotechnical properties (Shrestha et al., 1999). Based on theengineering and environmental geological map of KathmanduValley (Shrestha et al., 1998), the geological setting of

Kathmandu Valley with different formations is shown in Fig. 3.The Quaternary formation, mainly formed by unconsolidatedmaterials/sediments, which consists of different four units;recent alluvial soil, residual soil, colluvial soil and alluvial fandeposit while the Plio-pleistocene formation consists of slightlyconsolidated sediments and has different seven units; Tokhaformation, Gokarna formation, Chapagaon formation, Kalimatiformation, Kobgaon formation, Lukundol formation and Basalboulder bed. The Quaternary formation, mainly formed byunconsolidated materials/sediments, which consists of differentfour units; recent alluvial soil, residual soil, colluvial soil andalluvial fan deposit. The brief description of each formation hasbeen presented in previous work (Pathak et al., 2009).

By convention, the aquifers in the Kathmandu Valley can bedivided into shallow and deep systems. A shallow unconfinedaquifer occurs at around 0e10 m depth and a deep confinedaquifer occurs at around 310e370 m (Khadka, 1993). Otherisolated groundwater storeys are situated at significantly deeperlevels (Gautam and Rao, 1991). Groundwater from the shallowaquifers is drawn from hand-dug wells, hand pumps or roarpumps, whereas the deeper aquifers are exploited from deepwells. Traditional stone spouts (locally known as dhunge dhara)are also common, drawing water from shallow aquifers.

69D.R. Pathak, A. Hiratsuka / Journal of Hydro-environment Research 5 (2011) 63e77Fig. 3. Geological map of Kathmandu Valley.

Groundwater from both shallow and deeper aquifers has beenused extensively for drinking and industrial purposes. About50% of the water used in the city of Kathmandu is derived fromgroundwater (Jha et al., 1997; Khatiwada et al., 2002). Exploi-tation of these aquifers, especially the shallow aquifer, has beenincreased rapidly in recent years. The quality of water extractedfrom such sources is under threat of degradation by contami-nants because of the different anthropogenic activities, resultingfrom rapid unplanned and haphazard urbanization of entirevalley. The urban growth detection was 10.86 square kilometersfrom 1988 to 1997 (ICIMOD, 2000), which has been furtherincreased since then.

4.2. Model input parameters and groundwatervulnerability index

All seven input data layers used in DRASTIC system weregenerated and/or obtained from its original source as a point,line, or polygon layer (Fig. 4). Then, all parameters contrib-uting to groundwater vulnerability were converted from vector(point, line, or polygon) to raster (grid) using the GIS. In rasterlayer, space is subdivided into discrete cells with requiredresolution. In this work, all input parameters for the DRASTIC

and fuzzy pattern recognition model were generated in sevenseparate raster layers of 30 m 30 m grid resolution (Fig. 5).GIS techniques were utilized with the help of Eqs. (6) and (7)to generate the continuous input layer of each DRASTICparameter.

Depth to water table was collected from borehole log infor-mation, direct measurement of existing groundwater wells andother secondary information. Both inverse distance movingaverage interpolation technique and kriging were tested on themeasured depth to groundwater point data to generate rastersurface. However, the kriging techniquewas found to be suitableto generate smooth surface. The membership degree value mapwas computed using Eq. (7) and rating map was prepared byassigning sensitivity rating values as 10 for depth (30 m).

The shallow aquifer of the valley is recharged mainly bydirect infiltration from precipitation therefore net recharge wasestimated by using following formula:

Net recharge rainfall evaporation runoff 18

70 D.R. Pathak, A. Hiratsuka / Journal of Hydro-environment Research 5 (2011) 63e77Fig. 4. Example of developing model input parameters.

ydroD.R. Pathak, A. Hiratsuka / Journal of Hwhere rainfall map was prepared by interpolation mean ofannual precipitation (mm/year) from the 21 representativerainfall stations in the Kathmandu Valley (DHM, 2006).Evaporation data was used from only one station of the valleyrecorded in international airport of Kathmandu (DHM, 2006).Runoff was calculated on each pixel based on empiricalrelation in which the runoff coefficients assumed to be 0.8 forbuilt up/urban area, 0.27 for forest, 0.25 for open field/lawn,0.4 for agricultural field with clay, 0.3 for agricultural fieldwith sand and 0.15 for water body and highly permeablerecent flood plain. Thus obtained recharge value from Eq. (18)was used to calculate the membership degree value as well asrating map to evaluate the degree of vulnerability.

