ORIGINAL PAPER

Modeling groundwater vulnerability prediction usinggeographic information system (GIS)-based ordered weightedaverage (OWA) method and DRASTIC model theoryhybrid approach

K. A. Mogaji & H. S. Lim & K. Abdullah

Received: 31 July 2013 /Accepted: 14 October 2013# Saudi Society for Geosciences 2013

Abstract A groundwater vulnerability prediction modeling,based on geographic information system-based orderedweighted average (OWA)-DRASTIC approach, isinvestigated in southern part of Perak, Malaysia. Theproposed approach is a mix of curiosity that allows the usesof different decision strategies for the purpose of quantifyinglevel of risk in vulnerability prediction. Seven pollutionpotential factors based on DRASTIC model theory wereindividual evaluated. Their results were model using OWAgeneric model. The OWA model integrates a pair-wisecomparison method and quantifier-guided OWA aggregationoperators to form a groundwater pollution potential mappingmethod that incorporates different decision strategies. WithOWA operators, ANDness, ORness, and Trade-off parameterswere calculated as a function of fuzzy (linguistic) quantifiers.The calculated parameters lies between the aggregations thatuses “AND” operator (which requires all the criteria to besatisfied) and OR operator (which requires at least onecriterion to be satisfied). The model results in multiplegroundwater vulnerability prediction scenarios, which applydifferent decision strategies and provide users with theflexibility to select one of them based on the level of riskcontrols in decision-making process. The risk adverse model

associated with OWA AND operator was selected forgroundwater vulnerability prediction map in the area. Theresults showed that predominant portions of the area belongedto the no vulnerable zones. The model was validated withgroundwater quality data, and results show a strongrelationship between the groundwater vulnerability modeland pH, NO3, Ca, Fe, and Zn concentrations whosecorrelation coefficients are 0.50, 0.55, 0.60, 0.69, and 0.91,respectively. The results obtained confirmed that themethodology hold significant potential to support thecomplexity of decision making in evaluating groundwaterpollution potential mapping in the area.

Keywords OWA .Analytic hierarchy process .

Vulnerability . DRASTIC . Anthropogenic . GVPM

Introduction

Integrating vulnerability assessments into groundwaterresources development is a means of maintaining andmonitoring the quality status of groundwater. This method isrequired because these non-renewable resources areinherently susceptible to contamination from land uses andanthropogenic impacts (Thirumalaivasan et al. 2003).Therefore, groundwater protection against contamination iscritical for water resources planning and management.Vulnerability mapping entails quantifying the sensitivity ofresources to the environment and is a practical visualizationtool for decision making (Rahman 2009; Nowlan 2005). Inhydrogeology, vulnerability assessment is typically utilized toassess the susceptibility of a water table, aquifer, or water wellto contaminants that can reduce groundwater quality.Antonakos and Lambrakis (2007) and Celalettin and Unsal(2008) reported that constructing a map of groundwater

K. A. MogajiDepartment of Applied Geophysics, Federal University ofTechnology, P.M.B. 704, Akure, Nigeria

K. A. Mogaji (*) :H. S. Lim :K. AbdullahSchool of Physics, Universiti Sains Malaysia, 11800 Penang,Malaysiae-mail: mogakeh@yahoo.com

H. S. Lime-mail: hslim@usm.my

K. Abdullahe-mail: khirudd@usm.my

Arab J GeosciDOI 10.1007/s12517-013-1163-3

vulnerability constitutes an effective and feasible way toprotect groundwater for quality assessment and management.In order to actualize this objective, detailed geological,hydrogeological, and environmental studies should be carriedout (Fredrick et al. 2004).

The assessment of groundwater vulnerability to pollutionhas been subjected to intensive research, and a variety ofmethods have been developed. These methods include thefollowing: (1) overlay and index methods, (2) process-basedmethods that apply deterministic models based on physicalprocesses, and (3) statistical models (National ResearchCouncil 1993). The pros and cons of these methods aredetailed in the studies of Jessica and Sonia (2009), Nobreet al. (2007), and Antonakos and Lambrakis (2007). Amongthese methods, the overlay and index methods are the mostwidely used because of their simplicity and ease inapplication. The DRASTIC model, which includes sevenparameters—depth to groundwater, net recharge, aquifermedia, soil media, topography, impact of vadose zone, andhydraulic conductivity—is under this category. Severalscientific studies have used this rating model (DRASTICmodel theory) for vulnerability modeling with successfulresults. However, these prior studies neglected user’s decisionstrategies (Napolitano 1995; Melloul and Collin 1998; Pizaniet al. 2002; Thirumalaivasan et al. 2003; Babiker et al. 2005).According to Nadi and Delavar (2011), the decision strategyrepresents the spectrum of the user’s desires and satisfaction,which ranges from a situation in which all the criteria aresatisfied to the satisfaction of at least one criterion. In the priorstudy of Yager (1988), ordered weighted averaging (OWA)operators method was used successfully to model a family ofparameterized decision strategies. The incorporation of user’sdecision strategies into DRASTIC model approach ofgroundwater vulnerability assessment using OWA operatorsmethod will provide a more practicable result. Besides, thisconcept will give an excellent insight into ranking criteria andaddressing uncertainty from their interaction (Yager 1988).

In previous environmental research, a number of geographicinformation system (GIS) methods and techniques have beendeveloped to enhance methodologies for knowledge-baseddecision support systems. The integration of multi-criteriaevaluation (MCE) techniques and GIS in environmental impactstudies is not an exception. The OWA is a relatively new MCEcombination that is analogous to weight linear combination(WLC) but which considers two sets of weights reported byGorsevski et al. (2012). On the other hand, OWA is a genericmulti-criteria analysis procedure that provides a platform foranalyzing, evaluating, and prioritizing decision (or evaluation)strategies (Malczewski 1999; Rinner and Malczewski 2002).The OWA method not only provides a single “optimal”solution but also generates a combination of solutions thatcan be further examined to develop decision or evaluationscenarios (Gorsevski et al. 2012). In OWA modeling, fuzzy

membership function is used for criteria standardization toenhance decisional uncertainty management (Zadeh 1965,1978). This fuzzy set theory has been combined with WLCapproaches for the purpose of solving or supporting spatialreasoning problems in a number of different contexts(Ekmekciog˘lu et al. 2010 and Moeinaddini et al. 2010).OWA method has been used in a number of investigations,including integrating the OWA concept as a component ofmulti criteria decision analysis (MCDA) (Fodor and Roubens1992; Fuller 1996; Herrera et al. 1996). Eastman (1997)implemented the OWA as a module in the IDRISI software,which is also used by Jiang and Eastman (2000) for land usesuitability assessment. Malczewski (2006) incorporated theconcept of fuzzy linguistic quantifiers into GIS-based landsuitability analysis using OWA and showed how a wide rangeof multi-criteria decision rules strategies could be obtained byapplying the appropriate fuzzy quantifiers. Malczewski andRinner (2005) also included fuzzy quantifier-guided OWA inthe common GIS OWA module and used it to evaluateresidential quality. Boroushaki and Malczewski (2008)implemented an extension of the AHP technique with fuzzyquantifier-guided OWA in ArcGIS software. All of thesestudies intended to incorporate users’ needs to provide a moresatisfactory solution for environmental decision-makingprocess. However, no investigations have studied OWAoperator combination with DRASTIC model theory ingroundwater vulnerability modeling.

