20120140507001

International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –

6480(Print), ISSN 0976 – 6499(Online) Volume 5, Issue 7, July (2014), pp. 01-18 © IAEME

1

MULTICRITERIA DECISION ANALYSIS BASED ON ANALYTIC

HEIRARCHY PROCESS IN GIS ENVIRONMENT FOR SITING

NUCLEAR POWER PLANT IN EGYPT

Abudeif, A. M.,[1]

, Abdel Monem, A. A.[1]

, Farrag, A. F.[2]

[1]

Geology Dep., Faculty of Science, Sohag University, Sohag. Egypt

[2]Nuclear Power Plants Authority, Siting Department, Cairo. Egypt

ABSTRACT

Due to increasing demand of electrical energy and freshwater in Egypt, it is safe to assume

that the decision makers will turn to nuclear power as the feasible alternative for energy. However, as

time goes by, fewer sites are available and suitable for nuclear power plant development. Site

selection are key parts of the installation process of a nuclear plant and may significantly affect the

safety and cost of the facility during its entire life cycle. The siting of nuclear power plants is one of

multi-criteria problem, which makes it complex. Many interrelated factors affect the process. Six

Constraints and twenty two factors corresponding to safety, environment and socio-economy were

considered in siting process.

Multi-Criteria Decision Analysis was applied for site selection of nuclear power plants in GIS

environment for solving problems. Three spatial decision making models was applied in this paper

during site selection stage. The binary overlay (Boolean logic) with Low Risk approach in which the

logical OR operator is used to determine the candidate areas. All constraints were represented in

binary maps, combined and a masking layer was created to eliminate the lands considered as

constraints in Arc GIS Software.

The 22nd

factors represented in normalized maps after unifying all of them to 0 – 1 score

scales based on the philosophy of suitability criteria (factors) using Weighted Linear Combination

(WLC) method. The relative scores and weight of factors which used in the maps conducted by

pairwise comparison were fed into Expert choice software that runs the Analytic Hierarchy Process.

Final composite map of potential site priorities were represented by several polygons produced in

MCDA add-in as an open source tool in Arc GIS 10.1.

Four sites, all located on the coast of North western coast and Red Sea, were highest scores

and chosen as Candidate sites after eliminated the lowest score sites. The Analytic Hierarchy Process

(AHP) was applied to select a suitable site by pairwise comparison and calculating the eigenvectors

INTERNATIONAL JOURNAL OF ADVANCED RESEARCH

IN ENGINEERING AND TECHNOLOGY (IJARET)

ISSN 0976 - 6480 (Print)

ISSN 0976 - 6499 (Online)

Volume 5, Issue 7, July (2014), pp. 01-18

© IAEME: http://www.iaeme.com/IJARET.asp

Journal Impact Factor (2014): 7.8273 (Calculated by GISI)

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IJARET

© I A E M E



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in Expert Choice Software package. The sites were ranked to determine which was most desirable.

El Dabaa Site was found to be most suitable, followed by East El Negila site on Mediterranean Sea.

Keyword: Site Selection, Nuclear Power Plant Siting, MCDA, GIS, AHP & WLC, El Dabaa, Egypt.

1. INTRODUCTION

Siting is the process of selecting a suitable site for a facility, including appropriate assessment

and definition of the related design bases while site is the area containing the plant, defined by a

boundary and under effective control of the plant management [1].

The site selection process is intended to identify preferred candidate sites with characteristics

that are optimal in terms of the safe and economic construction and operation of a nuclear power

plant while minimizing the adverse impact to the environment and social fabric of the surrounding

region. The siting process involves the systematic screening and comparative assessment methods

that take into account the rejection and acceptance criteria contained in the Egyptian regulatory

guidance [2] as well as the safety criteria contained in [3], [1], [4], and [5]. Fig. (1) show general

siting process:

Fig. (1): Schematic diagrame showing siting process stages [1]

2. OBJECTIVE

The objective of this study is giving recommendations for preferred candidate site(s) that would

have a high likelihood for successful licensing and therefore, that would be the most suitable site for

detailed specific investigations necessary to finally select nuclear power plant site(s). The study was

conducted on the basis of existing information and field reconnaissance.