Fig. 5. Seven input raste71-environment Research 5 (2011) 63e77The aquifer media map was developed based on varioussources regarding groundwater basin and geological formationmap of Kathmandu (Shrestha et al., 1998; JICA, 1990; Jha et al.,1997). Rating value was assigned based on DRASTIC method.

The grid layer of soil media was generated from soil mapfrom Department of Survey, Nepal (NGIIP, 1994). The majorsoil types available in study area are loamy, loamy skeletal andloamy/bouldery. Hence, rating was assigned as according toDRASTIC method based on the soil type.

The topographic contours map of 1:25000 scale (NGIIP,1994) was digitized to construct slope map using 3D analystand spatial analyst in ArcGIS9.2. The slope was converted intomembership value using Eq. (7) and rating map also prepared

r layers to compute vulnerability index.

ydro72 D.R. Pathak, A. Hiratsuka / Journal of Hwith assigning sensitivity rating as 10 for plain (18%).

The parameter, impact of vadose zone represents theinfluence of unsaturated zone above the water table, whichcontrols the passage and attenuation of the contaminatedmaterial to the aquifer. The impact of vadose zone map layerwas prepared using geological formation and soil map ofKathmandu Valley. This map was also verified using someborehole log information from different part of valley. Ratingvalue was assigned according to DRASTIC method tocompute vulnerability index.

Fig. 6. Illustration for how integrated GIS based fuzzy model compute different vu

method.-environment Research 5 (2011) 63e77Aquifer hydraulic conductivity is the ability of the aquiferformation to transmit water. It depends on the intrinsicpermeability of the material and on the degree of saturation.Generally, the hydraulic conductivity is measured from the fieldpumping tests data. In this study, hydraulic conductivity valueswere obtained from pumping test data (Metcalf and Eddy, 2000)and have been interpolated to generate hydraulic conductivitymap of required resolution. According to Metcalf and Eddy(2000), hydraulic conductivity of the study area is lower than10 m/d that suggests hydraulic conductivity has less contribu-tion to groundwater vulnerability to contamination in designedstudy area. Hydraulic conductivity data was converted to fuzzy

lnerability value within same range of input parameters despite of DRASTIC

sultsmumd 23

factors and unveils two upper and lower bound whereas theoutput ofDRASTIC has a discrete nature (Fig. 7aec). The fuzzyindex is higher than DRASTIC index however, both modelsfollow same trend. It is shown that by assigning ratings forrelated factors falling into certain range, DRASTICmethod willignore the difference of factor values within the same range andis unable to reflect to the influence the variation of hydro-geological factors on the groundwater vulnerability. Since, themodel parameters are derived from the DRASTIC system,similar results with that ofDRASTIC are expected, however, themain difference is that fuzzy pattern recognition model cangenerates a continuous vulnerability function unlike step

Fig. 7. (a) Vulnerability variation of water depth in fuzzy pattern recognition

model and DRASTIC method. (b) Vulnerability variation of recharge in

fuzzy pattern recognition model and DRASTIC method. (c)Vulnerability

variation of topography in fuzzy pattern recognition model and DRASTIC

method.

ydrorespectively, then the normalized DRASTIC index may beobtained as:

In Id 23=203 19

where In and Id are normalized and computed DRASTICindices, respectively. To compare the output of the fuzzy modelwith DRASTIC index, vulnerability variation resulting fromfuzzy and DRASTIC model with respect to any single inputvariable, making other parameters constant, is computed takingthe characteristics of selected hydrogeological settings. Thecharacteristics of the seven factors of the selected hydro-geological setting used for comparison study are shown in Tablevalidates the models performance by comparing the rewith those of normalized DRASTIC index. Since maxiand minimum values of the DRASTIC index are 226 anmembership value using Eq. (6) and rating value was assignedaccording to DRASTIC method.

Finally, relative degree of vulnerability (i.e. vulnerabilityindex) of the each sample of the study area was calculatedusing Eq. (17). The output of this model was utilized togenerate groundwater vulnerability map, where index value isranged from most difficult to be polluted to easiest to bepolluted in term of linguistic variables. Further, DRASTICindex was computed using the Eq. (1) to compare the output offuzzy pattern recognition model.