Assessing groundwater quality and developing strategies toprotect aquifers from contamination are necessary in planningand designing water resources management. Groundwaterquality assessment involves the determination ofconcentrations of physical and chemical soluble parametersfrom the weathering of source rocks and anthropogenicactivities, but there is no record of groundwater pollution inthe study area. However, the assessment of groundwaterquality analysis will give a tremendous insight intomonitoring groundwater quality status in the area.

The purpose of this study was to develop an OWA-basedDRASTIC model for groundwater vulnerability assessment.These two techniques were chosen because of their ability todeal with multiple decision makers and heterogeneous datatypes. The DRASTIC theory was explored for defining thegroundwater pollution potential rating of the selectedparameters/factors. The OWA technique was used becauseits aggregation procedure incorporates uncertainty through afuzzy membership function and expert opinions. The mainmerit of OWA method is its ability to provide leverage forcontrolling the level of uncertainties associated with differentdecision alternatives and risk taking (i.e., optimistic,pessimistic, and neutral). Hence, this hybrid methodologycan show case-flexible ways to manage decisional uncertaintyin groundwater pollution potential prediction. The proposedapproach is illustrated using a case study in the southern part

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of Perak, Malaysia regarding evaluation and management ofgroundwater resources in the area. The study will produce agroundwater vulnerability prediction map (GVPM) with highreliability and precision. Furthermore, the produced GVPMwill be compared with groundwater quality analyzed resultsobtainable in the area.

Materials and methods

Geography, hydrology, and hydrogeology of the study area

The study area is located on the southern part of Perak andshared boundary with Selango in Peninsular Malaysia. Thearea is bounded by longitudes 101°0′E–101°40′E andlatitudes 3°37′N–4°18′N and covers an area of around 2,884 km2 (Fig. 1). The area is having four major underlyingrock types defined by geological age, namely, Quaternary(mainly recent alluvium), Devonian (sedimentary rocks),Silurian (sedimentary rocks with lava and tuff), and acidicand undifferentiated granitoids as depicted in Fig. 2. Thesubsurface hydrogeological analysis of the rock types fromthe available borehole logs obtained from the MalaysianDepartment of Mineral and Geosciences revealed differentlithological variations at varying depths. The aquiferous unit

underlying the alluvium is made up of sand and gravel, silt,and clay with thick column of clay overlying it, which isregarded as aquitard (Minerals and Geoscience Department,Malaysia 2004). The Devonian is associated with BelataFormation. Its aquifer unit has a higher clay content than sandand is unconfined. The Silurian has a residual soil thatcomprises clay silt overlying layers of sand silt. Its aquiferunit contains mostly weathered shale and high clay content.The aquifer in Silurian rock is confined by low-permeabilitysoil column. The igneous geological formation is a crystallinerock that is associated with granite and Terolak Formationmetasediment contact. Fractures within the crystalline rocksform the aquiferous unit and are mostly confined. In summary,the aquifers characterizing the area vary between fractured andweathered layers aquifers. The groundwater potentiality in thearea is largely depending on the constituents of the underlyingaquifers and the overlying materials. The region ischaracterized by equatorial maritime climate with nearlyuniform air temperatures throughout the year. The averagedaily temperature is approximately 27 °C, and relativehumidity has a monthly mean value of 62 % and 78 % forthe dry period and peak of the rainy season, respectively. Thegeneral annual precipitation in Perak state is in the range 830to 3,000 mm. However, 20 years of monthly records ofprecipitation between 1991 and 2010 for the study area were

Fig. 1 Location map of the study area

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acquired at half-degree resolution (0.5°) from the ClimaticResearch Unit (CRU) database established in 1972 referencedby New et al. (2000). The precipitation data were processedusing R programming language code and GIS software toproduce spatial rainfall distribution in the area (Fig. 3).

DRASTIC model

The DRASTIC model was developed in the United Stateswith the support of the U.S. Environmental Protection Agencyand was designed to be a standardized system for evaluatingthe groundwater vulnerability for a variety of land areas (Alleret al. 1987). It was based on the concept of thehydrogeological setting that is defined as compositedescription of all the major geologic and hydrologic factorsthat affect and control the groundwater movement into,

through and out of an area. The term “DRASTIC” stands forseven parameters in the model which are depth togroundwater table (D), net recharge (R), aquifer media (A),soil media (S), topography (T), impact of vadose zone (I),and hydraulic conductivity (C) (see Table 1). Rating andweight are assigned to each parameter’s range to indicatetheir relative importance within the area in contributing togroundwater vulnerability assessment. The linear additiveof the aforementioned variables is usually combined usingratings and weights to determine the DRASTIC index(Aller et al. 1987). DRASTIC index is expressed asfollows:

DRASTIC index ¼ DrDw þ RrRw þ ArAw þ SrSw

þ T rTw þ I rIw þ CrCw

ð1Þ

Fig. 2 Geological map of thearea

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where D , R , A , S , T, I , and C represent the seven parameters,with r being the rating and w being the weight assigned to eachparameter. A high DRASTIC index value indicates largegroundwater pollution potential. Thematic layers for each ofthese parameters were the criterion maps considered for theOWA modeling approach.