3. DATA GATHERING AND COLLECTIONS

Background data available from either previous studies or public sources was used. This data

included; (topographic, demographic, geologic and seismotectonic maps; meteorological data;

tourism; national parks and land use) [6], [7], [8] and others.



3

4. SITING METHODOLOGY

Three phases of siting process as general were described in safety guide of [1]. Phase I;

regional analysis, phase II; screening and comparison of potential sites and Phase III; screening,

comparison and ranking of candidate sites. The main objective of this paper is to delineate the

preferred candidate site(s) for developing a nuclear power plant. For achievement this target the

authors used three approaches of Multi Criteria Decision Analysis theory (MCDA) in Geographic

Information System (GIS) environment, these are:

a) Binary overly (Boolean logic) method for detecting candidate siting area using six

Constraints.

b) Weighted Linear Combination (WLC) method for selecting potential sites (alternatives)

using twenty two factors.

c) Analytic Hierarchy Process method (AHP) for evaluating the candidate sites to detect the

preferred candidate site.

Many papers focused on site selection using decision analysis as [9]; [10]; [11]; [12] and [13]

using Saaty's approach (AHP) for selecting a suitable site for building a nuclear power plant in

Kingdome Saudi Arabia from 4 candidate sites on the Red Sea coast. Mainly twenty one attributes

were used in the ranking process. [14] was selected three sites, from five nominated, to a nuclear

waste repository in USA by Analytic Hierarchy Process Method. He used 14th

attributes

corresponded to safety, environment and socioeconomic in his study. [15] used GIS technique to

compare between different spatial decisions making models performance for siting a nuclear power

plant in Kingdom of Saudi Arabia. In the following discussion for the multi-criteria decision models

used in this paper from the review of the available literature.

4.1. Multi-Criteria Decision Analysis and GIS It is generally assumed that multi-criteria decision analysis (MCDA) originated at the

beginning of 1960s. GIS-based multi-criteria decision analysis can be thought as a process that

combines and transforms spatial data into a resultant decision. The Multi Criteria Decision Making

(MCDM) procedures are decision rules which define a relationship between the input and output

maps. The procedures use geographic data, the decision maker’s preferences, data manipulation, and

preferences according to decision rules. Two considerations of critical importance for spatial MCDA

are the GIS capabilities of data acquisition, storage, retrieval, manipulation and analysis, and the

MCDM ability to combine the geographic data and the decision maker’s preferences into one

dimensional value of alternative decisions [10].

The MCDM approaches can be implemented in both raster and vector GIS environments.

Some GIS software, e.g. IDRISI & ILWIS [16] have built-in routines for the WLC and OWA

methods, and there are available an open source modules or scripts, e.g. for Arc GIS [17], to perform

that kind of MCDA of this sort. There are many ways in which decision criteria can be combined in

MCDA, the following ways was used in this paper:

4.1.1. Binary Overlay Methods (Boolean logic) The most famous effort in this direction although not the first one, was conducted by [18], he

used a manual technique of overlaying transparencies each representing certain criterion in which

data is divided into good and bad areas; the good areas are left transparent, while the bad ones are

blackened. At end, photocopying the overlaid transparencies to generate a final map where clear

areas yield the best sites.



4

With fast development in GIS techniques, the same model is adopted in early grid analysis

systems using an AND function to combine the binary scores of different criteria, hence eliminating

any cells that didn't score 'good' in all criteria. Later efforts used an OR function to allow for

minimum risk approach where all cells that scored 'good' at least one criterion are selected as

suitable. Below is an illustration to the product of combining of the value of cells from three layers;

the first option is the High Risk approach in which the logical AND operator is used to determine the

outcome of the combination. The result is no matter how good the site has done in all layers, it takes

only one 'bad' value to eliminate the site. The second option is the Low Risk approach, in which a

logical OR operator is used, and in that application, it is enough for a candidate site to score only one

'good' in any of the criteria to included in the selected sites.

