Scientia Iranica A (2015) 22(2), 319{330

Sharif University of Technology

Scientia Iranica
Transactions A: Civil Engineering
www.scientiairanica.com

An integrated decision support system for dam site

selection
Y. Minatoura , J. Khazaiea; , M. Ataeib and A.A. Javadic
a. Department of Civil Engineering, Razi University of Kermanshah, Kermanshah, Iran.
b. Department of Mining Engineering, Shahrood University of Technology, Shahrood, Iran.
c. Department of Engineering, University of Exeter, Exeter, UK.
Received 5 February 2013; received in revised form 20 September 2013; accepted 28 July 2014

KEYWORDS Abstract. Selection of suitable site for dam is one of the problems associated with
Dam site selection; water resources management, and it is dependent on a set of qualitative and quantitative
Fuzzy AHP; criteria. Such problems can be resolved using Multi-Criteria Decision-Making (MCDM)
Multi-Criteria approaches. This study aims to develop a MCDM method integrated with fuzzy logic and
Decision-Making group decision-making, speci cally focused on dam site selection. A fuzzy AHP method
(MCDM); was extended to group decision making, and then the resulting group fuzzy AHP was
VIKOR; combined with the VIKOR method. In the integrated method, fuzzy concepts were used
Water resources to account for decision-makers' subjective judgments when considering the uncertainties of
management. the site selection process. Group fuzzy AHP was used to determine the weights of di erent
criteria and VIKOR was used to rank alternatives. The integrated method was applied to
selection of the optimal site for an earth dam in Harsin city, Iran. The results show that
the proposed method is an e ective and reliable method in selecting the optimal dam site.
© 2015 Sharif University of Technology. All rights reserved.

1. Introduction criteria related to water resource management them-

selves have many di erent characteristics accompanied
Selection of the best site for a dam is among the by uncertainties. This makes decision-making and
decisions that are of particular importance in water planning a very complex task. The most important
supply management, as an optimal selection can im- characteristics of water resource management are as
prove the security of the water supply of a region and follows:
groundwater regeneration. However, dam construction
is very expensive and has long-term environmental 1. The attributes are in con ict with each other in
impacts. Therefore, the selection of optimal location some cases;
for dam could lead to signi cant cost saving. In order 2. Some attributes are not measurable;
to locate the optimal dam site, various studies are 3. Various organizations and individuals are interested
necessary. Decision-making and planning on issues of in water resources;
such signi cance cannot be conducted only through the
traditional viewpoint of cost-bene t analysis. Decisions 4. Each attribute contains a lot of information and a
on these issues are related to di erent criteria, and the lot of research is needed;
5. Evaluation of some qualitative attributes is complex
and they can be judged only by verbal variables;
*. Corresponding author.
E-mail addresses: yasser.4607@gmail.com (Y. Minatour); 6. Qualitative attributes are associated with uncer-
J.khazaie@razi.ac.ir (J. Khazaie); ataei@shahroodut.ac.ir tainty, so the opinions of di erent people must be
(M. Ataei); a.a.javadi@ex.ac.uk (A.A. Javadi) used to assess them.
320 Y. Minatour et al./Scientia Iranica, Transactions A: Civil Engineering 22 (2015) 319{330

Multi-Criteria Decision Making (MCDM) meth- San Francisco river basin management [11], urban
ods are very suitable in addressing these problems. In water supply of Zahedan, Iran [12], prioritization of
MCDM, among all possible alternatives, the best one water management for sustainability [13], urban water
is selected based on evaluation criteria. MCDM meth- supply, Melbourne city, Australia [14], ranking the
ods have been usually introduced based on classical reservoirs systems [15], environmental assessment of
mathematics. Often MCDM problems are dependent water programmers [16], water resources planning [17],
on di erent and in some cases, con icting criteria. assessment model of water supply system [18], applica-
It is also possible that complying with the nature of tion of recycled water for household laundry in Sydney,
decision making problems, the expert opinions could Australia [19], mapping urban water demands [20],
be di erent, or there could exist no exact information evaluating water transfer projects [21], and ood risk
about them. In such conditions, utilizing traditional assessment [22].
MCDM methods does not render the capacity to handle The main objectives of the present study are
uncertainties and may in some cases lead to wrong rstly to determine e ective criteria in dam site se-
decision making results. To address this problem, lection, secondly to present a fuzzy MCDM method to
researchers have expanded the MCDM methods based determine the criteria weights based on opinions of a
on fuzzy sets (fuzzy MCDM methods). decision making group and rating proposed sites, and
Analytic Hierarchy Process (AHP) which was rst thirdly to select the optimal site for the Harsin dam as
introduced by Saaty [1], is one of the most powerful a case study.
and simplest MCDM methods. Many researchers A fuzzy AHP approach was extended to group
have extended the AHP based on fuzzy sets (fuzzy decision-making. The resulting group fuzzy AHP was
AHP methods). Fuzzy AHP methods are systematic then combined with VIKOR. Group fuzzy AHP was
approaches to the determination of the criteria weights used to determine the weights of criteria, and VIKOR
and justi cation problem by using the concepts of was used to rank alternatives. The integrated method
fuzzy set theory and hierarchical structure analysis. was applied to the selection of the optimal site for an
The most important and earliest fuzzy AHP methods earth dam in Harsin city, Iran.
include the following: Van Laarhoven and Pedrcyz [2]
presented the rst study on the application of fuzzy 2. Method
logic principle to AHP; Buckley [3] initiated trapezoidal
fuzzy numbers to express the decision maker's evalua- The method used in this study is based on the integra-
tion on alternatives with respect to each criterion while tion of fuzzy AHP and VIKOR methods. The weights
Van Laarhoven and Pedrcyz [2] used triangular fuzzy that are obtained from group fuzzy AHP calculations
numbers; Chang [4] introduced a new approach for han- are considered and used in VIKOR calculations. Deci-
dling fuzzy AHP with the use of triangular fuzzy num- sion making in this integrated method involves several
bers for pair-wise comparison scale of fuzzy AHP, and essential steps.
the use of the extent analysis method for the synthetic 2.1. Forming a team of decision makers
extent values of the pair-wise comparisons; Cheng [5] This team involves dam construction experts and deci-
proposed a new algorithm for evaluating naval tactical sion makers.
missile systems by the fuzzy AHP based on grade value
of membership function; Deng [6] presented a fuzzy 2.2. Determining e ective criteria and
approach for tackling qualitative multi-criteria analysis potential alternatives
problems in a simple and straightforward manner. In this step, e ective criteria in locating the dam site
The VIKOR which is an abbreviation of the are determined by using comprehensive review of liter-
Serbian expression of \VlseKriterijumslca Optimizacija ature and expert opinions. The potential alternatives
I Kompromisno Resenje", meaning \multi-criteria op- are then proposed based on determined criteria.
timization and compromise solution" [7], was rst 2.3. Developing the hierarchical structure
introduced by Opricovic [8] as an MCDM method. The The hierarchy diagram is a graphic representation of
VIKOR is used to solve discrete multi criteria problems a complex problem in which objectives, criteria, and
with non-commensurable and con icting criteria. It alternatives are at the highest, intermediate and lowest
focuses on ranking and selecting from a set of alter- levels, respectively.
natives, and determines compromise solutions for a
problem with con icting criteria. It is one of the most 2.4. De ning the fuzzy scale
widely used MCDM methods in rating problems. In order to express the importance of criteria and
To date, MCDM and fuzzy MCDM methods have formation of the pair-wise comparison matrix, a fuzzy
been used in the various elds of water engineering by scale is de ned by decision makers. Table 1 shows a
many researches. These include urban water supply fuzzy scale that was used in this study. The graphical
of Zahedan city, Iran [9], watershed management [10], form of this scale is shown in Figure 1.
 Y. Minatour et al./Scientia Iranica, Transactions A: Civil Engineering 22 (2015) 319{330 321