Layer preparation of the DRASTIC model parameters

In the current study, the DRASTIC model with its sevenparameters was derived from different data sets. Table 1highlights the information and sources of data sets used inthis study. The depth-to-water table (D) is one of the mostimportant factors in evaluating pollution potential because itprovides a greater chance for natural attenuation in terms ofdispersion, absorption, and biodegradation to occur as thedepth to water increases (Ckakraborty 2007). This studyconsidered data determined based on water level

measurements performed in 50 bore wells drilled in the year2009 and uniformly spread in the entire study area to producea depth-to-water table thematic map. This map was evaluatedfor pollution potential mapping based on the ranges of depth-to-water table distribution in the area. The hydrologicalimplication of the map indicated that at a shallower watertable, the groundwater reservoir is more susceptible topollution and vice versa (Atiqur Rahman 2008). Hence, thepollution potential weights and ratings of depth to waterthematic map classes are in accordance with the Delphitechnique (Aller et al. 1987) (see Table 2). This study adopted20 years of monthly records of precipitation between 1991and 2010 to estimate net recharge in the area. The modifiedversion of the Chaturvedi (1973) formula (Eq. 2) was used tocompute the net recharge due to rainfall for the area using theprocessed precipitation data:

R ¼ 1:35 P−14ð Þ0:5 ð2Þ

Fig. 3 Map showing the averagerainfall distribution for the studyarea

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where R is the net recharge from precipitation during the yearand P is annual precipitation. The obtainable net rechargevalues were interpolated to generate net recharge (R) map inGIS environment. The net recharge represents the annualaverage amount of water that infiltrates the vadose zone andreaches the water table (Aller et al. 1987). The higher the netrecharge, the more vulnerable is the groundwater reservoir.This was supported in Samake et al. (2011) who reported thatrecharge controls the volume of water available for dispersionand dilution of the contaminant in the vadose zone. Theaquifer medium (A) is a water-bearing formation that yieldsuseable quantities of water from a well or spring (Heath 1987).These parameters determine the attenuation capacity ofpollutants. The aquifer medium was delineated from theinterpreted 2D resistivity imaging data acquired in the area(see Fig. 4). In this study, each geophysical measurement wasdesigned as close as possible to the existing boreholes in thearea which guide the interpretation of the inverted 2Dsections. The root mean square errors of our interpretationare in the range 3.4 % and 10.8%which fall within acceptablelimits as shown (Fig. 4). Based on the resistivity parameter ofthe media, the nature, texture, porosity, and permeability wereevaluated. These properties play important roles in thefiltering ability of this media. The attenuation capacity ofpollutants tends be lower with porous and highly permeableaquifer media and vice versa (Anwar et al. 2003). Hence, thepollution potential rating of the aquifer mediummap producedwas evaluated accordingly (see Table 2). Furthermore, theimpact of soil media (S) on pollution potential mappingcannot be overemphasized. The amount of rechargeinfiltrating the ground surface, the amount of potentialdispersion, and the purifying process of contaminants arestrongly influenced by the underlying soil type in the area(Anbazhagan et al. 2005). The soil media in this study wasprepared based on the 1962 district soil type map of Malaysia,which was obtained from the Department of Agriculture,Ministry of Agriculture in Kuala Lumpur, Malaysia (seeTable 1). The varying properties of the different soil types

were used in the rating pollution potential of this layer(Table 2). The topography (T) for this evaluation was derivedbased on the sloping factor of the area which is in accordancewith Chekirbane et al. (2013) who reported that topography isthe slope variability of an area. The degree of slope, whichvaries from one place to another, controls the likelihood thatrainfall and the contained pollutants have to run off or beretained in one area long enough to infiltrate it. The slopelayer is derived from the processed ASTER DEM image of30 m resolution [downloaded from NASA’s land processesdistributed active archive center (LP DAAC)]. This layer wasinterpreted by adopting a similar approach discussed in priorstudies of Adiat et al. (2012) and Doumouya et al. (2012). Anarea with a low slope degree (flat) tends to retain water forlonger time, hence providing greater chance for the infiltrationof recharge water, which may contain a considerable amountof pollutants. Meanwhile, an area with a steeper slope has littlepotential for recharge, thus allowing pollutants littleopportunity to reach the groundwater table. Table 2 showsthe pollution potential evaluation rating of this layer. The roleof vadose zone in evaluating pollution potential rating hasbeen documented in literatures (Chen et al. 2013; Samakeet al. 2011). It represents the ground portion between the watertable and the soil media. It influences the aquifer pollutionpotential depending on the permeability and attenuationcharacteristics of its soil cover media. Hence, if the vadosezone is vastly permeable, then this will lead to a highvulnerable rating (Corwin et al. 1996). The vadose zonethematic layer was generated from the interpretedgeophysical data (see Fig. 4) and evaluated accordingly(Table 2). The hydraulic conductivity parameter (C) is animportant factor that controls contaminant migration anddispersion from the injection point within the saturatedzone and, consequently, within the plume concentration inthe aquifer (Atiqur Rahman 2008). The transmission rateof water in subsurface formation is determined by thisparameter, which in turn controls contaminant movementrate. Therefore, an area underlain with water-bearing

Table 1 Information and sources of data used in this study

Data type Detail of data Format Output layer

Borehole data Malaysian Department of Mineral and GeosciencesDate acquired: 12 Feb 2009

Table and lithology log Depth to water table (D)

Average annual rainfall Climatic Research Unit database CRU 2.1Resolution: 0.5°

Table Recharge (R)

Geophysical data Batang Padang, Perak Point Aquifer (A)

Soil map Ministry of agriculture Kuala Lumpur, Malaysia (1962)Scale: 24 mi = 1 in.

Map Soil type (S)

Remote sensing imagery ASTER DEM data NASA (LP DAAC)Resolution: 30 m

Satellite image Topography/slope (T)

Geophysical data Batang Padang, Perak Point Impact of vadose zone (I)

Borehole data Malaysian Department of Mineral and GeosciencesDate acquired: 12 Feb 2009

Table and lithology log Hydraulic conductivity (C)

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formation (aquifer) with high hydraulic conductivity ismore vulnerable to potential contamination, relative toother formations with low hydraulic conductivity. Thehydraulic conductivity layer was prepared from boreholedata and evaluated for pollution potential mapping(Table 2).

