Preparation of a geotechnical microzonation model using

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Preparation of a geotechnical microzonation model usingGeographical Information Systems based on

Multicriteria Decision Analysis

al Kolata,, Vedat Doyuran a, Can Ayday b, M. Ltfi Szen a

a Department of Geological Engineering, Middle East Technical University, Ankara 06531, Turkeyb Anadolu University, Research Institute of Satellite and Space Sciences, Eskiehir 26470, Turkey

Received 7 July 2005; received in revised form 8 July 2006; accepted 13 July 2006

Abstract

The purpose of this study is to develop a geotechnical microzonation model using Geographical Information Systems (GIS)based on Multicriteria Decision Analysis (MCDA). As study area, the Eskiehir downtown area has been chosen. Eskiehir is oneof the most rapidly growing cities in central Turkey. The model inputs include slope, flood susceptibility, soil, depth to groundwatertable, swelling potential, and liquefaction potential. The weight and rank values are assigned to the layers and to the classes of eachlayer respectively. The assignment of the weight/rank values and the analysis are realized by the application of two differentdecision models, namely Simple Additive Weighting (SAW) and Analytic Hierarchy Process (AHP) methods. The geotechnical

microzonation maps prepared as outputs of these methods are found to be consistent with each other and confirmed by the expertswithin the study area. The geotechnical microzonation map prepared using the AHP method is recommended as the final map ofthe study. 2006 Elsevier B.V. All rights reserved.

Keywords: Geotechnical microzonation model; Geographical Information Systems; Multicriteria Decision Analysis; Analytic Hierarchy Process;Eskiehir

1. Introduction

Rapidly growing cities with increasing populationunderline the requirement for new residential areas. En-gineering geological evaluations should be performed inorder to determine the most suitable residential areas.Preparation of the geotechnical microzonation maps

provides an effective solution for this requirement. Mostof these geotechnical site selection applications require

an enormous amount of data which must be geograph-ically related to each other. Conventionally, geo-environmental evaluation and mapping were laboriousand time-consuming tasks because of the large amount oftime and effort required for the manual handling andprocessing of the spatial data (Dai et al., 2001). Con-sequently, there exists a necessity of a system where allof these large quantities of data could be manipulatedwith ease. Geographical Information Systems (GIS),being a computer-based system that enables acquisition,storage, retrieval, modeling, manipulation and analysis

Engineering Geology xx (2006) xxxxxx

+ MODEL

ENGEO-02587; No of Pages 15

www.elsevier.com/locate/enggeo

Corresponding author. Tel.: +90 312 210 57 23; fax: +90 312 21057 50.

E-mail addresses: [email protected](. Kolat),[email protected](V. Doyuran), [email protected](C. Ayday),[email protected](M. Ltfi Szen).

0013-7952/$ - see front matter 2006 Elsevier B.V. All rights reserved.doi:10.1016/j.enggeo.2006.07.005

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Please cite this article as: al Kolat et al., Preparation of a geotechnical microzonation model using Geographical Information Systems basedon Multicriteria Decision Analysis, Engineering Geology (2006), doi:10.1016/j.enggeo.2006.07.005.
mailto:[email protected]:[email protected]:[email protected]:[email protected]://dx.doi.org/10.1016/j.enggeo.2006.07.005http://dx.doi.org/10.1016/j.enggeo.2006.07.005http://dx.doi.org/10.1016/j.enggeo.2006.07.005http://dx.doi.org/10.1016/j.enggeo.2006.07.005mailto:[email protected]:[email protected]:[email protected]:[email protected]


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of geographically related data (Aronoff, 1993; Worboys,1995), has provided a complimentary solution for thisrequirement.

Site selection decision problems involve a set ofgeographically defined alternatives, from which a choice

of one or more alternatives is to be made on the basis ofmultiple, conflicting and incommensurate evaluationcriteria. The alternatives are geographically defined in

the sense that results of the analysis (decisions) dependon their spatial arrangement. Accordingly, many real-world spatial planning and management problems giverise to GIS based multicriteria decision making or spatialMulticriteria Decision Analysis (MCDA) (Malczewski,

1999).GIS should be considered as a special-purpose digital

database in which a common spatial coordinate system

Fig. 1. Location map of the study area.