5. Results and discussion

5.1. Vulnerability variation in fuzzy pattern recognitionmodel

In DRASTIC method, all seven input raster layers aredivided into certain ranges to employ rating value to eachrange then final vulnerability index is computed. However, itshould be noted that if a factor value can be measurednumerically, unlike the function of DRASTIC index, the fuzzysystem generates a continuous vulnerability function. Theinput parameters, which are very important to groundwatervulnerability to contamination, such as depth to water table,recharge, hydraulic conductivity and slope could be measurednumerically. Therefore, it is not necessarily to divide in certainrange to compute vulnerability index by assigning rating valuewhat is usually done in DRASTIC method. For e.g., ratingvalue 7 is assigned for depth 4.6e9.1 m range that impliesDRASTIC index is equal at any place at this range of waterdepth however, vulnerability value may differ at water depthof 4.6 m and 9.1 m. Fig. 6 illustrates an example how inte-grated GIS based fuzzy pattern recognition model computedifferent vulnerability index value within same range of inputparameters at particular hydrogeological setting despite ofDRASTIC method.

The decision making model benefiting from fuzzy logic,which is also based on the knowledge of the DRASTIC systemhence its verification seems quite vital. Hence, this study

D.R. Pathak, A. Hiratsuka / Journal of H4. The comparison between outputs of two models indicate thatthe fuzzy system has continuous nature with respect to input73-environment Research 5 (2011) 63e77DRASTIC output function, which may be considered asa distinct advantage over DRASTIC method.

ydro74 D.R. Pathak, A. Hiratsuka / Journal of H5.2. Vulnerability map

Groundwater vulnerability index was computed using Eq.(17) utilizing all developed input parameters in GIS frame-works. Fig. 8a shows the relative degree of groundwatervulnerability to contamination which was obtained from thefuzzy pattern recognition model based on DRASTIC system.The values extend from 0.24 to 0.87 i.e. most difficult to bepolluted to easiest to be polluted in term of linguistic

Fig. 8. (a) Groundwater contamination potential map based on vulnerability index co

potential map based on vulnerability index computed from DRASTIC method.-environment Research 5 (2011) 63e77variables. Fig. 8b also shows the degree of groundwatervulnerability to contamination in term of normalizedDRASTIC index, where the values vary from 0.29 to 0.79 withthe lowest possible rating being 0.29 and the highest ratingbeing 0.79. We categorized groundwater vulnerability mapinto five classes: very low, low, medium, high and very high byintroducing the higher the index, the greater the relativepollution potential. Fig. 9 illustrates the number of samples(pixels) corresponding to vulnerability output of fuzzy and

mputed from fuzzy pattern recognition model. (b) Groundwater contamination

while the dominant part of the study area had more than254 mm/year. The results also indicate the rich groundwaterresources area; northern part of Kathmandu like, Gokarnaformation and highly permeable alluvial deposits are highlysusceptible for vulnerability in which, if pollution is common,contaminants can reach the groundwater within a very shorttime.

5.3. Validation of output of vulnerability model by fieldmeasured nitrate data

The modeled results should validate from field observationswhich is however quite difficult and expensive in largegeographical area. In this study, the output of vulnerabilityFig. 9. Frequency distribution of vulnerability index from fuzzy pattern and

DRASTIC model.

75D.R. Pathak, A. Hiratsuka / Journal of Hydro-environment Research 5 (2011) 63e77DRASTIC model in study area. A high index that correspondsto easiest to be polluted in linguistic term, indicates thecapacity of the hydrogeologic environment and the landscapefactors to readily move waterborne contaminants into thegroundwater and consequently need to be managed moreclosely. Low index i.e. most difficult to be polluted repre-sents groundwater that is better protected from contaminantleaching by natural environment.

The output of fuzzy model reveals especially northern partof valley and recent alluvial deposits falls under very highvulnerable that is about 28% of the total area. About 47, 21and 4% of the valley was classified as high, medium and lowvulnerable area respectively. No area was found in the cate-gory very low vulnerable. While in DRASTIC method, no areawas categorized as very high vulnerable zone nevertheless 58and 38% of the area categorized as high and mediumvulnerable. Similarly, 4% of the total area is classified as lowvulnerable zone. The combination of the model parametersthat pertinent to groundwater vulnerability like very shallowdepth to water table (

ydrocan be concluded that vulnerability predicted by fuzzy patternrecognition method is more reliable than DRASTIC method. Ifa watershed manager uses this result to conduct the ground-water sampling strategy for potential contaminants by humanactivities in the study area, this will be far more useful,compared to results generated by conventional overlay indexmethod like DRASTIC.