Table 1. Fuzzy scale used in this study. ~

W
x~ij = ~ i ; (i; j = 1; 2; 3; :::; n; i 6= j ); (5)
Linguistic variables Fuzzy numbers Wj
Very Low (VL) (0.1, 0.1, 0.3)
where A~ is a fuzzy pair-wise comparison matrix, x~ij is
Low (L) (0.1, 0.3, 0.5) a triangular fuzzy number that expresses the relative
Medium (M) (0.3, 0.5, 0.7) importance of criterion i with respect to criterion j .
High (H) (0.5, 0.7, 0.9) W~ i and W~ j are the aggregated fuzzy weights of criteria
Very High (VH) (0.7, 0.9, 0.9) i and j , respectively.
2.8. Applying Chang's extent analysis
Chang's extent analysis is used to determine the rel-
ative weights of the criteria. The method of Chang's
extent analysis [4] is brie y described below.
Let X = fx1 ; x2 ; x3 ; :::; xn g be an object set, and
G = fg1 ; g1 ; g3 ; :::; gm g be a goal set. Each object is
taken and extent analysis is performed for each goal.
Therefore, m extent analysis values for each object can
be obtained as:
Figure 1. Graphical form of fuzzy scale used in this Mgi1 ; Mgi2 ; :::; Mgim ; i = 1; 2; 3; :::; n; (6)
study.
where Mgij (j = 1; 2; 3; :::; m) all are triangular fuzzy
2.5. Pooling the decision maker's opinions numbers. The steps of Chang's extent analysis are as
The expert's opinions, expressing the importance of the follows.
criteria are pooled using a questionnaire. The values of fuzzy synthetic extent with respect
to the ith object (Si ) are de ned as:
2.6. Obtaining the aggregated fuzzy weight of
2 3 1
criteria m
X O Xn X
m
The decision maker's opinions about the importance of Si = Mj
gi
4 Mj 5 : gi (7)
the di erent criteria are aggregated. The aggregated j =1 i=1 j =1
~ j ) is obtained as:
fuzzy weight of criterion j (W
P
~ j = (aj ; bj ; cj ); To obtain m j
j =1 Mgi , the fuzzy addition operation of
W (1) m extent analysis values is performed for a particular
where: matrix such that:
0 1
k fajk g;
aj = Min m
X m
X m
X m
X
Mgij = @ lj ; mj ; uj A : (8)
Pk j =1 j =1 j =1 j =1
bj = k=1 bjk ;
K n
hP
m jP i 1
To obtain i=1 j =1 Mgi , the fuzzy addition
k fcjk g; k = 1; 2; :::; K;
cj = Max (2) j
operation of Mgi (j = 1; 2; 3; :::; m) values is performed
with K being the number of decision groups. such that:
!
n X
m n n n
2.7. Forming the fuzzy pair-wise comparison X
Mgij =
X
li ;
X
mi ;
X
ui : (9)
matrix of criteria i=1 j =1 i=1 i=1 i=1
Each criterion in the hierarchical structure is compared
with other criteria in a fuzzy pair-wise comparison The inverse of the vector in Eq. (9) is then computed
matrix. The fuzzy pair-wise comparison matrix is according to:
de ned as:
2 3 2 3 1
n X
m  
(1; 1; 1) x~12 : : : x~1n X
j 1 1 1
6 x ~21 (1; 1; 1) : : : x~2n 7 4 M 5 = Pn
gi ; Pn ; Pn :
A~ = 6 . .. .. 7
7
; (3) i=1 j =1 i=1 ui i=1 mi i=1 li (10)
6
. ...
4 . . . 5
x~n1 x~n2 : : : (1; 1; 1) As M1 = (l1 ; m1 ; u1 ) and M2 = (l2 ; m2 ; u2 ) are two
triangular fuzzy numbers, the degree of possibility of
x~ij = (1; 1; 1); (i; j = 1; 2; 3; :::; n; i = j ); (4) M2 = (l2 ; m2 ; u2 )  M1 = (l1 ; m1 ; u1 ) is de ned as:
322 Y. Minatour et al./Scientia Iranica, Transactions A: Civil Engineering 22 (2015) 319{330