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is the primary means of storing and accessing data whileprocessing the data to obtain information for decisionmaking. The ultimate aim of GIS is to provide supportfor decision making (Densham, 1991). This can beachieved by integrating the MCDA and the analytical

capabilities of GIS (Diamond and Wright, 1988; Carver,1991; Eastman et al., 1995; Jankowski, 1995; Keller,1996; Malczewski, 1999).

The purpose of this s tudy is to prepare ageotechnical microzonation model using MCDA tech-niques with GIS support and to propose a conciseflowchart to be used in further similar studies. As studyarea, the sub-section of Eskiehir downtown area has

been chosen (Fig. 1). Eskiehir is one of the mostrapidly growing cities in the Central Anatolian regionof Turkey. The study area is bounded by thecoordinates 4408166 N and 283628 E in the north-western edge and 4403718 N and 288712 E in the

southeastern edge in Universal Transverse Mercator(UTM) projection (Zone 36 N, European Mean Datum1950). The study area covers approximately 22.5 km2

(5.1 km4.4 km). Since the study area is alreadydensely settled, the proposed microzonation model canalso be used to check the suitability of already settledareas and also to determine if further precautions areneeded for safer planning actions or modifications.

Fig. 2. Flowchart for the preparation of the microzonation map of the study area using GIS based MCDA.

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2. Methodology

The steps followed throughout the study are pre-sented in Fig. 2, where steps of implementing MCDA andGIS for the preparation of the geotechnical microzonation

model are emphasized. The data sets used in this study canbe grouped in three main data sources as topographical basemaps, lithological maps and geotechnical borehole data.

From these three data sources six different predictor mapswere produced. These were slope, flood susceptibility, soil,depth to groundwater table, swelling and liquefactionpotential layers. The next step was to assign weight andrank values to the layers and to the classes of each layer,

respectively. The assignment of the weight/rank values andthe analysis were realized by the application of twodifferent decision models, namely the Simple Additive

Fig. 3. a. The Digital Elevation Model (DEM) of the study area, b. Slope layer of the study area, c. Flood susceptibility layer of the study area.


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Weighting (SAW) and the Analytic Hierarchy Process(AHP) methods.

This flowchart can be applied for further similar studies,provided that the layers used for theanalysis are determinedaccording to the needs of the study area.

3. Data evaluation

3.1. Topographical map

1:25.000 and 1:5.000 scale topographical maps werefirst registered according to the Universal TransverseMercator (UTM) projection system (Zone: 36 andDatum: European 1950 Mean) and digitized. Thesedigitized contours were used to produce the DigitalElevation Model (DEM) of the study area.

The cell size of the DEM was determined by themethod proposed by Florinsky and Kuryakova (2000)where the adequate cell size for the DEM of the studyarea was determined as 10 m. Six different surface fittingmethods were applied and evaluated according to theiraccuracy in order to decide the most appropriate methodthat represents the natural environment best. The surfacefitting methods applied were; minimum curvature, in-verse distance, profiles, polynomial, triangulation andkriging. Inverse distance and polynomial methods were

discarded since they seem to produce significant errors inrepresenting the study area properly. The other fourmethods were evaluated according to their accuracy. Theaccuracy assessment for DEM was performed by thecalculation of Root-Mean-Square Error (RMSE). For

computing the RMSE, sample points were taken accord-ing to USGS mapping standards (USGS, 2004).

The true and interpolated topographic values of thesample points were examined, and compared bycalculating their RMSE; to be able to decide whichinterpolation method should have been used. During theevaluation, it was observed that when the study area isdivided into two sections (as flat area and gentle hills)and two different methods are applied for each respectivesection, the vertical accuracy of the produced DEMincreases. The RMSE values were calculated, compared

and evaluated separately for these two sections. As aresult, the profile surface fitting algorithm was used forthe gentle hills section and the minimum curvaturealgorithm for the flat area section. The integration ofthese two methods yielded an RMSE of 0.079 m in thewhole DEM (Fig. 3a), which is quite acceptable.

3.1.1. Slope layer

Slope is an important factor while considering theease of engineering construction and susceptibility to

Fig. 4. a. Lithology map of the study area (Ayday et al., 2001), b. Soil layer of the study area including the borehole locations.