Quantifier-guided OWA method

The OWA technique provides a tool for generating andvisualizing a wide range of multi-criteria evaluation strategiesby applying different operators and the associated set ofordered weights. OWA method employs two set of weights,

Table 2 DRASTIC model parameters ratings and their weights (modified after Atiqur Rahman 2008 and Chen et al. 2013)

The criterion Ranges (classes) Pollution potentialityfor groundwatervulnerability

Rating(unstandardized values)

Assigned weight

Depth to water (D) 0.34–1.58 High 5 5

1.58–1.86 Medium high 4

1.86–2.14 Medium 3

2.14–2.53 Low 2

2.53–4.42 Very low 1

Recharge 195–256 Very low 1 4

(R) 256–289 Low 2

289–315 Medium 3

315–336 Medium high 4

336–382 High 5

Aquifer media (A) 160–250 Ωm (sandy clay/fine sand) Very low 1 3

250–305 Ωm (sand/gravel) Medium 3

305–367 Ωm (medium sand/coarse gravel) Medium high 4

367–445 Ωm (weathered sandstone) Low 2

445–600 Ωm (fractured/jointed sandstone) High 5

Soil media (S) Lithosol and shallow latosol (steep mountain) Very low 1 2

Red and yellow podozolic soil from acidigneous rock

Low 2

Red and yellow latosols podozolic soil fromsedimentary rocks

Medium 3

Red and yellow latosols podozolic soil fromolder and sub-recent alluvium

Medium high 4

Organic soil–peat and poorly drained Very low 1

Low humic gley soil developed in the valleyand flood plain

High 5

Agricultural land Medium high 4

Topography (slope) 0–2.57 (Flat) high 5 1

(T) 2.57–8.77 (Undulating) medium high 4

8.77–15.54 (Rolling) medium 3

15.54–25.83 (Moderately steep) low 2

25.83–40.32 (Steep) very low 1

Impact of vadose zone (I) 63–270 Ωm (clay/silty sand) Very low 1 5

270–388 Ωm (sand/gravel) Medium high 4

388–511 Ωm (fissure sandstone/coarse gravel) High 5

511–793 Ωm (hard sandstone/coarse gravel) Medium 3

793–1,464 Ωm (consolidated/hard rock) Low 2

Hydraulic 11–72 Very low 1 3

Conductivity (C) 72–121 Low 2

121–208 Medium 3

208–350 Medium high 4

350–1,853 High 5

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namely global criterion weights (WK) and order weights (Vk).The two sets of weights were solved on the thematic layers ofthe interpreted geologic and hydrologic factors influencingpollution potential in the study area. The seven thematic layersderived from the DRASTIC parameters discussed in the“Layer preparation of the DRASTIC model parameters”section were identified as criterion maps and then used asinput in OWA modeling procedures. It is important to notethat these criteria were obtained from different data sourcesand with varying scales, and as such, they were transformedinto a standardized scale using the following methods (Carver1991):

rk ¼ maxi bikf g−mini bikf g; ð3Þ

aik ¼ maxi bikf g−bikrk

; for k � thcriterion ð4Þ

where bik refers to the raw (unstandardized) criterion value(see Table 2); max i{b ik} and min i{b ik} indicate themaximum and minimum criterion value of the k -th criterion,respectively; rk is the global range of the k -th criterion; andaik is the standardized criterion value, ranging from 0 to 1,where 0 is the least desired value and 1 indicates the mostdesired value (see Table 3). The centroids of the grid cells ineach criterion were generated using the fishnet module in GISenvironment and x and y grid coordinates of the centroidswere observed. Equation 4 was applied to each of the criterionmaps derived with DRASTIC parameters (see Fig. 5). Eachcriteria map was identified with criterion weight (Wk) called

BH 38

Vadose layerAquifer media

BH 14

Vadose layerAquifer media

BH 26

Aquifer mediaVadose layer

a

b

c

Fig. 4 Examples of 2D sectionsshowing how aquifer media andvadose layer were delineated

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global criterion weights: w1,w2.....,wn(wk∈[0,1],∑k =1n wk=

1). This weight represented the preferences (of the decisionmaker or expert) with respect to each criterion to indicate theirrelative importance. On the other hand, order weights (v ik) is

the weight assigned to ordered criteria associated withcriterion values on a location-by-location basis.

Given the set of criterion maps originated from DRASTICparameters, the function of OWA scores was defined asfollows (Yager 1988):

OWAi ¼X N

K¼1vikzik ð5Þ

zik ¼ aikwk ð6Þwhere OWAi is the overall OWA score of the i -th location oralternative, v ik is the order weight, and z ik is the orderedweighted criterion value obtained by sorting the weightedstandardized criterion values (aikwk) for each alternative orlocation in a descending order (Yager 1988; Malczewski andRinner 2005). The v ik (order weight) is expressed in Eq. 6 asdefined by Yager (1996) and Malczewski (2006):

vik ¼X k

j¼1uij

� �α

−X k−1

j¼1uij

� �α

;α > 0 ð6Þ

Table 3 Standardized criterionvalue (SCV) calculation

aik SCV, D depth of water table,R recharge, A aquifer media, Ssoil media, T topography, Iimpact of vadose zone, Chydraulic conductivity

Grid no. x y aik_D aik_R aik_A aik_S aik_T aik_I aik_C

0 754,386.28 468,233.36 0.5 0.5 0.25 0.5 0.25 0.5 0

1 765,042.46 468,233.36 0.75 0.5 1 0 0.25 0 0

2 743,977.93 456,833.73 0.5 0.75 0.5 0.75 0 0.75 0

3 754,386.28 456,833.73 0.75 0.75 0.75 0.75 0.25 1 0

4 765,042.46 456,833.73 0.75 1 0 0.5 0.75 1 0

5 775,450.82 456,833.73 0.75 0.75 0.5 0 0.5 1 0.25

6 733,321.75 445,434.10 0.5 0.5 1 1 0 0.75 0

7 743,977.93 445,434.10 0.5 0.5 0.75 1 0 0.75 0

8 754,386.28 445,434.10 0.5 0.5 1 1 0 1 0

9 765,042.46 445,434.10 0.75 0.75 1 0.75 0.25 0.5 0

10 775,450.82 445,434.10 0.5 0.5 0.5 0 0.5 1 0.25

11 733,321.75 434,034.46 0.75 0 0 0 0 0 0.25

12 743,977.93 434,034.46 0.75 0 0 0 0.75 0.75 0

13 754,386.28 434,034.46 0.75 0.25 0.75 0.75 0.25 1 0.25

14 765,042.46 434,034.46 0.25 0 0.5 0.5 0.75 0.5 0.25

15 775,450.82 434,034.46 0 0.25 0.75 0.5 0.5 0 0.5

16 786,107.00 434,034.46 0 0.25 0.75 0 0.75 0 0.75

17 733,321.75 422,882.65 1 0 0.5 0.5 0.75 0.75 0.5

18 743,977.93 422,882.65 0.75 0 0.5 0.5 1 1 0.25

19 754,386.28 422,882.65 0.25 0.25 0.5 0.5 0.25 1 0

20 765,042.46 422,882.65 0.25 0 0.75 0.75 0 1 0.25

21 775,450.82 422,882.65 0 0.25 0.25 0.75 0.25 0.75 0.75

22 786,107.00 422,882.65 0.25 0.25 0.75 0.5 1 0 0.75

23 743,977.93 411,235.20 1 0.25 0.5 0.5 1 0.5 0.25

24 754,386.28 411,235.20 1 0.25 0.5 0.75 0.5 0.5 0.75

25 775,450.82 411,235.20 0 0.25 0 0.5 0 0 1

26 786,107.00 411,235.20 0.25 0.25 0.75 0.5 0.25 0.75 0.75

Fig. 5 Typical GIS and MCA spatial criterion maps modeling (modifiedafter Xinyang Liu 2013)