The decision matrix can be obtained as:

X = (xij )mn ; (17)
where xij is the performance of alternative Ai with
respect to criterion j . The normalized decision matrix
can be obtained as:
F = (fij )mn ; (18)
Figure 2. The intersection between M1 and M2 [4]. xij
fij = qP
m
; i =1; 2; 3; :::; m; j =1; 2; 3; :::; n:
2
V (M2  M1 ) = supbmin(M1 (x); M2 (y))c: (11) i=1 xij (19)
yx
The best values (fj+ ) and the worst values (fj ) of all
And it can be expressed as: criterion functions for j = 1; 2; :::; n are determined as:
\
V (M2  M1 ) = hgt(M1 M2 ) = M2 (d) f  = maxi fij ; f = mini fij ; if j 2 B;
j j (20)
8
>
>1 if m2  m1 fj = mini fij ; fj = maxi fij ; if j 2 C; (21)
<
= 0 if l1  u2 (12) in which fj and fj represent the positive ideal solution
>
>
: l1 u2
(m2 u2 ) (m1 l1 ) otherwise and the negative ideal solution for the criterion j ,
respectively; B is the set of bene t criteria (+) and
Figure 2 is a graphical representation of Eq. (12) where C is the set of cost criteria (-).
d is the ordinate of the highest intersection point D The values of Si and Ri for i = 1; 2; 3; :::; m are
between M1 and M2 . To compare M1 and M2 we computed as:
need both values of V (M1  M2 ) & V (M1  M2 ). X n w (f  f )
If the degree of possibility for a convex fuzzy j j ij
Si = ; (22)
number is greater than k, then convex fuzzy Mi (i = j =1(fj fj )
1; 2


k) numbers can be de ned as follows: 2 3
V (M  M1 ; M2 ; :::; Mk ) = V [(M  M1 ) Xn wj (fj fij ) 5
Ri = maxj 4  fj ) ; (23)
j =1 (fj
and (M  M2 ) and ::: and (M  Mk )] P
where wj ( nj=1 wj = 1; wj 2 [0; 1], j = 1; 2; :::; n) are
= minV (M  Mi ); i = 1; 2; 3; :::; k: (13) the relative importance weights of the criterion j .
The Qi values for j = 1; 2; 3; :::; n are computed
Assuming that: as:
d0 (Ai ) = minV (Si  Sk ) for k = 1; 2; :::; n; k 6= i;
   
S S Ri R
Qi = v i + (1 v ) ; (24)
(14) S S R R
the weight vector is given by: S  = minSi ; (25)
i
W 0 = (d0 (A1 ); d0 (A2 ); :::; d0 (An ))T ; (15)
S = maxSi ; (26)
i
where Ai (i = 1; 2:::n) are n elements. The normalized
weight vectors are obtained as: R = minRi ; (27)
i
W = (d(A1 ); d(A2 ); :::; d(An ))T ; (16) R = maxRi ; (28)
i
where W is a non-fuzzy number.
with v introduced as a weight for the strategy of
2.9. Applying the VIKOR method to rank maximum group utility, whereas 1 v is the weight
alternatives of the individual regret.
Assume an MCDM problem has m alternatives Ranking the alternatives (sorting by the values Si ,
(A1 ; A2 ; :::; Am ) and n decision criteria (C1 ; C2 ; :::; Cn ). Ri and Qi in decreasing order), the results will be three
The following steps are involved in the VIKOR ranking lists. Proposed as a compromise solution, the
method [23,24]. alternative A1 which is the best rank by the measure
 Y. Minatour et al./Scientia Iranica, Transactions A: Civil Engineering 22 (2015) 319{330 323

Qi (minimum) will be reached if the following two Annual yield (C3 ): It is the annual volume of the
conditions (Condition 1 and Condition 2) are satis ed: water that passes through the cross section of the river
at the dam site. Annual yield plays an important role
Condition 1. Acceptable advantage: in locating the dam site.