4.1.2. Weighted Linear Combination (WLC) Weighted linear combination (WLC) is based on the concept of a weighted average in which

continuous criteria are standardized to a common numeric range, and then combined by means of a

weighted average. The decision maker assigns the weights of relative importance directly to each

attribute map layer. The total score for each alternative is obtained by multiplying the importance

weight assigned to each attribute by the scaled value given for that attribute to the alternative and

then summing the products over all attributes. The scores are calculated for all of the alternatives and

that with the highest overall score is chosen. The method can be executed using any GIS system with

overlay capabilities, and allows the evaluation criterion map layers to be combined in order to

determine the composite map layer which is output [4].

With the weighted linear combination, factors are combined by applying a weight to each

followed by a summation of the results to yield a suitability map:

Where S is suitability, wi is weight of factor i and xi is the criterion score of factor i. In cases,

where Boolean constraints also apply, the procedure can be modified by multiplying the suitability

calculated from the factors by the product of the constraints:

Where Cj is the criterion score of the constraint j.

4.1.2.1. Standardization of Criterion Scores Because criteria are measured on different scales, it is necessary that factors be standardized

before combination, and that they are transformed, if necessary, so that all factor maps are positively

correlated with suitability.

If the continuous factors are really fuzzy sets, this is easily recognizable as just one of many

possible set membership functions.[19] suggested the standardization of factors using a range of

fuzzy set membership functions to either a 0-1 real number scale or a 0-255 byte scale. Importantly,

the higher value of the standardized scale must represent the case of being more likely to belong to

the decision set. Besides this deterministic (linear scaling) and fuzzy approach, there are other

processes for standardizing evaluation criteria, such as the value/utility function approach, and the

)1....(..............................1

i

n

i

i xwS ∑=

=

)2........(....................1

ji

n

i

i CxwS ∏∑=

=



5

4.......................................................maxωλ=Aw

probability approach [20]. In this study the criterion scores is derived by pairwise comparison (AHP)

and standardized 0-1.

4.1.2.2. Evaluation of Criterion Weights A weight can be defined as a value assigned to an evaluation criterion indicative of its

importance relative to other criteria under consideration. The larger the weight, the more important is

the criterion in the overall utility [20].

A variety of techniques exist for the development of weights. In very simple cases,

assignment of criteria weights may be accomplished by dividing 1.0 among the criteria. When more

than a few criteria are involved and many considerations apply, it becomes difficult to make weight

evaluations on the set as a whole. The weights are then usually normalized so that they sum to 1. In

the case of n criteria, a set of weights is defined as follows:

w = (w1, w2, w3, …..wn) and 1=∑ jw

There are four main techniques for the development of weights such as Ranking method,

Rating method, Pairwise comparison method and Trade-off analysis method.

In this study the criterion weights is evaluated by pairwise comparison (AHP) method.

4.1.3. Analytic Hierarchy Process (AHP) Method

The Analytic Hierarchy Process (AHP) was introduced by [21] and [22]. To apply the AHP

method in spatial decision making we need the following steps.

A) Calculating the Criteria Scores Each alternative are compared pairwise with respect to a specific criterion to obtain the scores

(w1,…wn) of alternatives. The eigenvectors is obtained after normalizing the judgmental matrices.

B) Calculating the Criteria Weights [22] has used the lambda max technique to obtain the weights of the criteria in the pair-wise

comparison method. Each alternative are compared pair-wise with respect to the each criteria to

obtain the weights (Wi…. Wn). Every matrix has a set of eigenvalues and for every eigenvalue there

is a corresponding eigenvector. In Saaty’s lambda max technique, a vector of weights is defined as

the normalized eigenvector corresponding to the largest eigenvalue λmax.

C) Local Priorities and Consistency of Comparisons The judgmental matrix of comparisons of criteria with respect to the goal is available, the

local priorities of criteria are obtained and the consistency of the judgments is determined. The scale

of the pairwise comparison was introduced by Saaty (table 1). It has been generally agreed that

priorities of criteria can be estimated by finding the principal eigenvector w of the matrix A. That is:

When the vector ω is normalized, it becomes the vector of priorities of the criteria with

respect to the goal. λmax is the largest eigenvalue of the matrix A and the corresponding eigenvector

w contains only positive entries. The consistency of the judgmental matrix can be determined by a

measure called the consistency ratio CR defined as:



6

6.....................................1

max

−

−=

n

nCI

λ

5.........................................RI

CICR =

Where CI is called the consistency index and RI, the Random Index. CI is defined as:

If CR of the matrix is higher, it means that the input judgments are not consistent, and hence

are not reliable. In general, a consistency ratio of 0.10 or less is considered acceptable. If the value is

higher, the judgments may not be reliable and have to be elicited again.