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where u ij is the ordered criterion of weight and α is theparameter associated with the fuzzy quantifier. The value ofα or the quantifiers ranging from “all” to “at least one”determined the number of OWA operators that can begenerated (see Table 4). The set of order weights (v ik)determined the type of OWA operators. In order to determinethe level of risk associated with various types of OWAoperators, the Andness, ORness, and Trade-off parameterswere estimated using Eqs. 7, 8, and 9. These parameterscharacterize the nature of OWA as a measurement of risktaking in the decision-making process for the purpose ofcontrolling the level of risk in spatial prediction (Jiang andEastman 2000; Rinner and Malczewski 2002).

ANDness ¼X n

k¼1

n−kð Þn−1

vik ; 0≤ANDness≤1 ð7Þ

ORness ¼ 1−ANDness ð8Þ

Trade−off ¼ 1−

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffinX n

k¼1

vik− 1n

� �2n−1

s; 0≤Trade−off ≤1 ð9Þ

where n is the number of criteria and v ik is the weight for thecriterion of the k -th order.

Application of quantifier-guided OWA operatorsto groundwater vulnerability mapping

In order to incorporate user’s decision strategies ingroundwater vulnerability mapping, the quantifier-guidedOWA operators approach was adopted. The generic modeluses the two sets of weights discussed in “Quantifier-guidedOWA method” section in its aggregation procedures and wereused to generate a wide range of decision alternative forgroundwater vulnerability modeling. The first sets of weights,which are the global weights of the criterion, were evaluatedbased on the principle of analytics hierarchies processes(AHP). The AHP here was used to determine the criterionweight for the purpose of representing the relative importanceof the selected criterion during the multi criteria interaction.According to Malczewski (1999), AHP helps in reducing thecomplexity of the decision problem by considering two criteriaat a time, with each criterion being scored according to itsrelative influence/importance on the pollution potentialevaluation. Using Saaty’s nine-point scale (Table 5) and theexpert weight assigned to the DRASTIC model parametersused in Chen et al. (2013) (see Table 2), the pair-wisecomparison matrix shown in Table 6 was developed. Thepair-wise comparison was normalized, where each cell of the

Table 4 Properties of regularincreasing monotone quantifierswith selected values of parameterα (source: Malczewski 2006)

a The set of order weights dependson values of sorted criterionweights and parameter αaccording to Eq. 6

Parametersα Quantifier (Q) Order weights (vik) GIS combination procedure ORness Trade-off

α→0 At least one vi1=1;vik=0

(1<k ≤n)OWA (OR) 1.0 0

α→0.1 At least a few a OWA a a

α→0.5 A few a OWA a a

α→1 Half (identity) vik ¼ 1�n;

(1≤k ≤n)

OWA (WLC) 0.5 1

α→2 Most a OWA a a

α→10 Almost a OWA a a

α→∞ All vin=1;vk=0

(1≤k<n)OWA (AND) 0 0.0

Table 5 Saaty scale for pair-wise comparisons (Saaty and Vargas 1991)

Score Judgment Explanation

1 Equally Two factors contribute equally to the objective

3 Slightly favor Slightly favor one attribute over another

5 Strongly favor Strongly favor one attribute over another

7 Very strongly Strongly favor one attribute with demonstratedimportance over another

9 Extremely Evidence favoring one attribute over anotheris one of the highest possible order ofaffirmation

2, 4, 6, 8 Intermediate The intermediate values are used whencompromise is needed

Reciprocals Values for inverse comparison

Table 6 A pair-wise comparison matrix for calculating criteria weightsfor pollution potential mapping using DRASTIC parameters

D R A S T I C Weights CR

D 1 3 5 7 9 1 5 0.31 0.039

R 1/3 1 3 5 9 1/3 3 0.16

A 1/5 1/3 1 3 5 1/5 1 0.08

S 1/7 1/5 1/3 1 3 1/7 1/3 0.04

T 1/9 1/9 1/5 1/3 1 1/9 1/5 0.02

I 1 3 5 7 9 1 5 0.31

C 1/5 1/3 1 3 5 1/5 1 0.08

The consistency ratio CR=0.039<0.1 indicates that the global criterionweights are based on a consistent set of pair-wise comparisons

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Fig. 6 Global criterion map derived from DRASTIC parameters

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Fig. 6 continued.

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matrix was divided by its column total. The associated criterionweights (wk) were obtained by computing the average value ofthe normalized pair-wise comparisons. The consistency ratio(CR) obtained for the pair-wise comparison matrix was 0.039(see Table 6). According to Feizizadeh and Blaschke (2013),CR=0.039 indicates that the comparisons of characteristics areperfectly consistent and that the relative weights is appropriatefor OWA modeling. The associated criterion weights (wk)obtained multiplied by the values of each aik in the k -thlocation digitized from the standardized global criterion maps(Fig. 6) generated using Eqs. 3 to 4 (see Table 3) give the valueof aik wk. The estimated values for the aik wk, when sorted indescending order, yielded the zik sequence for each criterion.

The second set of weights is the order weights (vik), which areassigned on a location-by-location basis, where descending orderof weighted criteria control aggregation. The vik was computedusing Eq. 6. Table 7 shows the computed order weights of sevencriteria based on different values of α (Table 4).

The OWA index scores were determined for each i -thlocation using Eq. 5, based on vik and zik values. The OWAindex values obtained at each centroid location based on thedifferent decision strategies enabled generation of a wide rangeof different vulnerability predictive scenario maps (Fig. 7).