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landsliding (Dai et al., 2001; Szen and Doyuran,2004a). There is no slope stability problem encounteredin the study area (Koyuncu, 2001). Therefore, the slopelayer will only contribute to the microzonation map in

the ease of engineering constructions, since steep slopesinterfere with excavation processes.

The slope map was prepared in degrees using the DEMof the study area. Afterwards, the slope values were

Fig. 5. a. Depth to groundwater table layer of the study area including the borehole locations, b. Swelling potential layer of the study area includingthe borehole locations, c. Liquefaction potential layer of the study area (modified from Ayday et al., 2001).


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subdivided into three main classes according to the

Guidelines for Urban Engineering Geological Investiga-tions (South African Institute of Engineering Geologists).Slopes between 2 and 6 were assigned as the mostfavorable class, slopes less than 2 and slopes between 6and 12 were assigned as the intermediate class, and slopesgreater than 12 were assigned as the least favorable class.The slope layer of the study area is given in Fig. 3b.

3.1.2. Flood susceptibility layer

The flood susceptibility of the study area wasexamined in case of thunderstorms of long duration.

The potential flood-prone areas (Fig. 3c) were definedas the areas having slope values less than 2 within thePorsuk Stream floodplain. It should be noted that in thepreparation of the flood susceptibility layer, it wasassumed that the Porsuk Dam will not fail, even in caseof an earthquake.

3.2. Lithology map and soil layer

The lithology map of the study area was taken fromAyday et al. (2001). For the study area, six lithologicalunits were identified (Fig. 4a). These units includeConglomerate-sandstone Member of Mamuca Formation

(Lower Eocene); Conglomerate-sandstone, TuffMarlClay and Limestone Members of Porsuk Formation(Upper Miocene); Old Alluvium (Quaternary) and YoungAlluvium (Quaternary). In the study area, Mamuca andPorsuk Formations were classified as bedrock.

The soil layer of the study area was based on thelithology map and on the report prepared by Ayday et al.(2001). The relevant data of 66 boreholes were reviewedand evaluated. The liquid limit and plasticity index datawere compiled for the depth range corresponding to theaverage foundation excavations and the data wereplotted into the plasticity chart in order to evaluate thesoil behavior. The units classified as bedrock were nottaken into consideration. The soil layer of the study areawas prepared according to the Unified Soil Classifica-tion System (USCS). Based on USCS, the main soil

classes include, SC (Clayey sands, sand

clay mixtures),MH / ML (Inorganic silts, micaceous or diatomaceousfine sands or silts, elastic silts/Inorganic silts, very finesands, rock flour, silty of clayey fine sands), CH(Inorganic clays of high plasticity, fat clays) and CL(Inorganic clays of low to medium plasticity, gravellyclays, sandy clays, silty clays, lean clays). The results

Table 2Standardized rank values and normalized weight values

Layers Weighting(normalized)

Classes Ranking(standardized)

Liquefactionpotential layer

0.2703 No liquefactionpotential

1

Moderateliquefactionpotential

0.4

High liquefactionpotential

0.2

Flood susceptibilitylayer

0.2162 Non-flood areas 1Flood-prone areas 0.2

Soil layer 0.1892 Bedrock 1SC 0.8MH/ML 0.6

CL 0.4CH 0.2

Depth togroundwatertable layer

0.1351 N10 m 1510 m 0.605 m 0.2

Swellingpotential layer

0.1081 Bedrock 1Low expansion 0.8Medium expansion 0.6

Slope layer 0.0811 Most favorableslope class

1

Intermediateslope class

0.8

Least favorable

slope class

0.2

Table 1Assigned weight and rank values for the layers/classes of the studyarea

Layers Weighting Classes Ranking

Liquefaction

potential layer

10 No liquefaction

potential

5

Moderate liquefactionpotential

2


1


8 Non-flood areas 5Flood-prone areas 1

Soil layer 7 Bedrock 5SC 4MH/ML 3CL 2CH 1

Depth to groundwater

table layer

5 N10 m 5

510 m 305 m 1

Swellingpotential layer

4 Bedrock 5Low expansion 4Medium expansion 3

Slope layer 3 Most favorableslope class

5


4

Least favorable slopeclass

1


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obtained were evaluated spatially and the boundaries ofthe soil classes were determined manually. The soil layerof the study area including the borehole locations isgiven in Fig. 4b.