Table (1): The fundamental scale of absolute numbers

Intensity of

importance Definition Explanation

1 Equal Importance Tow activities contribute equally to the

objective

3

Moderate Importance

Experience and judgment slightly favor one

activity over an other activity

5 Strong Importance Experience and judgment strongly favor one

activity over an other activity

7 Very Strong Importance An activity is favored very strongly over an

other activity

9 Extreme importance

The evidence favoring one activity over

another is of the highest possible order of

affirmation

2, 4, 6, 8 Intermediate values between the

two adjacent judgments When compromise is needed

Reciprocals

of above

If activity ί has one of the above

non zero numbers assigned to it

when compared with activity j,

when j has the reciprocal value

when compared with ί.

A reasonable assumption



7

D) Determination of Suitable Alternative The normalized eigenvectors which obtained from pairwise comparison are aggregated by

using the formula:

Where: wi= Relative weight of attribute (criteria) ί, 0 ≤ wi ≥ 1, Wij= Relative weight of

alternative (site) j with respect to criteria ί, 0 ≤ Wij ≥ 1, Zj= Overall weight of alternative (site) j 0 ≤ Zj

≥ 1, n= number of criterion ί

5. SITING CRITERIA AND ITS TYPES

An extensive hierarchy of issues and considerations pertaining to nuclear power plant siting

was developed. The issues concerned safety, environmental, economic and social considerations.

Criteria defining a required level of achievement on each consideration were established to identify

areas for further evaluation. Another point to keep in mind is that screening criteria may change with

time; they depend on social, political, technological, and financial conditions. Further siting efforts

may need to use different and/or additional criteria as conditions change [9].

Two types of criteria were used in site selection process; these criteria are defined based on

the severity of constraints imposed by underlying regulatory requirements. Those criteria are:

5.1. Constraints (Rejection Criteria) 1. Potential for fault displacement at or near the site.

2. Areas close to major population centers with population more than 100000.

3. Areas that don’t available large amount of cooling water.

4. Areas containing extensive and important drinking water resources.

5. Sites where feasibility of emergency plans cannot be apply.

6. Areas that contains sensitive and important habitat.

5.2. Factors (Suitability Criteria) Ten criteria (subdivided into twenty two sub criteria) were defined as factors that have the

greatest effect on the selection process, grouped in three main categories.

A. Safety and Health Criteria

This group is related to the natural elements of the site. This category includes ten criteria:

1. Geology Three sub criteria must be considered:

• Surface Geology; areas that have potential for fault displacement at or near the site are

rejected. Preference should be given to sites located at a sufficient safety distance from

capable faults (at least the site and site vicinity must show the absence of capable faults)

[1]and [3].

• Suitability of Subsurface Soil; areas with soil that have potentially undesirable response to

seismic should be avoided. Areas that may contain soils having a potential for liquefaction or

subsidence, thick layers of soft soil, anomalous soil conditions, a high ground water table,

subsurface cavities, are to be avoided [1] and [3].

)7.......(........................................1

ij

n

i

ij WwZ ∑=

=



8

• Hydrogeology; the area that have an aquitard or aquiclude are considered to be more

favorable than areas that have a high productive aquifers.

2. Seismicity Low geologic and seismic risk is an acceptance criterion. Areas affected in the past by

earthquake intensities higher than a given value which has been chosen on the basis of technical

judgment should be rejected [1].

3. Oceanography

Oceanography includes the following sub-attribute:

• Flooding; in the regional analysis, areas subjected to high flood levels are rejected. Potential

sites can be screened on the basis of severity of effects of flooding. Candidate sites that are

less affected are usually preferred [1].