Determination of water quality index

Approximately 50 boreholes spatially spread across themultifaceted geological setting were extensively sampledand analyzed to determine the subsurface water quality inthe area. Groundwater samples were collected by turning thebore pump on and allowing it to operate for approximately30min until water was freshly drawn from the aquifer. Exactly15 L of groundwater samples from each borehole wascollected in 600-mL sterilized polyethylene bottles. Thesamples were stored at 4 °C prior to analysis. The analysisconsidered the physical and chemical parameters of the watersamples. Qualitative analyses were conducted throughstandard laboratory procedures in accordance with APHA(2005). Physical parameters tested were pH and totaldissolved solid (TDS). Chemical parameters analyzed were

nitrate, sulfate, potassium, and calcium, along with heavymetals, such as total iron, zinc, and manganese. A portablepH meter was utilized to determine the pH in accordance withthe APHA 4500-H+B method. Inductively coupled plasmamass spectrometry was performed to determine the heavymetal content of minerals, such as zinc, calcium, andmanganese. Results were compared with those obtained bythe World Health Organization (WHO) and FAO standards.

Results and discussion

The user’s strategies decision predictive scenarios

Figure 7 shows a total of six user’s decision strategiesalternative solutions for groundwater vulnerability predictionassociated with the DRASTIC model parameters. Thesevarying predictive scenarios were generated using theestimated OWA index values for each grid based on theOWA weights shown in Table 7. The measures and controlsof level of risk for each user’s decision strategies aredetermined by the computed ANDness, ORness, and Trade-off parameter values (see Table 7). Using the natural breaksclassificationmethod (Ayalew et al. 2004; Kritikos and Davies2011), the OWA index values interpolated in GISenvironment were broken down into class boundariesrepresenting regional vulnerable zones in the study area. Fivevulnerable zones identified in the study area, which include novulnerable (NV), very low vulnerable (VLV), low vulnerable(LV), moderate vulnerable (MV), and high vulnerable (HV),were based on the ascending order of OWA range values foreach class boundaries which varies from one decision strategyscenario to another (see Table 8). The decision alternative(Fig. 7a) is associated with OR operator and produces a risk-taking solution (Bell et al. 2007; Nadi and Delavar 2011;Gorsevski et al. 2012). The cardinal description of thisalternative shows that the NV zones were located in isolatedspots mainly in the northern part of the area relative to NVspatial extent in Fig. 7b. The central, western, and southernparts of Fig. 7a were covered by the VLV, LV, MV, and HV

Table 7 OWAweights used to control levels of trade-off and risk for the pollution potential factors based on selected linguistic quantifiers

Decision strategy Order weights (n =7) ANDness ORness Trade-off

w1 w2 w3 w4 w5 w6 w7

At least one (OR operator) 1.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00

Most 0.03 0.08 0.11 0.14 0.17 0.20 0.25 0.66 0.34 0.81

Almost 0.00 0.00 0.01 0.04 0.10 0.21 0.63 0.90 0.10 0.34

Half (mean operator) 0.14 0.14 0.14 0.14 0.14 0.14 0.14 0.50 0.50 1.00

A few 0.34 0.16 0.13 0.11 0.10 0.09 0.08 0.34 0.66 0.75

All (AND operator) 0.00 0.00 0.00 0.00 0.00 0.00 1.00 1.00 0.00 0.00

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Fig. 7 Different vulnerability scenarios from DRASTIC factors derived by quantifier guided OWA operator

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zones in sandwiched nature and its solution has trade-off value0. Figure 7b is associated with the AND operator andproduces a risk-averse solution model. The NVarea with thisdecision strategy scenario has the very largest spatial extent inthe area in comparison with other scenarios solutionspredicted. The other zones including VLV, LV, MV, and HVzones in sandwich pattern underlie the southernmost part ofthe area. The decision strategy scenario in Fig. 7c is associatedwith the convectional WLC aggregation which is a meanoperator quantifier. Here, the NV area is mainly distributedat the southwestern part of the area and its spatial extent islesser compared with that of Fig. 7a and b. This solution is

associated with a very high level of risk and it is characterizedwith full trade-off of value 1. Figure 7d, associated with OWAMOST operator, shows more spatial extent area in the HVzones relative to that of Fig. 7c. Its solution pattern allowssome trade-off (0.81) with increase in level of risk. Figure 7eand f shows the decision strategies associated with A FEWand ALMOST OWA operators, respectively. These solutionalternatives equally showed varying degrees of vulnerabilityprediction in the area. Both HV and MV areas with A FEWoperator map have the largest spatial area extent in the area.Contrarily, the VLV and LV areas occupy the largest areaextent in ALMOST OWA operator scenario solution.

Fig. 7 continued.

Table 8 Groundwater vulnerability potential classification obtainable

Ordered weight average (OWA) index values

Classifications OR AND HALF MOST A FEW ALMOST

NV 0.0001–0.0715 0–0.0009 0.0200–0.0445 0.0083–0.0219 0.0353–0.0693 0–0042

VLV 0.0715–0.1356 0.0009–0.0031 0.0445–0.0610 0.0219–0.0303 0.0693–0.0949 0.0042–0.0065

LV 0.1356–0.1986 0.0031–0.0067 0.0610–0.0762 0.0303–0.0387 0.0949–0.1179 0.0065–0.0094

MV 0.1986–0.2604 0.0067–0.0125 0.0762–0.8891 0.0387–0.0465 0.1179–0.1388 0.0094–0.0141

HV 0.2604–0.3100 0.0125–0.0200 0.8891–0.1082 0.0465–0.0582 0.1388–0.1691 0.0141–0.0212

NV no vulnerable, VLV very low vulnerable, LV low vulnerable, MV moderate vulnerable, HV high vulnerable

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Moreover, the Fig. 7e and f scenarios solution allowed partialtrade-offs of 0.75 and 0.34 (Table 7), respectively. Theobtainable results from the decision strategy scenariosprediction show that low-risk decision making is associatedwith least vulnerable areas because aggregation requires high

vulnerability modeling from all criteria. On the other hand,high-risk decision making suggested the high vulnerablezones because aggregation requires a high vulnerabilitymodeling for one of the criteria. The hybrid methodologyhas demonstrated the strengths associated with the weightingflexibility of the OWA approach by providing a robustinteractive toolset for adjusting trade-offs and compensationbetween criteria that allows a rapid assessment andinterpretation of possible alternative scenarios involved inthe decision-making process. Hence, the user’s decisionstrategies incorporated with the use of quantifier-guidedOWA operators and DRASTIC model theory produces morereal results for characterizing groundwater vulnerable zones inthe area.