QA2 QA1  n 1 1; (29) Topographic conditions (C4 ): The existence of

a secondary valley or rock abutments with suitable
where A2 is the alternative with second position in topography around the main river is important for
the ranking list by Qi , and n is the number of constructing dam spillway. In general, the best
alternatives. site for an earth dam is where a wide valley with
high walls leads to a narrow canyon with tenacious
walls.
Condition 2. Acceptable stability in decision mak-
ing:
The alternative A1 must also be the best rank Access to materials and facilities (C5 ): Access to
by Si or/and Ri . This compromise solution is stable materials (borrow sources, cement, etc.) and facilities
within a decision making process, which could be the (power transmission lines, oil and gas distribution
strategy of maximum group utility (when v > 0:5 is pipelines, road, etc.) is also important in deciding the
needed), or \by consensus" v  0:5, or \with veto" best location for a dam.
(v < 0:5).
If one of the conditions is not satis ed, then a set Economic development (C6 ): The e ects of dam
of compromise solutions is proposed, which consists of: construction on agricultural and industrial develop-
ment, power generation, shery, job creation, etc.
ˆ Alternatives A1 ; A2 ; :::; AM if Condition 1 is not (which are related to economic development) are re-
satis ed; AM is determined by the relation QAM garded as important attributes for selecting the dam
QA1  (1=(n 1)) for maximum M (the positions site.
of these alternatives are \in closeness"), or:
ˆ Alternatives A1 and A2 if only Condition 2 is not Water quality (C7 ): Quality of water stored in
satis ed. reservoirs used for drinking and agricultural purposes
is important.
3. Case study
Damage of dam body and reservoir (C8 ): Envi-
The case study focuses on selection of a suitable site ronmental damages (wildlife, vegetation, cutting trees,
for an earth dam in Harsin City. The aims of this etc.), and socio-economic damages (destroying mines,
dam are developments in agriculture and industry, historical monuments, agricultural lands, displacement
drinking water supply, power generation, sheries, etc. of peoples, displacement of roads, railway and power
in Harsin City, Iran. The city is located 16 km east lines, changing the route of oil and gas pipelines,
of Kermanshah (the capital of Kermanshah Province, telecommunication facilities, etc.) caused by the con-
Iran). Its longitude and latitude are 34 160 and 45 350 struction of the dam body should be considered.
respectively.
In the rst step, to determine e ective factors in Volume of reservoir (C9 ): When the reservoir that
selecting an appropriate earth dam site for the Harsin is created after dam construction has a large volume,
dam, a comprehensive literature review was conducted the surface area of the reservoir water is increased and
and the most important criteria were selected. A it has more impact on the local climate. Furthermore,
brief explanation of the selected criteria is presented evaporation and potential for water pollution will
below. increase with increasing the surface area. Therefore,
the dam should be constructed where the reservoir
Health of dam site (C1 ): In this criterion, geotech- capacity is optimal.
nical and geological parameters were considered.
River ow regime (C10 ): Seasonal rivers have
Overall cost (C2 ): The overall cost includes the more sediment transport and hence lower water quality.
costs of construction of dam body and reservoir, wa- In addition, the management of water resources is
ter diversion during construction, water transfer to more dicult due to the lack of accurate informa-
consumption location, energy supply, site preparation, tion on the discharge of water entering the reservoir.
land use, and other costs associated with the dam Therefore, a permanent ow regime would be more
project. favorable.
324 Y. Minatour et al./Scientia Iranica, Transactions A: Civil Engineering 22 (2015) 319{330

Figure 3. Alternatives for the Harsin earth dam site.

Water diversion and transfer (C11 ): The dam residential lands for dam construction, reservoir dewa-
site should be located where the costs of water diversion tering and also utilizing the dam water in downstream
during construction and water transfer to consumption should be considered.
location are minimum.
Political impacts (C18 ): The dam construction goals
Annual volume of sediment (C12 ): If the annual for reducing political tensions including water supply
volume of sediment entering the reservoir is kept to of a city, preventing grievances and immigration of
minimum, the volume of the reservoir during its useful residents of a border city, etc. are among the attributes
life, water quality and the eciency of dam would be that should also be considered.
higher. After collecting and evaluating the required in-
formation based on the selected criteria (mentioned
Probability of dam break (C13 ): The dam should above), four feasible alternatives were proposed for the
be constructed in a place that minimizes the socio- Harsin earth dam site. The locations of the proposed
economic risks posed by a possible dam break. alternatives are shown in Figure 3 and identi ed by `A',
`B', `C' and `D' letters.
Probable maximum ood (C14 ): The maximum After selecting the criteria for locating the earth
volume of water caused by thawing snow and ice dam site and considering alternatives (see Figure 3),
or other atmospheric precipitation occurring within the integrated fuzzy AHP and VIKOR method was
a speci ed return period in rivers is called probable applied to select the best site. Figure 4 shows the
maximum ood. problem of Harsin earth dam site selection using a
hierarchical structure. The structure has three levels:
Average annual evaporation (C15 ): Due to the objective (locating the Harsin earth dam site), criteria
annual average temperature di erences in di erent (C1 to C18 ) and alternatives (A, B, C, and D).
regions in Iran, evaporation from the dam reservoirs To assess the relevance of the criteria incorporated
varies regionally. This variability has e ects on the in the fuzzy AHP group method, a questionnaire was
retention time of water in the reservoir (in terms of developed, and 4 experts (E1 , E2 , E3 and E4 ) involved
volume) and consequently on the eciency of the dam. in Harsin earth dam project were asked to express the
importance of each criterion using linguistic variables
Environmental impacts (C16 ): Changing weather which were inserted in the questionnaire. Table 2
conditions, vegetation, and wild life are other at- summarizes the expert opinions about the importance
tributes that play signi cant roles in locating the dam of the di erent criteria.
site. The aggregated fuzzy weights of the criteria were
obtained by integrating expert opinions using Eqs. (1)
Social impacts (C17 ): The social impacts of relo- and (2). Then, a fuzzy pair-wise comparison matrix
cation of population centers and the integration of for determining the weights of criteria was formed
di erent ethnic cultures due to the appropriation of according to Table 3 (see Eqs. (3) to (5)).
 Y. Minatour et al./Scientia Iranica, Transactions A: Civil Engineering 22 (2015) 319{330 325

Figure 4. Problem of the Harsin earth dam site selection using a Hierarchical structure.
Table 2. Expert opinions about the importance of the W 0 = (1:000; 0:960; 0:989; 0:978; 1:000; 0:995; 0:989;
di erent criteria.
Criteria Expert 0:973; 0:960; 0:949; 0:995; 0:968; 0:955;
E1 E2 E3 E4
C1 VH VH VH VH
0:960; 0:960; 0:955; 0:929; 0:887)T :
C2 M L H H After normalization, the normalized weights of the
C3 VH H VH H criteria were calculated using Eq. (16) as:
C4 H VH H M W = (0:0575; 0:0552; 0:0568; 0:0562; 0:0575; 0:0572;
C5 VH VH VH VH
C6 VH VH VH H 0:0568; 0:0559; 0:0552; 0:0546; 0:0572; 0:0556;
C7 H H VH VH
0:0549; 0:0552; 0:0552; 0:0549; 0:0534; 0:0509)T :
C8 H H M H
C9 L H H M The VIKOR method was then applied to rank the
alternatives. The normalized decision matrix was
C10 H M L L obtained using Eqs. (18) and (19), and the resulting
C11 VH H VH VH matrix is given in Table 7. Table 8 presents the values
C12 M M H H of fj and fj with respect to each criterion using
Eqs. (20) and (21).
C13 M H M L Using Eqs. (22) and (23), the values Si and Ri for
C14 H H L M alternative i were obtained as:
C15 M H L H SA = 0:5424; RA = 0:0575;
C16 M M H L
C17 M M L L SB = 0:3452; RB = 0:0572;
C18 L L L L
SC = 0:6012; RC = 0:0575;