In addition, the relationship between nitrate and ground-water depth (one of the important parameters that pertinent togroundwater vulnerability to contamination) indicates nitrateconcentration was, high it existed within the top 10 m and nondetectable amounts were found in deeper groundwater(Fig. 10). As the groundwater wells are contaminated bynitrate due to the anthropogenic activities from or nearbyground surface, concentrations of nitrate should be higher atwells of low water depth. However, significant number ofwells which have a high concentration of nitrate is located inthe low vulnerable zones, especially areas that belongs oldurban setting of valley. Possible reasons that nitrate concen-trations observed high values in old urban areas even catego-rized as low vulnerable zones based on intrinsic vulnerabilityindex are due to the inadequate disposal of human and animalwaste and leach from septic tanks for a long time. In thoseareas, many households use septic tanks and the proximity ofthe septic tanks and the groundwater wells are not maintained,where the nitrate could infiltrate into the shallow aquifers.

6. Summary and conclusions

The overall goal of this study is twofold. First, it aims toimprove the methodology for the computation of groundwatervulnerability index to generate contamination potential map byincorporating the continuous nature of vulnerability tocontamination using DRASTIC parameters in large geograph-ical area. Second, it brings up to date the input parameters ofvulnerability model and geochemical data to validate vulnera-bility map of shallow groundwater aquifer of Kathmandu,where more than half of the population depend on groundwatersources to fulfill their water demand. Generally speaking, thereis a continuous transition from easiest to the most difficultaquifer to be polluted, which is in fact fuzzy nature ofgroundwater vulnerability to contamination. In this regard,integrated GIS based fuzzy pattern recognition model generatesthe continuous vulnerability function unlike step DRASTICindex, which is in fact the pronounced advantage overDRASTIC method. This approach could take fuzziness natureof groundwater vulnerability (i.e. continuous transition fromeasiest to the most difficult aquifer to be polluted) more effi-ciently than DRASTIC method.

An integrated GIS based fuzzy pattern recognition modelbased on DRASTIC system can be applied to any aquifersystems to predict groundwater vulnerability more efficiently.This approach has been applied to develop the groundwatervulnerability map to shallow groundwater systems of Kath-mandu Valley as case study. A comparison between the output

76 D.R. Pathak, A. Hiratsuka / Journal of Hof fuzzy pattern recognition model and the DRASTIC wasaccomplished. The fuzzy index is higher than DRASTIC indexhowever, both models follow same trend. The study shows that75% and 58% of the valleys shallow groundwater aquifer isunder high to very high vulnerability to contamination fromfuzzy and DRASTIC method respectively which is the maincause of concern for more than 2 million people living inKathmandu.

Moreover, the accuracy of the DRASTIC and fuzzy resultswas evaluated by comparing the results with nitrate datasampled from shallow groundwater aquifer of Kathmandu.Fuzzy pattern recognition model predicted three and six wellsout of 15 contaminated wells as very high vulnerable and highvulnerable respectively while DRASTIC predicted no area thatwas categorized as very high vulnerable zone. From this result,it can be concluded that vulnerability predicted by fuzzypattern recognition method is more reliable than DRASTICmethod. This result affirms the validation and reliability of anintegrated GIS based fuzzy pattern recognition model to someextent, which reflect an aquifers inherent capacity to becomecontaminated. However, special emphasis should be given toupdate model input parameters, loadings and fate of contam-inants transport into groundwater systems to get the reliableoutput for policy and decision making in groundwatermanagement in watershed scale. The groundwater vulnera-bility maps developed in this study are significant screeningtools in policy and decision making for many aspects of theregional and local groundwater resources management andprotection.

References

Afshar, A., Marino, M.A., Ebtehaj, M., Moosavi, J., 2007. Rule-based fuzzy

system for assessing groundwater vulnerability. Journal of Environmental

Engineering ASCE 133 (5), 532e540.

Aller, L., Bennet, T., Lehr, H.J., Petty, J.R., Hackett, G., 1987. DRASTIC;

a standardized system for evaluating groundwater pollution potential using

hydrogeologic settings. USEPA-600/2-87-035, 622 pp.