3.3. Borehole data

The borehole data used in this study was obtainedfrom Ayday et al. (2001). The borehole data were ana-

lyzed and evaluated in order to obtain the depth togroundwater table, swelling potential and liquefactionpotential layers.

3.3.1. Depth to groundwater table layer

Groundwater is one of the main factors governing thestability of foundation excavations as well as the ease and/

or difficulty of the excavation works. In liquefaction as-sessment the position of the water table within non-

Fig. 6. The microzonation map prepared by using the SAW method and the histogram showing the boundaries of the three zones.

Fig. 7. Hierarchical structure used in the preparation of the microzonation map.


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cohesive sediments should also be known. The lithologicalunits exposed in the study area formed fair-to-poor aquifersdue to the abundance of MH/ML type soils. Both thebedrock and the alluvial deposits were hydraulically con-nectedand serve as a single aquifer, which was unconfined.

The depth to water table layer was prepared byconsidering the highest elevations of the static water levels.Thus, from the available geotechnical boreholes, six withinthe study area and sixlocated at close vicinity, groundwaterlevels measured during May were compiled and depth towater table layer was prepared. Areas underlain by shallow(05 m) groundwater table were considered as leastfavorable, between 510 m as favorable, and deeper than10 m as the most favorable (Fig. 5a).

3.3.2. Swelling potential layer

The swelling potential layer of the study area was

determined using 13 boreholes, for the first 2.5 m from thesurface, since below this depth, the soil moisture remainsconstant. After compiling the Clay Content (%) andPlasticity Index (%) from the borehole data within 02.5 m depth interval, these values were evaluated by usingthe activity chart, and were found to be within the low andmedium expansion classes. Therefore, the classes of theswelling potential layer of the study area were determinedas; low expansion and medium expansion (Fig. 5b).

3.3.3. Liquefaction potential layer

The liquefaction potential layer of the study area wastaken from Ayday et al. (2001), which was based on theSeed and De Alba (1986) approach. In the calculations,peak horizontal acceleration was taken as 0.4 g and thegrain size data for a depth of 5 m was used.

The liquefaction potential map was reclassified toobtain the liquefaction potential layer of the study area.The classes ofbedrock and groundwater level deeperthan10mwere assigned to the class ofno liquefaction.The liquefaction potential layer of the study area includeshigh liquefaction potential (Factor of Safety (FS)b1.0),moderate liquefaction potential (1.0bFSb1.2) and noliquefaction potential (FSN1.2) (Fig. 5c).

4. Analysis

4.1. Common steps of Multicriteria Decision Analysis

applications

In order to obtain geotechnical microzonation model ofthe study area two methods were applied: Simple AdditiveWeighting(SAW) and Analytic Hierarchy Process (AHP).

In the analysis, weight values to the layers and rankvalues to the classes of each layer were assigned. Foreach class of the layer, rankings were given according totheir significance in foundation performance. After therankings were assigned to the classes of each layer, theweights were assigned to layers according to theirimportance. The interaction between the layers was nottaken into consideration since the layers were assumed

to be independent of each other.The weight and rank values of the layers and classes ofeach layer were standardized in order to obtain a commondimensionless unit. Afterwards, the output (microzona-tion map) was created by multiplying the weight valueassigned to each layer by the rank value given to theclasses of that layer and finally by adding up the products.As a result, the microzonation map of Eskiehirdowntown area regarding the foundation suitability ofresidential areas was grouped into different zones basedon the recommended subdivisions by The GeneralDirectorate of Disaster Affairs of Turkey (GDDA, 2000).

4.2. Simple Additive Weighting (SAW)

In the SAW method, the weight and rank values aregiven totally based on expert opinion. In this method, allof the layers are concurrently considered in assigningweight values, and all classes of each layer are alsoconcurrently considered while assigning rank values. Asa result, six weight values were assigned to the six layers.