The weights of the criteria were calculated using SD = 0:4996; RD = 0:0572:

the fuzzy HAP process where the values of fuzzy
synthetic extent with respect to the ith object (i = The values of S  , S , R and R were obtained
1; 2; :::; 18) were obtained using Eq. (7); the results according to Eqs. (25)-(28) as:
are given in Table 4. Table 5 lists the degrees of
possibility Si  Sk (i; k = 1; :::; 18; i 6= k) calculated S  = 0:3452; S = 0:6012;
using Eq. (12), while Table 6 enumerates the mini-
mum degrees of possibility Sj  Si obtained using R = 0:0572; R = 0:0575:
Eq. (14).
These values (Table 6) yield the following weight Then, using Eq. (24) the VIKOR values (Qi ) for each
vector according to Eq. (15): alternative were obtained as:
326 Y. Minatour et al./Scientia Iranica, Transactions A: Civil Engineering 22 (2015) 319{330

Table 3. Fuzzy pair-wise comparison matrix of criteria.

C1 C2 C3 C4 C5 C6 C7 C8 C9
C1 (1.00,1.00,1.00) (0.78,1.64,9.00) (0.78,1.13,1.80) (0.78,1.29,3.00) (0.78,1.00,1.29) (0.78,1.06,1.80) (0.78,1.13,1.80) (0.78,1.38,3.00) (0.78,1.64,9.22)
C2 (0.11,0.61,1.29) (1.00,1.00,1.00) (0.11,0.69,1.80) (0.11,0.79,3.00) (0.11,0.61,1.29) (0.11,0.65,1.80) (0.11,0.69,1.80) (0.11,0.85,3.00) (0.11,1.00,9.00)
C3 (0.56,0.89,1.29) (0.56,1.45,9.00) (1.00,1.00,1.00) (0.56,1.14,3.00) (0.56,0.89,1.29) (0.56,0.94,1.80) (0.56,1.00,1.80) (0.56,1.23,3.00) (0.56,1.45,9.00)
C4 (0.33,0.78,1.29) (0.33,1.27,9.00) (0.33,0.88,1.80) (1.00,1.00,1.00) (0.33,0.78,1.29) (0.33,0.82,1.80) (0.33,0.88,1.80) (0.33,1.08,3.00) (0.33,1.27,9.00)
C5 (0.78,1.00,1.29) (0.78,1.64,9.00) (0.78,1.13,1.80) (0.78,1.29,3.00) (1.00,1.00,1.00) (0.78,1.06,1.80) (0.78,1.13,1.80) (0.78,1.38,3.00) (0.78,1.64,9.00)
C6 (0.56,0.94,1.29) (0.56,1.55,9.00) (0.56,1.06,1.80) (0.56,1.21,3.00) (0.56,0.94,1.29) (1.00,1.00,1.00) (0.56,1.06,1.80) (0.56,1.31,3.00) (0.56,1.55,9.00)
C7 (0.56,0.89,1.29) (0.56,1.45,9.00) (0.56,1.00,1.80) (0.56,1.14,3.00) (0.56,0.89,1.29) (0.56,0.94,1.80) (1.00,1.00,1.00) (0.56,1.23,3.00) (0.56,1.45,9.00)
C8 (0.33,0.72,1.29) (0.33,1.18,9.00) (0.33,0.81,1.80) (0.33,0.93,3.00) (0.33,0.72,1.29) (0.33,0.76,1.80) (0.33,0.81,1.80) (1.00,1.00,1.00) (0.33,1.18,9.00)
C9 (0.11,0.61,1.29) (0.11,1.00,9.00) (0.11,0.69,1.80) (0.11,0.79,3.00) (0.11,0.61,1.29) (0.11,0.65,1.80) (0.11,0.69,1.80) (0.11,0.85,3.00) (1.00,1.00,1.00)
C10 (0.11,0.50,1.29) (0.11,0.82,9.00) (0.11,0.56,1.80) (0.11,0.64,3.00) (0.11,0.50,1.29) (0.11,0.53,1.80) (0.11,0.56,1.80) (0.11,0.69,3.00) (0.11,0.82,9.00)
C11 (0.56,0.94,1.29) (0.56,1.55,9.00) (0.56,1.06,1.80) (0.56,1.21,3.00) (0.56,0.94,1.29) (0.56,1.00,1.80) (0.56,1.06,1.80) (0.56,1.31,3.00) (0.56,1.55,9.00)
C12 (0.33,0.67,1.29) (0.33,1.09,9.00) (0.33,0.75,1.80) (0.33,0.86,3.00) (0.33,0.67,1.29) (0.33,0.71,1.80) (0.33,0.75,1.80) (0.33,0.92,3.00) (0.33,1.09,9.00)
C13 (0.11,0.56,1.29) (0.11,0.91,9.00) (0.11,0.63,1.80) (0.11,0.71,3.00) (0.11,0.56,1.29) (0.11,0.59,1.80) (0.11,0.63,1.80) (0.11,0.77,3.00) (0.11,0.91,9.00)
C14 (0.11,0.61,1.29) (0.11,1.00,9.00) (0.11,0.69,1.80) (0.11,0.79,3.00) (0.11,0.61,1.29) (0.11,0.65,1.80) (0.11,0.69,1.80) (0.11,0.85,3.00) (0.11,1.00,9.00)
C15 (0.11,0.61,1.29) (0.11,1.00,9.00) (0.11,0.69,1.80) (0.11,0.79,3.00) (0.11,0.61,1.29) (0.11,0.65,1.80) (0.11,0.69,1.80) (0.11,0.85,3.00) (0.11,1.00,9.00)