• Depth of the Sea; the greater depths near the site; the better site is, since it is easier to draw

the cooling water from a depth well below the free surface where the water discharge is

carried out [13].

• Turbidity; the turbidity of the water is a disadvantage for a proper functioning of condenser.

High sedimentation rates pose difficulty for marine installations, especially when the coast is

sandy or in those areas with muddy bottoms [13].

• Water Temperature; the lower cooling water temperatures are preferable because of the larger

temperature gradient allowing more efficient heat transfer [13].

4. Meteorological Phenomena Low risk of extreme weather events and suitable pollution dispersion conditions are

acceptance criteria. Two sub-attributes belong:

• Wind Direction; the atmospheric discharges are the most important, so placement of the

power station in relation to the dominant winds and agricultural or urban areas must be

considered.

• Rainfall; the area that have low rainfall rate is preferred than that who have a high rainfall

rate.

B. Environmental and Social Criteria This group of criteria involves the adverse impacts of plant on the ecologic systems. It includes:

5. Ecology

These criteria deal with the effect of the nuclear power plant on the environment. They includes:

• Terrestrial Environments; the largest impact on the terrestrial environment occurs during the

clearing of terrain for construction of site facilities [14] and [3].

• Aquatic Environment; to varying degrees, aquatic organisms will be subject to entrainment,

impingement and increases in temperature and salinity. Sites that pose minimal risk to

important ecological areas are accepted [14] and [3].



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6. Sociology

Social criteria include the following:

• Tourism & Archaeology; touristy destinations should be as far away as possible from the

nuclear power plant. Sites that pose minimal risk to important heritage areas (historical,

archaeological areas) are accepted.

• Population distribution; the comparison and ranking of potential sites are preformed based on

appropriate suitability factors, with the most sophisticated being used in the final phase.

Areas close to major population centers with population more than 100,000 are rejected [1]

and [5].

7. Land Uses The use of the land in the region may influence site selection. Account should be also taken

in possible future developments and regional planning [1]. It includes:

• Agriculture Lands; the proximity to an agricultural zone is obviously a drawback compared

to most of the other sites which are in desert areas. Depositional effects on agricultural land

have to be considered [13] and [9].

• Special Land Use; lands with special use, eg. Public lands, citizen lands,…etc.

C. Engineering and Economic Criteria These considerations relate to the construction and operation of the power plant itself and its

function of producing electricity in a safe and efficient manner.

8. Economy Economy is always a factor in any project; however, it is of vital importance to poor

countries. It includes the following critrtia:

• Site Topography; the problems of construction arise mainly from the relief and the need for

earth fill or removal. Relatively flat terrains have an advantage from this standpoint. The

cliffs and mountain are a disadvantage [5].

• Grid Connection; proximity to appropriate existing electricity infrastructure are acceptance

criterion. Potential cost components are transmission construction [1].

• Proximity to Hazardous Facilities; sufficient distance to current and projected minerals, oil

and gas exploration areas is acceptance criterion [1].

9. Construction Existing of raw materials and fresh water supply during construction as well as labor is

acceptance criteria. However, the most affected criterion in construction is:

• Access Facilities; availability or Proximity of transport routes (roads, railway and airports)

are an acceptance criterion due to facilitate the movement of plant equipment and other heavy

castings during the construction [23].



10

10. Operating Costs The operating costs of the plant consist of two main items:

• Proximity to Major Load Center; nuclear power plants should be located relatively close to

major load centers (large populations, energy intensive industries) in order to minimize the

cost of transmission lines and power losses [1].

• Security; areas around airports and military installations having a radius equal to a screening

distance value are rejected. The sites in mountainous desert areas are to some extent

vulnerable to terrorist actions; on the other hand, capes are a good deal easier to protect [1]

and [5].

Fig.2: Constraints in Binary Overly Model in Arc GIS

6. RESULTS AND DISCUSION

6.1.Siting Nuclear Power Plant Via Mcda & Gis

6.1.1. Phase I: Regional Analysis The regional analysis of siting process is based on the philosophy of exclusion criteria (six

above mentioned constraints).