Babiker, I.S., Mohammed, A.A.M., Hiyama, T., Kato, K., 2005. A GIS-based

DRASTIC model for assessing aquifer vulnerability in Kakamigahara

Heights, Gifu Prefecture, central Japan. Science of the Total Environment

345 (1e3), 127e140.

Barber, C., Bates, L.E., Barron, R., Allison, H., 1994. Comparison of stan-

dardized and Region-specific methods assessment of the vulnerability of

Groundwater to pollution: A case study in an Agricultural catchment. In:

Proceedings of 25th IAH Congress water Down under, Melbourne,

Australia.

Chen, S.Y., Fu, G., 2003. A DRASTIC fuzzy pattern recognition methodology

for groundwater vulnerability evaluation. Hydrological Science Journal

48 (2), 211e220.

Denny, S.C., Allen, D.M., Journeay, J.M., 2007. DRASTIC-Fm: a modified

vulnerability mapping method for structurally controlled aquifers in the

southern Gulf Islands, British Columbia, Canada. Hydrogeology Journal

15 (3), 483e493.

Department of Hydrology and Metrology (DHM), 2006. Precipitation Records

of the Bagmati Zone (1999e2005). Ministry of Environment, Science andTechnology, Government of Nepal.

Dixon, B., 2005. Groundwater vulnerability mapping: a GIS and fuzzy rule

based integrated tool. Applied Geography 25 (4), 327e347.

Doerfliger, N., Zwahlen, F., 1997. EPIK: a new method for outling of

protection areas in karstic environment. International Symposium on Karst

Waters and Environmental Impacts, pp. 117e123.

-environment Research 5 (2011) 63e77El Yaouti, F., El Mandour, A., Khattach, D., Kaufmann, O., 2008. Modelling

groundwater flow and advective contaminant transport in the Bou-Areg

unconfined aquifer (NE Morocco). Journal of Hydro-environment

Research 2 (2008), 192e209.

Foster, S.S.D., 1987. Fundamental concepts in aquifer vulnerability, pollution

risk and protection strategy. Proceedings and Information/TNO Committee

on Hydrological Research 38, 36e86.Frind, E.O.,Molson, J.W., Rudolph, D.L., 2006.Well vulnerability: a quantitative

approach for source water protection. Ground Water 44 (5), 732e742.

Gautam, R., Rao, G.K., 1991. Groundwater resources evaluation of the

Kathmandu Valley. Journal of Nepal Geological Society 7, 39e48.

Gemitzi, A., Petalas, C., Tsihrintzis, V.A., Pisinaras, V., 2006. Assessment of

groundwater vulnerability to pollution: a combination of GIS, fuzzy logic

and decision making techniques. Environmental Geology 49 (5), 653e673.Gogu, R.C., Hallet, V., Dassargues, A., 2003. Comparison of aquifer vulner-

ability assessment techniques, application to the Neblon river basin

(Belgium). Environmental Geology 44 (8), 881e892.

Gomezdelcampo, E., Dickerson, J.R., 2008. A modified DRASTIC model for

sitting confined animal Feeding Operations in Williams County, Ohio,

USA. Environmental Geology 55 (8), 1821e1832.

Jha, M.G., Khadka, M.S., Shrestha, M.P., Regmi, S., Bauld, J., Jacobson, G.,

1997. The Assessment of Groundwater Pollution in the Kathmandu Valley,

Nepal. Report on Joint Nepal-Australia project 1995e96. Australian

Geological Survey Organization, Canberra, pp. 64.

Nobre, R.C.M., Filho, O.C.R., Mansur, W.J., Nobre, M.M.M., Cosenza, C.A.

N., 2007. Groundwater vulnerability and risk mapping using GIS,

modeling and a fuzzy logic tool. Journal of Contaminant Hydrology 94,

277e292.

National Research Council (NRC), 1993. Ground Water Vulnerability

Assessment: Contamination Potential under Conditions of Uncertainty.

National Academy Press, Washington, DC.

Pathak, D.R., Hiratsuka, A., Awata, I., Chen, L., 2009. Groundwater vulner-

ability assessment in shallow aquifer of Kathmandu Valley using GIS-

based DRASTIC model. Environmental Geology 57 (7), 1569e1578. doi:

10.1007/s00254-008-1432-8.