C16 (0.11,0.56,1.29) (0.11,0.91,9.00) (0.11,0.63,1.80) (0.11,0.71,3.00) (0.11,0.56,1.29) (0.11,0.59,1.80) (0.11,0.63,1.80) (0.11,0.77,3.00) (0.11,0.91,9.00)
C17 (0.11,0.44,1.00) (0.11,0.73,7.00) (0.11,0.50,1.40) (0.11,0.57,2.33) (0.11,0.44,1.00) (0.11,0.47,1.40) (0.11,0.50,1.40) (0.11,0.62,2.33) (0.11,0.73,7.00)
C18 (0.11,0.33,0.71) (0.11,0.55,5.00) (0.11,0.38,1.00) (0.11,0.43,1.67) (0.11,0.33,0.71) (0.11,0.35,1.00) (0.11,0.38,1.00) (0.11,0.46,1.67) (0.11,0.55,5.00)
C10 C11 C12 C13 C14 C15 C16 C17 C18
C1 (0.78,2.00,9.00) (0.78,1.06,1.80) (0.78,1.50,3.00) (0.78,1.80,9.00) (0.78,1.64,9.00) (0.78,1.64,9.00) (0.78,1.80,9.00) (1.00,2.25,9.00) (1.40,3.00,9.00)
C2 (0.11,1.22,9.00) (0.11,0.65,1.80) (0.11,0.92,3.00) (0.11,1.10,9.00) (0.11,1.00,9.00) (0.11,1.00,9.00) (0.11,1.10,9.00) (0.14,1.38,9.00) (0.20,1.83,9.00)
C3 (0.56,1.78,9.00) (0.56,0.94,1.80) (0.56,1.33,3.00) (0.56,1.60,9.00) (0.56,1.45,9.00) (0.56,1.45,9.00) (0.56,1.60,9.00) (0.71,2.00,9.00) (1.00,2.67,9.00)
C4 (0.33,1.56,9.00) (0.33,0.82,1.80) (0.33,1.17,3.00) (0.33,1.40,9.00) (0.33,1.27,9.00) (0.33,1.27,9.00) (0.33,1.40,9.00) (0.43,1.75,9.00) (0.60,2.33,9.00)
C5 (0.78,2.00,9.00) (0.78,1.06,1.80) (0.78,1.50,3.00) (0.78,1.80,9.00) (0.78,1.64,9.00) (0.78,1.64,9.00) (0.78,1.80,9.00) (1.00,2.25,9.00) (1.40,3.00,9.00)
C6 (0.56,1.89,9.00) (0.56,1.00,1.80) (0.56,1.42,3.00) (0.56,1.70,9.00) (0.56,1.55,9.00) (0.56,1.55,9.00) (0.56,1.70,9.00) (0.71,2.13,9.00) (1.00,2.83,9.00)
C7 (0.56,1.78,9.00) (0.56,0.94,1.80) (0.56,1.33,3.00) (0.56,1.60,9.00) (0.56,1.45,9.00) (0.56,1.45,9.00) (0.56,1.60,9.00) (0.71,2.00,9.00) (1.00,2.67,9.00)
C8 (0.33,1.44,9.00) (0.33,0.76,1.80) (0.33,1.08,3.00) (0.33,1.30,9.00) (0.33,1.18,9.00) (0.33,1.18,9.00) (0.33,1.30,9.00) (0.43,1.63,9.00) (0.60,2.17,9.00)
C9 (0.11,1.22,9.00) (0.11,0.65,1.80) (0.11,0.92,3.00) (0.11,1.10,9.00) (0.11,1.10,9.00) (0.11,1.00,9.00) (0.11,1.10,9.00) (0.14,1.38,9.00) (0.20,1.83,9.00)
C10 (1.00,1.00,1.00) (0.11,0.53,1.80) (0.11,0.75,3.00) (0.11,0.90,9.00) (0.11,0.82,9.00) (0.11,0.82,9.00) (0.11,0.90,9.00) (0.14,1.13,9.00) (0.20,1.50,9.00)
C11 (0.56,1.89,9.00) (1.00,1.00,1.00) (0.56,1.42,3.00) (0.56,1.70,9.00) (0.56,1.55,9.00) (0.56,1.55,9.00) (0.56,1.70,9.00) (0.71,2.13,9.00) (1.00,2.83,9.00)
C12 (0.33,1.33,9.00) (0.33,0.71,1.80) (1.00,1.00,1.00) (0.33,1.20,9.00) (0.33,1.09,9.00) (0.33,1.09,9.00) (0.33,1.20,9.00) (0.43,1.50,9.00) (0.60,2.00,9.00)
C13 (0.11,1.11,9.00) (0.11,0.59,1.80) (0.11,0.83,3.00) (1.00,1.00,1.00) (0.11,0.91,9.00) (0.11,0.91,9.00) (0.11,1.00,9.00) (0.14,1.25,9.00) (0.20,1.67,9.00)
C14 (0.11,1.22,9.00) (0.11,0.65,1.80) (0.11,0.92,3.00) (0.11,1.10,9.00) (1.00,1.00,1.00) (0.11,1.00,9.00) (0.11,1.10,9.00) (0.14,1.38,9.00) (0.20,1.83,9.00)
C15 (0.11,1.22,9.00) (0.11,0.65,1.80) (0.11,0.92,3.00) (0.11,1.10,9.00) (0.11,1.00,9.00) (1.00,1.00,1.00) (0.11,1.10,9.00) (0.14,1.38,9.00) (0.20,1.83,9.00)
C16 (0.11,1.11,9.00) (0.11,0.59,1.80) (0.11,0.83,3.00) (0.11,1.00,9.00) (0.11,0.91,9.00) (0.11,0.91,9.00) (1.00,1.00,100) (0.14,1.25,9.00) (0.20,1.67,9.00)
C17 (0.11,0.89,7.00) (0.11,0.47,1.40) (0.11,0.67,2.33) (0.11,0.80,7.00) (0.11,0.73,7.00) (0.11,0.73,7.00) (0.11,0.80,7.00) (1.00,1.00,1.00) (0.20,1.33,7.00)
C18 (0.11,0.67,5.00) (0.11,0.35,1.00) (0.11,0.50,1.67) (0.11,0.60,5.00) (0.11,0.55,5.00) (0.11,0.55,5.00) (0.11,0.60,5.00) (0.14,0.75,5.00) (1.00,1.00,1.00)