4.2.1. Assigning weight and rank values

In the assigning of weight and rank values, inverseweighting and ranking criteria was used. For weight

Table 3Comparison judgments from a fundamental scale of absolute numbersfor assigning weight/rank values (Saaty, 2004)

Weight/Rank Intensities

1 Equal3 Moderately dominant 5 Strongly dominant 7 Very strongly dominant 9 Extremely dominant 2, 4, 6, 8 Intermediate valuesReciprocals For inverse judgments

Table 4The pairwise comparison table for assigning weight values

Liquefaction Swelling Soil Depthto gwt

Slope Flood

Liquefaction 1 5 3 4 6 1Swelling 1/5 1 1/3 1/2 1 1/4Soil 1/3 3 1 2 4 1Depth to gwt 1/4 2 1/2 1 3 1/3Slope 1/6 1 1/4 1/3 1 1/5

Flood 1 4 1 3 5 1


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values, the assignment start from the least importantwith the value of 1, the next least important is assignedthe value 2, and the most important layer gets the valueof 10. Similarly the rank values were quantified as theleast important class value being 1 and the most im-portant class value being 5.

The assigned weight and rank values for the layers/classes of the study area based on engineering judgmentare given in Table 1. As can be observed from the table,the most important layer was defined as the liquefactionpotential layer, followed by the flood susceptibility, soil,

depth to groundwater table, swelling potential and slopelayers in decreasing order of importance. In thedetermination of the weight values, the liquefactionlayer had the greatest value since the study area islocated in the second degree earthquake zone (GDDA,1996). The flood layer has taken the second importantrole since there is a large floodplain in the study area andfor the case of thunderstorms of long duration, there is apossible risk of flood (In 1963, the overflow of PorsukStream resulted in serious flood hazard). Soil layer wasalso important since the behavior of the soil under staticand dynamic loading conditions should be taken intoconsideration during construction. In contributing to the

microzonation map, the depth to groundwater table,swelling potential and slope layers have carriedrelatively low importance, since the possible problemscan be handled relatively more easily and practically.The weight assigning order of these three layers wasgiven according to the ease of the precautions needed, as(in descending order): depth to groundwater table,swelling potential and slope.

4.2.2. Standardization of rank and weight values

The simplest formula for standardizing the raw data

is to divide each raw score by the maximum value for agiven criterion (Malczewski, 1999). Hence, the rankvalues of the classes were standardized according to therelative distance between the origin and the maximumrank value, using the following formula:

XVij Xij=Xmax

j

where X'ij is the standardized rank value for the ith classfor the jth layer. Xij is the raw rank value, and Xj

max isthe maximum rank value for the jth layer.

On the other hand, the weight values were normal-ized by dividing each weight by the sum of the weights.

Table 5The pairwise comparison tables for assigning rank values to the classes of each layer

Liquefaction potential layer No liquefactionpotential


Highliquefactionpotential

No liquefaction potential 1 7 9

Moderate liquefaction potential 1/7 1 2High liquefaction potential 1/9 1/2 1

Flood susceptibility layer Non-flood areas Flood-prone areasNon-flood areas 1 1/5Flood-prone areas 5 1

Soil layer Bedrock SC MH/ML CL CHBedrock 1 3 5 6 7SC 1/3 1 3 5 6MH/ML 1/5 1/3 1 3 4CL 1/6 1/5 1/3 1 3CH 1/7 1/6 1/4 1/3 1

Depth to groundwater table layer N10 m 510 m 05 mN10 m 1 2 7

5

10 m 1/2 1 405 m 1/7 1/4 1Swelling potential layer Bedrock Low expansion Medium

expansionBedrock 1 3 5Low expansion 1/3 1 3Medium expansion 1/5 1/3 1

Slope layer Most favorableslope class


Leastfavorableslope class

Most favorable slope class 1 3 6Intermediate slope class 1/3 1 5Least favorable slope class 1/6 1/5 1


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Thus the sum of the normalized weight values was equalto 1. The standardized rank values and the normalizedweight values are given in Table 2.

4.2.3. Result map of SAW

For the preparation of the microzonation map of thestudy area, the overlay operations of the layers were used.The formula proposed by Malczewski (1999) forobtaining the total scores was applied in this study.

Accordingly, each pixel of the output microzonation map(Mi) was calculated by using the following summation:

Mi Rjwjxij

where, xij=rank value of the ith class with respect tothe jth layer

wj=normalized weight of the jth layer.Thus the normalized weight value assigned for each

layer was multiplied by the standardized rank valuegiven to the classes of that layer. Finally the sum of theproducts was calculated.