Rahman, A., 2008. A GIS based DRASTIC model for assessing groundwater

vulnerability in shallow aquifer in Aligarh, India. Applied Geography 28

(1), 32e53.

Shrestha, O.M., Koirala, A., Karmacharya, S.L., Pradhananga, U.B.,

Pradhan, P.M., Karmacharya, R., Hanisch, J., Kerntke, M., Joshi, P.R.,

Steiner, L., Busch, K., Jnwali, B.M., Maske, N.D., Tuladhar, G.B.,

Kaphle, K.P., 1998. Engineering and Environmental Geological Map of

Kathmandu Valley. Scale 1:50000. Department of Mines and Geology,

Nepal.

Shrestha, O.M., Koirala, A., Hanisch, J., Busch, K., Kerntke, M., Jager, S.,

77D.R. Pathak, A. Hiratsuka / Journal of Hydro-environment Research 5 (2011) 63e77JICA, 1990. Groundwater management project in Kathmandu Valley, Final

report, main report and supporting reports, November 1990.

Kathmandu Valley GIS database. Towards bridging the data gap, 2000.

MENRIS/ICIMOD.

Khadka, M.S., 1993. Groundwater quality situation in alluvial aquifers of the

Kathmandu Valley. AGSO. Journal of Australian Geology & Geophysics

14, 207e211.

Khatiwada, N.R., Takizawa, S., Tran, T.V.N., Inoue, M., 2002. Groundwater

contamination assessment for sustainable water supply in Kathmandu

Valley, Nepal. Water Science and Technology 46 (9), 147e154.

Lynch, S.D., Reynders, A.G., Schulze, R.E., 1997. A DRASTIC approach to

groundwater vulnerability in South Africa. South African Journal of

Science 93 (2), 59e65.

Merchant, J.W., 1994. GIS-based groundwater pollution hazard assessment:

a critical review of the DRASTIC model. Photogrammetry Engineering &

Remote Sensing 60 (9), 1117e1127.Metcalf and Eddy, 18 February 2000. Urban Water Supply Reforms in the

Kathmandu Valley (ADB TA Number 2998-NEP), Completion Report. In:

Executive summary, main report and Annex 1 to 7, vols. I, II. Metcalf &

Eddy, Inc. with CEMAT Consultants Ltd..

National Geographic Information Infrastructure Project (NGIIP), 1994.

Department of Survey, Government of Nepal, Kathmandu, Nepal.1999. A geo-environmental map for the sustainable development of the

Kathmandu Valley, Nepal. Geo-journal 49 (2), 165e172.Thirumalaivasan, D., Karmegam, M., Venugopal, K., 2003. AHP-DRASTIC:

software for specific aquifer vulnerability assessment using DRASTIC

model and GIS. Environmental Modeling and Software 18 (7), 645e656.

Uricchio, V.F., Giordano, R., Lopez, N., 2004. A fuzzy knowledge e baseddecision support system for groundwater pollution risk evaluation. Journal

of Environmental Management 73 (3), 189e197.

United State Environmental Protection Agency (USEPA), 2009. 2009 Edition

of the Drinking Water Standards and Health Advisories, EPA 822-R-09-

011, Office of Water U.S. Environmental Protection Agency, Washington,

DC, pp 18.

Vrba, J., Zaporozec, A., 1994. Guidebook on Mapping Groundwater Vulner-

ability. In: International Contributions to Hydrology, vol. 16. Heinz Heise,

Hannover, 131 pp.

Zadeh, L.A., 1965. Fuzzy sets. Journal of Information and Control 8 (3),

338e353.Zhang, R., Hamerlinck, J.D., Gloss, S.P., Munn, L., 1996. Determination of

nonpoint-source pollution using GIS and numerical models. Journal of

Environmental Quality 25 (3), 411e418.

Zhou, H.C., Wang, G.L., Yang, Q., 1999. A multiobjective fuzzy pattern

recognition model for assessing groundwater vulnerability based on

DRASTIC system. Hydrological Science Journal 44 (4), 611e618.

An integrated GIS based fuzzy pattern recognition model to compute groundwater vulnerability index for decision makingIntroductionDRASTIC methodFuzzy approachFuzzy pattern recognition model

Case studyStudy areaModel input parameters and groundwater vulnerability index

Results and discussionVulnerability variation in fuzzy pattern recognition modelVulnerability mapValidation of output of vulnerability model by field measured nitrate data

Summary and conclusionsReferences