Table 4. The value of fuzzy synthetic extent with respect QA = 0:8850; QB = 0:0000;
to the ith object.
Si Value QC = 1:0000; QD = 0:3016:
S1 (0.009,0.079,0.813) In the nal step, the alternatives were ranked based on
S2 (0.002,0.048,0.750) Si , Ri , Qi as individuals and the results are presented
S3 (0.007,0.070,0.809) in Table 9. Based on Qi , B and D are the alternatives
S4 (0.004,0.061,0.799) with the rst and second positions, respectively. For
S5 (0.009,0.079,0.813) these two alternatives, Condition 1:
S6 (0.007,0.075,0.809) 1
(QD QB = 0:3016) < ( = 0:33); (30)
S7 (0.007,0.070,0.809) n 1
S8 (0.004,0.057,0.799) is not satis ed based on Eq. (29), but Condition 2
S9 (0.002,0.048,0.750) is satis ed. Therefore, with respect to these results
S10 (0.002,0.039,0.750) (Table 9), B and D are proposed as the best alternatives
S11 (0.007,0.075,0.809) for the Harsin earth dam site. Figure 5 shows the front
S12 (0.004,0.053,0.799)
view of these alternatives.
S13 (0.002,0.044,0.750)
S14 (0.002,0.048,0.750)
4. Sensitivity analysis
S15 (0.002,0.048,0.750) To evaluate the performance of the proposed method,
S16 (0.002,0.044,0.750) a comprehensive sensitivity analysis was carried out
S17 (0.002,0.035,0.585) based on the importance of the criteria. In one of
the tests, the e ect of each criterion was examined
S18 (0.002,0.026,0.420) by reducing the weight of each criterion separately by
 Y. Minatour et al./Scientia Iranica, Transactions A: Civil Engineering 22 (2015) 319{330 327

Table 5. Degrees of possibility Si  Sk .

i k
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
1 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.0 00 1.000 1.000 1.000 1.000 1.000 1.000 1.000
2 0.960 0.971 0.983 0.960 0.966 0.971 0.988 1.000 1.000 0.9 66 0.994 1.000 1.000 1.000 1.000 1.000 1.000
3 0.989 1.000 1.000 0.989 0.995 1.000 1.000 1.000 1.000 0.9 95 1.000 1.000 1.000 1.000 1.000 1.000 1.000
4 0.978 1.000 0.989 0.978 0.984 0.989 1.000 1.000 1.000 0.9 84 1.000 1.000 1.000 1.000 1.000 1.000 1.000
5 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.0 00 1.000 1.000 1.000 1.000 1.000 1.000 1.000
6 0.995 1.000 1.000 1.000 0.995 1.000 1.000 1.000 1 .000 1.0 00 1.000 1.000 1.000 1.000 1.000 1.000 1.000
7 0.989 1.000 1.000 1.000 0.989 0.995 1.000 1.000 1.000 0.9 95 1.000 1.000 1.000 1.000 1.000 1.000 1.000
8 0.973 1.000 0.984 0.995 0.973 0.978 0.984 1.000 1.000 0.9 78 1.000 1.000 1.000 1.000 1.000 1.000 1.000
9 0.960 1.000 0.971 0.983 0.960 0.966 0.971 0.988 1.000 0.9 66 0.994 1.000 1.000 1.000 1.000 1.000 1.000
10 0.949 0.988 0.960 0.971 0.949 0.955 0.960 0.977 0.988 0.955 0.983 0.994 0.988 0.988 0.994 1.000 1.000
11 0.995 1.000 1.000 1.000 0.995 1.000 1.000 1.000 1.000 1.0 00 1.000 1.000 1.000 1.000 1.000 1.000 1.000
12 0.968 1.000 0.978 0.989 0.968 0.973 0.978 0.995 1.000 1.000 0.973 1.000 1.000 1.000 1.000 1.000 1.000
13 0.955 0.994 0.966 0.977 0.955 0.960 0.966 0.983 0.994 1.0 00 0.960 0.988 0.994 0.994 1.000 1.000 1.000
14 0.960 1.000 0.971 0.983 0.960 0.966 0.971 0.988 1.000 1.0 00 0.966 0.994 1.000 1.000 1.000 1.000 1.000
15 0.960 1.000 0.971 0.983 0.960 0.966 0.971 0.988 1.000 1.0 00 0.966 0.994 1.000 1.000 1.000 1.000 1.000
16 0.955 0.994 0.966 0.977 0.955 0.960 0.966 0.983 0.994 1.0 00 0.960 0.988 1.000 0.994 0.994 1.000 1.000
17 0.929 0.978 0.943 0.957 0.929 0.936 0.943 0.964 0.978 0.9 93 0.936 0.971 0.985 0.978 0.978 0.985 1.000
18 0.887 0.950 0.904 0.922 0.887 0.896 0.904 0.931 0.950 0.9 70 0.896 0.941 0.960 0.950 0.950 0.960 0.979

Table 6. Minimum degrees of possibility Si  Sk . Table 7. Normalized fuzzy decision matrix.