The microzonation map of the study area wascategorized in three resultant classes as: Areas Suitablefor Settlement (SA), Provisional Settlement Areas(PSA) and Detailed Geotechnical Investigation Re-quired Areas (DGA) (Fig. 6). The boundary conditionsfor the categories were evaluated according to the expertjudgment taking into consideration the score distribu-tions by means of discrete histograms (Fig. 6).

4.3. Analytic Hierarchy Process (AHP)

In the AHP method, pairwise comparisons form

the backbone of the methodology. AHP provides uswith a way to derive from an observer's quantifiedjudgments (i.e., from the relative values associated

Table 6The computed weight and rank values

Layers Weighting Classes Ranking

Liquefactionpotential layer

0.350722 No liquefactionpotential

0.792757


0.131221


0.076022


0.256510 Non-flood areas 0.833333Flood-prone areas 0.166667

Soil layer 0.184303 Bedrock 0.496900SC 0.264927MH/ML 0.129109CL 0.069362CH 0.039702

Depth to groundwatertable layer

0.102535 N10 m 0.602629510 m 0.315029

05 m 0.082342Swelling

potential layer0.057615 Bedrock 0.636986

Low Expansion 0.258285Medium expansion 0.104729

Slope layer 0.048315 Most favorableslope class

0.634838


0.287203

Least favorableslope class

0.077959

Fig. 8. The microzonation map prepared by using the AHP method and the histogram showing the boundaries of the three zones.


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with pairs of stimuli), a set of relative weights orpriorities associated with individual stimuli (zdemir,2005). Hence, in assigning the weights of the layers,only two layers were considered at a time. In total 15different weights were given, since in assigning each

weight, only two layers were considered at a time. Inother words, the total number of pairings where everylayer was matched with all others is 15. Similarly, therankings of the classes of each layer were alsoassigned considering only two classes at a time.

The hierarchical structure used in the preparation ofthe microzonation map is given in Fig. 7. In assigningweights, pairwise comparisons of each layer with otherlayers were assessed, while in assigning rank values,pairwise comparisons of each class with other classesof the same layer were assessed. For the assignment of

weight and rank values, the comparison judgmentsscale from Saaty (2006) was used, as given in Table 3with their corresponding meanings.

4.3.1. Assigning weight values

The pairwise comparison matrix for assigningthe weight values is given in Table 4. The logicbehind assigning values in the pairwise comparisonmatrix can be explained through the liquefaction layeras follows:

The liquefaction layer has equal importance with theflood layer and moderate prevalence to soil layer,

whereas it has moderatestrong, strong, and strongvery strong prevalence against depth to groundwatertable, swelling and slope layers, respectively.

4.3.2. Assigning rank values

The AHP method can be used not only to assessweights but also to assess the performance ofalternatives by pairwise comparison of the alternatives(Janssen, 1992). In order to assign the rank values tothe classes of each layer, the pairwise comparisonmatrix was prepared separately for the layers, using the

comparison judgments presented in Table 3. Theimplementation of pairwise comparison tables forassigning rank values to the classes of each layer isgiven in Table 5.

4.3.3. Obtaining overall weight and rank valuesIn order to obtain the overall weight/rank values, the

eigenvector solution was used. This approach had beendemonstrated mathematically by Saaty (1990). Thesolution for the eigenvector can be explained in thefollowing steps:

i. A short computational way to obtain the weight/rank values is to raise the pairwise matrix to pow-ers that are successively squared each time.

ii. The row sums are then calculated and normalized.

iii. The iteration is instructed to stop when thedifference between these sums in two consecutivecalculations is smaller than a prescribed value.

Applying the above method, the overall weightvalues of the layers and the overall rank values of theclasses of each layer were obtained. The pairwisecomparison matrix was squared; row sums werecalculated and normalized, the matrix obtained wassquared; row sums were calculated and normalizedagain. This process was repeated until the differencebetween the normalized values fell below the

threshold of computing capabilities. When the differ-ence value appeared to be zero, then the normalizedvalues were taken as weight/rank values for theclasses/layers. The computed weight and rank valuesare given in Table 6.