The first model configuration was that of the Binary overlay using Low Risk approach in

which the logical OR operator is used to determine the outcome of the combination (the candidate

areas). Six constraints were represented in binary maps, combined and a masking layer was created

to eliminate the lands considered as constraints (rejection criteria) in Arc GIS Software (Fig.3). The

following maps were created based on binary techniques (1/0) to combine a final map that show the

candidate areas and excluded areas (Fig.4). The following candidate siting area that screened by the

six constraints:

Region 1: North Western Mediterranean Coast

Region 2: North Eastern Mediterranean Coast

Region 3: Red Sea & Gulf of Suez

Region 4: Qaroun Lake



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Fig.3a: Binary Map of Built-up Areas & Fig.3b: Binary Map of Major

Communities Surface Faults

Fig.3c: Binary map of freshwater Fig.3d: Binary map of availability

source of cooling water

Fig.3e: Binary map of built-up Fig.3f: Binary map of areas where

areas & communities emergency plan can't be apply



12

Fig.4: Final map of candidate areas

6.1.2. Phase II: Screening and Ranking of Potential Sites Weighted Overlay methods such as weighted linear combination (WLC) and Analytic

Hierarchy Process (AHP) have been proposed for processing site characteristics whose factors are

ratio or interval scaled. To evaluate the suitability of each alternative and each criterion is first

evaluated independently. This evaluation is accomplished by defining a utility function that

translates quantifiable site characteristics into a common suitability scale expressing preferences for

one site over another.

The candidate areas were obtained after eliminated the areas don't achievement the nuclear

safety conditions (six constraints) using OR operator (Boolean logic). The screening and ranking of

the potential sites based on the philosophy of suitability criteria (factors) using WLC method. The

(factors) and 22st sub-criteria represented in normalized maps after unifying all of them to 0 – 1 score

scales. This unification is intended to be able to measure effect of each criterion in the same way.

The pairwise comparison was conducted and relative scores were fed into Expert Choice Software

Package that runs the Analytic Hierarchy Process. This run yielded the scores used in the criteria

maps. Also the relative weights of each criterion were derived by pairwise comparison from the

relative importance of each criterion in Expert Choice Software Package. Figure (5) shows the

normalized version of some criteria map that used in the MCDA add-in as an open source tool in Arc

GIS 10.1 developed by [24]. As mentioned, the factor weights and scores obtained using AHP

method by Expert Choice software Package (Fig.6) were then imported to the MCDA tool in Arc

GIS 10.1 as shown in Figure (7).



13

Fig.5a: Surface geology map Fig.5b: Rain fall map

Fig.5c: Soil map Fig.5d: Hydrogeology map

Fig.5e: Seismicity map Fig.5f: Proximity to major load center map



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Fig.5g: Proximity to agricultural lands map Fig.5h: Proximity to transportations map

Fig. 6: Pairwise comparison matrix used Fig.7: WLC of MCDA tool in Arc GIS

in Expert Choice Software Package

Fig.8: Final suitability map of NPP site using WLC tool in Arc GIS 10.1



15

Finally, the result of the composite map of twenty two criteria (layers) was multiplied by one

masking layer. This layer is called suitability map of potential sites which represented by several

polygons. The result of potential site priorities using WLC which presented in suitability map (Fig.

8), the best sites is polygons that have green color.

6.1.3. Phase III: Ranking and Comparison of Candidate Sites Four highest score sites (all located at the North Western Coast of Mediterranean Sea and

Red Sea Coast) were chosen as candidate sites after eliminated the lowest score sites. Analytic

Hierarchy Process method is a simple and produces very good results. So the researcher used it for

ranking and comparison of candidate sites to select a suitable site for nuclear power plant

construction. The above 22nd

mentioned suitability factors were used for ranking and comparison.

The weights and scores (normalized eigenvectors) of criteria and sites are obtained by pairwise

comparisons between the attributes, and between the sites with respect to the each criterion. The

Expert choice Software package was used for solving the matrices as show in Figure (8) And the

final weight of alternatives j was obtained by formula (7). Pairwise judgment of all related hierarchy

elements were done by the Authors.