The OWA AND operator prediction scenario solution(Fig. 7b) is, however, considered appropriate for representingthe groundwater vulnerability prediction map (GVPM) for thearea because its level of risk aversion in decision making is

Fig. 8 Groundwatervulnerability prediction map ofthe study area

Table 9 Area and proportion of groundwater vulnerability zones in thearea

Groundwater vulnerability zones Area (km2) Percentage (%)

NV 2,095 73

VLV 479 17

LV 149 5

MV 117 4

HV 23 1

NV no vulnerable, VLV very low vulnerable, LV low vulnerable, MVmoderate vulnerable, HV high vulnerable

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higher and hence will produce a prediction of a higherreliability and precision better than other operator scenariossolutions. The produced GVPM for the area is depicted inFig. 8.

Distribution of vulnerable zones in the area

In the GIS environment, the area and percentage distributionof the different groundwater vulnerable zones shown in Fig. 8were evaluated (see Table 9). An area of 2,095 km2,accounting for 73 % of the total study area, belonged to theNV zone. The VLV and LV zones accounted for 17 %(479 km2) and 5 % (149 km2) of the total study area,respectively. Hence, more than three quarters (95 %) of thetotal study area were relatively protected from pollution. TheMV and HV zones accounted for 4 % (117 km2) and 1 %(23 km2) of the total study area, respectively.

Model validation

Model validation was carried out by correlating GVPM(Fig. 8) with the analyzed groundwater quality resultsobtained in the area. Validation was conducted to verify theprediction accuracy of the GVPM using trace elementconcentrations in groundwater samples obtained in the area.The determined physiochemical parameters and heavy metalswere assumed to be completely soluble in water. It should be

noted that a rare reference on groundwater contaminationproblem was present in the area. Hence, this study aimed toassess impending contamination and identify futurevulnerable zones. Contamination of the underlain aquifer unitsmay then be addressed, given that contamination may beinevitable because of the nature of human activities in the areaincluding oil palm plantation and other agricultural practices.Concentrations of the determined physiochemical parametersand traceable heavy metal pollutants considered for validationinclude pH, TDS, nitrate (NO3), sulfate (SO4), potassium (K),calcium (Ca), iron (Fe), and zinc (Zn). Validation wasperformed by comparing the analyzed sample parametersresults with those indicated by WHO and FAO standards forwater quality evaluation. Tables 10, 11, 12, 13, and 14 showthe analyzed results of the physiochemical parameters and theheavy metal analyzed across the predicted vulnerable zones.The estimated mean concentrations value of TDS, NO3, Ca,K, and SO4 across the NV, VLV, LV, MV, and HV zones weremuch lesser than the maximum permitted values in theanalyzed borehole samples based on the WHO and FAOstandards. However, the mean concentration values for pHyielded 5.28, 5.17, 5.56, 5.56, and 5.4 across the NV, VLV,LV, MV, and HV zones, respectively. Based on the WHO andFAO standards, the underground water in the area is acidic,which, according to Akinbile and Mohd (2011), indicates thepresence of metals, particularly toxic metals. Hence, abioremediation measure is suggested for possible correction

Table 10 Physiochemical andheavy metals constituents in thesampled boreholes located in theNV zone and their comparisonswith WHO and FAO standards

The pH is dimensionless; exceptotherwise stated, all units are inmilligrams per liter

NV no vulnerable

BH-NAME Physiochemical parameters Heavy metals

pH TDS NO3 Ca K SO4 Fe Zn Mn

WHO 6.5–8.5 500 50 75 – 20 0.5–50 3 0.1

FAO 6.5–8.5 2,000 10 20 – 20 – 0.01 –

JMG 44 4.7 43 0.10 1.20 1.70 3.30 19.20 0.01 0.25

JMG 51 5.1 28 0.00 3.10 3.90 1.00 34.00 0.07 0.52

JMG 36 5.3 31 0.10 2.50 2.30 0.20 46.30 0.10 0.37

JMG 39 5.5 90 0.00 3.20 2.20 0.50 30.70 0.07 0.48

JMG 22 5.8 46 0.05 2.70 2.30 0.20 52.00 0.09 0.54

Mean 5.28 48 0.05 1.83 2.48 1.4 36.44 0.07 0.43

Table 11 Physiochemical andheavy metals constituents in thesampled boreholes located in theVLV zone and their comparisonswith WHO and FAO standards

The pH is dimensionless; exceptotherwise stated, all units are inmilligrams per liter

VLV very low vulnerable

BH-NAME Physiochemical parameters Heavy metals

pH TDS NO3 Ca K SO4 Fe Zn Mn

WHO 6.5–8.5 500 50 75 – 20 0.5–50 3 0.1

FAO 6.5–8.5 2,000 10 20 – 20 – 0.01 –

JMG 9 4.9 29.00 0.00 1.60 1.60 7.10 28.20 0.10 0.38

JMG 34 5.5 30.00 0.20 1.80 2.00 0.70 24.50 0.10 0.48

JMG 25 5.1 38.00 0.00 1.80 1.90 1.50 20.20 0.10 0.43

Mean 5.17 32.33 0.07 1.73 1.83 3.1 24.3 0.10 0.43

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of the pH anomaly. The general assessment of thegroundwater quality in the area is relatively in good status.Furthermore, the results of the heavy metal analysis shown inTables 10, 11, 12, 13, and 14 indicated that Fe concentrationvalues are relatively high across the predicted vulnerablezones. However, these concentration values fall within thepermissible level based on WHO standards. The Mn tracesin the samples had mean concentration values in the range0.29 to 0.66 mg/L across the predicted vulnerable zones. Withthe measured WHO standard, these Mn concentration valuesindicated some traces of water pollutants. The Mncontamination is not limited to the direct infiltration withinthe scope of our model prediction but rather the infiltration of

such heavy metal into groundwater could only be throughindirect recharge means from surrounding water bodies (DeVries and Simmer 2002). The zinc concentration values in therange 0.07 to 0.1 mg/L were also much lesser than themaximum permitted values based on WHO standard (WHO2004). All these comparisons are relatively confirming theprediction accuracy of the GVPM (Fig. 8) for the area. Theresults of coefficient of statistical correlation conducted todetermine the degree of association between the areapercentages of the predicted vulnerable zones (VLV, LV,MV, and HV) and the means of the analyzed parametersconcentration in the area including the measured pH, NO3,Ca, Fe, and Zn yielded 0.50, 0.55, 0.60, 0.69, and 0.91,

Table 12 Physiochemical andheavy metals constituents in thesampled boreholes located in theLV zone and their comparisonswith WHO and FAO standards

The pH is dimensionless; exceptotherwise stated, all units are inmilligrams per liter