4.3.4. Consistency ratio

The consistency ratio was calculated in order todetermine whether the pairwise comparisons wereconsistent or not. The consistency ratio (CR) is designedin such a way that if CRb0.10, the ratio indicates a

reasonable level of consistency in the pairwise compar-isons; if CR0.10, the values of the ratio are indicativeof inconsistent judgments (Malczewski, 1999). Theconsistency ratios for all of the pairwise comparisonsused in order to obtain the microzonation map werecalculated and found to be consistent.

4.3.5. Result map of AHP

The geotechnical microzonation map of the studyarea using results of AHP method was prepared in amanner consistent with that of the SAW method.Thus, the normalized weight value assigned for eachlayer was multiplied by the standardized rank value

Table 7Possible result numbers of the addition process with the correspondingpercentages


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given to the classes of that layer. Finally the sum ofthe products was calculated.

As in the SAW result, the AHP microzonation mapof the study area was categorized again in three resul-tant classes as: Areas Suitable for Settlement (SA),

Provisional Settlement Areas (PSA) and Detailed Geo-technical Investigation Required Areas (DGA) (Fig. 8).The boundary conditions for the categories wereevaluated according to the expert judgment takinginto account the score distributions by means ofdiscrete histograms (Fig. 8).

4.4. Comparison of microzonation maps

In order to compare the microzonation maps whichwere prepared using SAW and AHP methods, the

common areas and noncommon areas were highlighted.During this process, firstly the classes of a categoricalmap were transferred from the ordinal scale to theinterval scale (Szen, 2002). Numbers were assigned tothe classes of both microzonation maps, in such a waythat, the sum of these values from the two microzona-tion maps must be unique and represent differentconditions (Szen and Doyuran, 2004b). In this study,two microzonation maps were added to find out thecommon and noncommon areas. The possible results ofthe addition process are shown in Table 7 with thecorresponding percentages obtained from the addition

process.As a result of the comparison analysis of the study

area, the two microzonation maps were found to givesimilar outputs. In the comparison output map; only theclasses of11, 22, 33, and 32 were found. Theabsence of classes 13 and 31 shows that thereexists no conflict between the microzonation maps.Furthermore, classes 11, 22, and 33 stand forexactly same classes both in SAW and in AHP to DGA,PSA and SA, respectively. Unfortunately class 32means that the PSA class found in the microzonation

map prepared with SAW method is found as SA classin the microzonation map prepared with AHP method.As a result, the ratio of the correct classificationcategory (classes 11, 22 and 33) in thecomparison of two microzonation maps was found tobe 98.39% of the total study area.

5. Discussion

Multiple objectives are essential to many real sys-tems. Frequently, these multiple objectives conflict witheach other (as one objective is improved, the others maydeteriorate). Dimensional analysis can help the decision

maker to make better decisions under such circum-stances (Starr and Stein, 1976).

In decision making context, a criterion would implysome sort of standard by which one particular choice orcourse of action could be judged to be more desirable

than another. Actually in real life, every decision re-quires the balancing of multiple factors so that in somesense, everyone is well practiced in multicriteria de-cision making. However, the human brain can onlysimultaneously consider a limited amount of informa-tion, so that all factors cannot be resolved in one's head(Belton and Stewart, 2002).

Usage of GIS based MCDA is essential in thepreparation of geotechnical microzonation maps due tothe need for using a large amount of spatial data andintegrating the geographical data with the decision

maker's preferences.Two considerations are of critical importance forspatial Multicriteria Decision Analysis: (i) the GIS capa-bilities of data acquisition, storage, retrieval, manipula-tion and analysis, and (ii) the MCDA capabilities foraggregating the geographical data and the decisionmaker's preferences into uni-dimensional values ofalternative decisions (Carver, 1991; Jankowski, 1995).Accordingly, the possible sources of errors in our studycan be categorized as data related errors and errorsresulting from the decision maker's preferences:

Considering the data related errors; the original

geological, hydrogeological, and geotechnical dataseem to be quite satisfactory for this study. However,the number of boreholes (especially for the groundwaterlevel observations) could be increased to obtain moreinformation for better geotechnical characterization ofthe study area. For the preparation of the data layers, thecontinuous surfaces are formed from the interpolation ofthis raw point/line data. During this interpolationprocess, some errors may have occurred due to lack ofinformation between the consecutive points/lines. Wetried to minimize these errors in the preparation of DEM

of the study area by applying several surface fittingmethods and choosing the method with least RMSEvalue. For the maps prepared using the borehole data, thepoint data is interpolated manually as accurate aspossible.