The numerical values of all the eigenvectors and inconsistency ratio as well as final ranking

of the alternatives (candidate sites) are given in Table (2) and presented in Figure (9).

Based on the results of calculating of eigenvectors of the matrices using AHP method, El

Dabaa site is the most suitable sites for establishing the nuclear power plant due to less seismicity

and less environmental impact than others and proximity to major load centers.

Fig.9: Cumulated histogram for final ranking of candidate sites

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

Sc

ore

s

1 2 3 4

Candidate sites



16

Table (2): Eigenvectors and Preference among the Sites

Cri

teri

a g

rou

p

Attribute ί

Su

b -

Att

rib

ute

ί

Candidate sites (Alternatives)

wi

C.R Wij

West

Negila

East

Negila Dabaa

South

M.alam

Saf

ety

& H

ealt

h

Geology

X1 0.286 0.286 0.286 0.143 0.083 0.0000

X2 0.227 0.227 0.424 0.122 0.083 0.0039

X3 0.200 0.200 0.200 0.400 0.050 0.0000

Oceanograph

y

X4 0.250 0.250 0.250 0.250 0.083 0.0000

X5 0.250 0.250 0.250 0.250 0.083 0.0000

X6 0.250 0.250 0.250 0.250 0.083 0.0000

X7 0.286 0.286 0.286 0.143 0.050 0.0000

Seismicity

X8 0.227 0.227 0.424 0.122 0.125 0.0039

Meteorology X9 0.200 0.200 0.200 0.400 0.050 0.0000

X10 0.200 0.200 0.200 0.400 0.050 0.0000

En

vir

on

men

tal

&

So

cia

l

Ecology

X11 0.286 0.286 0.286 0.143 0.030 0.0000

X12 0.286 0.286 0.286 0.143 0.030 0.0000

Land use X13 0.200 0.200 0.200 0.400 0.052 0.0000

X14 0.167 0.167 0.500 0.167 0.049 0.0000

Sociology

X15 0.250 0.250 0.250 0.250 0.018 0.0000

X16 0.204 0.204 0.347 0.246 0.018 0.0000

En

gin

eeri

ng &

Eco

no

mic

Economy

X17 0.286 0.286 0.286 0.143 0.012 0.0000

X18 0.141 0.263 0.455 0.141 0.030 0.0039

X19 0.250 0.250 0.250 0.250 0.030 0.0000

Construction X20 0.141 0.263 0.455 0.141 0.018 0.0039

Operating

cost

X21 0.141 0.263 0.455 0.141 0.012 0.0039

X22 0.141 0.263 0.455 0.141 0.012 0.0039

Total 0.2425

0.2512

0.3249 0.2325

1 1

Ranking 3 2 1 4

8. CONCLUSION AND RECOMMENDATIONS

There are three phases for nuclear power plant site selection. The binary overlay method was

used in Phase I through OR operator for selecting the candidate areas. The WLC & AHP methods

was used to screen and select the potential sites (phase II) in Arc GIS 10.1 software. Twenty two

factors related to safety, environment, economy and local society were represented in normalized

maps after unifying all of them to 0 – 1 score scales. The relative scores and weights were derived by

pairwise comparison (AHP) method. The final composite map of potential site priorities were

represented by several polygons produced in MCDA add-in as an open source tool in Arc GIS 10.1.

In phase III, four sites, all located on the North western coast and Red Sea, of highest scores

were chosen as Candidate sites after eliminated the lowest score sites. The AHP method was applied

to select preferred candidate site and calculating the eigenvectors in Expert Choice Software

Package.



17

The weights and scores were based on the judgments and preferences of the authors.

Therefore, it would not allow for misinterpretation or misjudgment of the weights or scores. The

results of the ranking process indicated that El Dabaa site (site 3) is the superior to the other sites

due to less seismicity and less environmental impact, proximity to major load centers. To select an

additional site must be re-ranked the next best sites after selecting El Dabaa site from the eight

considered.

7. REFERENCES

1. IAEA, Site Survey for Nuclear Power Plants. IAEA, Vienna. . Safety Series No. 50-SG-S9,

1984.

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