LV low vulnerable

BH-NAME Physiochemical parameters Heavy metals

pH TDS NO3 Ca K SO4 Fe Zn Mn

WHO 6.5–8.5 500 50 75 – 20 0.5–50 3 0.1

FAO 6.5–8.5 2,000 10 20 – 20 – 0.01 –

JMG 38 4.80 29.00 0.10 2.80 3.50 0.30 25.10 0.01 0.51

JMG 41 5.50 43.00 0.03 2.50 3.00 0.20 30.70 0.10 0.35

JMG 43 5.40 56.00 0.00 2.40 3.10 0.40 31.50 0.10 0.32

JMG 28 6.80 58.00 0.10 2.80 2.50 1.50 42.50 0.03 0.52

JMG 27 5.80 27.00 0.00 1.20 1.80 0.60 20.90 0.10 0.19

JMG 45 5.40 57.00 0.20 3.10 5.80 1.90 26.20 0.01 0.67

JMG 35 5.40 50.00 0.10 3.80 6.80 3.40 17.10 0.03 0.91

JMG 21 5.50 38.00 0.68 2.70 1.90 0.20 51.30 0.10 0.24

JMG 2 5.50 338.00 3.90 3.60 3.90 3.80 53.80 0.02 0.50

JMG 4 5.40 100.00 0.05 3.20 2.50 0.20 59.90 0.10 0.26

JMG 33 5.50 21.00 0.00 1.60 2.60 1.00 53.70 0.12 0.54

JMG 47 5.90 63.00 0.00 3.20 2.10 0.50 17.90 0.10 0.28

JMG 50 5.40 2.900 0.10 1.70 1.90 2.90 34.70 0.01 0.76

Mean 5.56 67.92 0.40 2.67 3.18 1.3 25.00 0.10 0.36

Table 13 Physiochemical andheavy metals constituents in thesampled boreholes located in theMV zone and their comparisonswith WHO and FAO standards

The pH is dimensionless; exceptotherwise stated, all units are inmilligrams per liter

MV moderate vulnerable

BH-NAME Physiochemical parameters Heavy metals

pH TDS NO3 Ca K SO4 Fe Zn Mn

WHO 6.5–8.5 500 50 75 – 20 0.5–50 3 0.1

FAO 6.5–8.5 2000 10 20 – 20 – 0.01 –

JMG 23 4.80 68.00 0.2000 4.20 3.70 62.70 36.30 0.04 1.20

JMG 17 5.50 73.00 0.1000 3.80 3.40 18.40 16.90 0.03 0.71

JMG 16 5.40 33.00 0.2500 1.40 1.80 2.40 23.20 0.10 0.45

JMG 19 6.80 59.00 0.0100 5.00 3.60 1.90 37.10 0.00 1.21

JMG 18 5.80 39.00 0.0485 4.50 2.80 1.90 44.80 0.35 1.16

JMG 32 5.40 19.00 0.3000 2.20 2.40 0.30 14.90 0.10 0.39

JMG 31 5.40 10.00 0.1000 0.50 1.90 1.90 4.10 0.10 0.13

JMG 38 5.50 29.00 0.1000 2.80 3.50 0.30 25.10 0.01 0.51

JMG 40 6.20 58.00 0.0477 5.20 3.60 3.10 7.30 0.06 0.20

Mean 5.56 43.00 0.13 3.29 2.96 10.32 23.30 0.09 0.66

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respectively. According to Asuero et al. (2006), the strength ofthese correlations is very high. This finding is in agreementwith the results reported in similar studies of Kalinski et al.(1994) and Lin et al. (1999). The obtained correlation resultsfurther validate the prediction accuracy of the methodproposed in this study.

Furthermore, results of the overlaying analysis of theGVPM (Fig. 8) and the DRASTIC model thematic layersestablished the possibility of the presence of clayey confiningunit (aquifer layers overlain by low permeable materials) andpoorly drained soils in the study area had limited the amountof agricultural chemicals to reach the aquifer. This is inagreement with the interpreted borehole log data of the areaobtained from the Minerals and Geoscience Department,Malaysia. Besides, the topography/sloping characteristics ofthis area which is mostly undulating and steep in nature couldhave as well reduced the infiltrating rate of pollutants orcontaminants in the subsurface through the high rate of run-off in the area. These hydrogeological implications alsosupported the prediction accuracy of the adopted method inthe study area.

Conclusion and future works

This study developed an OWA-based DRASTIC model forgroundwater vulnerability assessment in the southern part ofPerak, Malaysia through incorporation of user’s decisionstrategies. The OWA-based analysis and modeling ofDRASTIC model pollution potential factors gave us anexcellent insight into ranking criteria and addressinguncertainty from their interaction. The proposed model wasbased on the integration of a pair-wise comparison method(AHP methodology) and quantifier-guided OWA operatorsthat enable users to include a wide range of decision strategies,instead of simple weighted average, into the process ofsolving groundwater contamination problems. The analyzedresults of the model yielded six user’s decision strategiesalternatives for groundwater vulnerable zones prediction

associated with the DRASTIC model parameters. The risk-adverse model associated with OWA AND operator wasselected for GVPM in the area. Five vulnerable zones, namely,NV, VLV, LV, MV, and HV, were determined based on theestimated OWA index values. The results showed thatpredominant portions of the area belonged to NV zones. Theresults of the prediction accuracy of the GVPM verified withthe groundwater quality analyzed established a high degree ofcorrelation between the area percentages of the predictedvulnerable zones and the mean of the analyzed parameterconcentrations in the area. This research has demonstratedthe efficacy of GIS-based OWA-DRASTIC approach inevaluating groundwater pollution potential mapping in anarea. The research findings can offer hydrologists andengineers a sound guideline for establishing protection zonesand agricultural management strategies. However, a furtherimprovement of the accuracy of GIS-based OWA-DRASTICapproach can be achieved by integrating Monte Carlosimulation for sensitivity analysis of the weights derived fromAHP and assessing the uncertainty using Dempster–Shafertheory in future analysis.

Acknowledgment This project was carried out with financial supportfrom RUI, Investigation of the Impacts of Summertime MonsoonCirculation to the Aerosols Transportation and Distribution in SoutheastAsia Which Can Lead to Global Climate Change, 1001/PFIZIK/811228.

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Table 14 Physiochemical and heavy metals constituents in the sampled boreholes located in the HV zone and their comparisons with WHO and FAOstandards

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The pH is dimensionless; except otherwise stated, all units are in milligrams per liter

HV high vulnerable

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