In addition to the data related errors, there is un-certainty involved in the specification of decision makerpreferences. In fact, the criterion map errors and de-cision maker preference errors are interrelated. Theinformation derived from criterion maps is an essentialelement for specifying the decision maker's preferences.For reliable results, the decision maker is expected to bean expert to make preferences since the importance of


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each criterion can be overestimated or underestimatedaccording to these preferences.

The subjectivity of the preferences comes mainlyfrom the assignment of weight and rank values. In thescope of this study, the weight and rank values which are

used both in the SAWand the AHP methods, are assignedproperly according to the engineering judgment.

The basic differences between the SAW and the AHPmethods lie in their objectiveness, easiness and evaluationopportunities. Although AHP is more complicated thanSAW, AHP has more objective results. The basic strategyis to divide the decision problem into small, understand-able and manageable parts; analyze each part; andintegrate the parts in a logical manner to produce ameaningful solution (Malczewski, 1999). In the AHPmethod, this strategy is applied in assigning rank and

weight values since only two layers/classes are consideredand compared at a time. This decreases the subjectivity ofthe study and brings an advantage to AHP method.

Besides, the pairwise comparison for the determina-tion of weights is more suitable than direct assignmentof the weights, because one can check the consistency ofthe weights by calculating the consistency ratio inpairwise comparison; however, in direct assignment ofweights, the weights are depending on the preference ofdecision maker (ener et al., 2006).

On the other hand, the SAW method definitely has anadvantage in rapidity. In applying this method, the result

can be realized quickly with the contribution of aqualified expert. However, since SAW method usesdirect assignment of the weights/ranks, the qualificationof the expert needed is much more than needed in AHP.

Furthermore, the AHP method provides the user to beable to evaluate the situation in different aspects. Inmany decision problems, four kinds of concerns areconsidered: benefits, opportunities, costs, and risks(BOCR); in which the first two are advantageous, andhence, are positive and the second two are disadvanta-geous and are therefore negative (Saaty and zdemir,

2003). They have shown that the negative priorities canalso be defined as relative numbers and used along withpositive priorities in AHP. Therefore, another importantadvantage of the AHP method is to lead the expert to beable to evaluate the BOCR of the problem separately,which is not possible when applying the SAW method.

The microzonation maps obtained by using the SAWand the AHP methods are found to be consistent witheach other. The reason for this consistency lies in theproper assignments of weight and rank values by theexpert. The microzonation map prepared using the AHPmethod is chosen to be the final map of this study due tothe clear advantages of this method.

6. Conclusion

This study demonstrates the superiority of the usage ofMCDA techniques with GIS for the preparation of thegeotechnical microzonation map regarding the suitability

of the residential areas.The important advantages of usingthese techniques can be summarized as having relativelylow cost, easy data manipulation, rapidly updating of dataand the possibility to produce various new scenarios.

In this study, the slope, flood susceptibility, soil,depth to groundwater table, swelling potential andliquefaction potential layers were prepared for thechosen study area in Eskiehir. The assignment of theweight and rank values and the analysis were performedby application of the SAW and the AHP methods. As aresult, the study area was categorized into three different

zones regarding the foundation suitability of residentialareas as: (1) Areas Suitable for Settlement; (2)Provisional Settlement Areas; (3) Areas requiringdetailed geotechnical investigations. The maps preparedusing the SAW and the AHP methods were found to beconsistent with each other. The geotechnical micro-zonation map prepared using the AHP method isrecommended as the final map of the study area.

According to this final map; the majority of the studyarea is found to be in the Areas Suitable for Settlement(SA) and Provisional Settlement Areas (PSA) zones.The minority part of the study area is found to be in

DGA, in which detailed geotechnical investigations arerequired. For the already settled areas which are notfound in the first zone (SA), the necessity of theprecautions must be considered. The methodology usedfor the chosen area in this study can also be applied to theother locations and to other site selection procedures byreconstructing the necessary parameters appropriately.

Acknowledgments

This study was supported by METU Research

Foundation Grant No: BAP-2004-07-02-00-57.

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