Min V (si  Sk ) value Criterion Alternative
min V (S1  Sk ) 1.000
min V (S2  Sk ) 0.960
A B C D
min V (S3  Sk ) 0.989 C1 (+) 0.000210 0.000210 0.000030 0.000270
min V (S4  Sk ) 0.978 C2 (-) 0.008606 0.008216 0.008006 0.007196
min V (S5  Sk ) 1.000 C3 (+) 0.017091 0.016612 0.005397 0.003598
min V (S6  Sk ) 0.995 C4 (+) 0.000150 0.000210 0.000270 0.000270
min V (S7  Sk ) 0.989
C5 (+) 0.000090 0.000150 0.000210 0.000270
min V (S8  Sk ) 0.973
min V (S9  Sk ) 0.960 C6 (+) 0.000150 0.000150 0.000210 0.000210
min V (S10  Sk ) 0.949 C7 (+) 0.000270 0.000210 0.000150 0.000150
min V (S11  Sk ) 0.995 C8 (-) 0.000150 0.000150 0.000210 0.000210
min V (S12  Sk ) 0.968 C9 (+) 0.000150 0.000150 0.000090 0.000090
min V (S13  Sk ) 0.955
C10 (+) 0.005397 0.004888 0.006957 0.006597
min V (S14  Sk ) 0.960
min V (S15  Sk ) 0.960 C11 (-) 0.000150 0.000090 0.000150 0.000270
min V (S16  Sk ) 0.955 C12 (-) 0.000660 0.000690 0.000750 0.000810
min V (S17  Sk ) 0.929 C13 (-) 0.000150 0.000030 0.000270 0.000090
min V (S18  Sk ) 0.887 C14 (-) 0.709743 0.639278 0.226686 0.151124
two levels (i.e., from VH to M). If the importance of C15 (-) 0.047976 0.053973 0.062339 0.056971
C2 , C3 , C4 , C5 , C7 , C8 , C9 , C10 , C11 , C12 , C13 , C14 , C16 (+) 0.000150 0.000270 0.000150 0.000270
C15 , C16 , C17 and C18 is reduced by two levels, the C17 (+) 0.000090 0.000150 0.000150 0.000090
optimal alternatives remain unchanged (alternatives B
and D). C18 (+) 0.000150 0.000210 0.000150 0.000150
328 Y. Minatour et al./Scientia Iranica, Transactions A: Civil Engineering 22 (2015) 319{330

Table 8. Best and worst values with respect to each

criterion.
Criterion fj fj
C1 (+) 0.000270 0.000030
C2 (-) 0.007196 0.008606
C3 (+) 0.017091 0.003598
C4 (+) 0.000270 0.000150
C5 (+) 0.000270 0.000090
C6 (+) 0.000210 0.000150
C7 (+) 0.000270 0.000150
C8 (-) 0.000150 0.000210
C9 (+) 0.000150 0.000090
C10 (+) 0.006957 0.004888
C11 (-) 0.000090 0.000270
C12 (-) 0.000660 0.000810
C13 (-) 0.000030 0.000270
C14 (-) 0.151124 0.709743
C15 (-) 0.047976 0.062339 Figure 5. Front view of the proposed alternatives for the
C16 (+) 0.000270 0.000150 Harsin earth dam site: (a) Alternative B; and (b)
C17 (+) 0.000150 0.000090 alternative D.
C18 (+) 0.000210 0.000150
the weights of the criteria is of great importance as
they have qualitative nature and are associated with
If the importance of C1 is reduced by two levels, uncertainties. To deal with this feature, the fuzzy AHP
the set of alternatives If the importance of C1 is reduced approach performed well.
by two levels, the set of alternatives B, C and D are Once the weights of the criteria are determined
selected as the optimal alternatives. If the importance and all other information from potential dam sites
of C4 is reduced by two levels, the alternative B climbs are collected, a decision-making system is needed to
to rst position in overall ranking. prioritize di erent alternatives. In this study, the
Considering the results of the sensitivity analysis VIKOR method was used for prioritization. The
and local surveys, the experts involved in design of method presented in this paper was applied to locate
the dam con rmed the soundness of the research the optimal site for the Harsin dam, Iran. Among
methodology and ndings. the potential alternative locations, sites `B' and `D'
were found to be the best alternatives. Finally, by
5. Conclusions considering the results of this integrated method and
consulting experts, the alternative `D' was selected as
This paper has two main contributions. First, it the optimal location for the Harsin earth dam site. The
presents the in uential criteria and their corresponding proposed method is considered an e ective and reliable
weights to locate optimal site for a dam. Second, it method in selecting the optimal dam site.
presents a combined group fuzzy AHP and VIKOR
method to locate the dam site. The proposed method Acknowledgment
has a number of advantages. For instance, it en-
gages di erent qualitative and quantitative variables The authors would like to appreciate Kermanshah
in selecting the nal choice. The determination of Regional Water Company and Pandam Consulting

Table 9. Ranking the alternatives.

Alternative Based on Si Based on Ri Based on Qi Proposed alternative(s)
Value Rank Value Rank Value Rank Based on Based on Generally
Condition 1 Condition 2
A 0.5424 3 0.0575 2 0.8850 3
B 0.3452 1 0.0572 1 0.0000 1
B and D B B and D
C 0.6012 4 0.0575 2 1.0000 4
D 0.4996 2 0.0572 1 0.3016 2
 Y. Minatour et al./Scientia Iranica, Transactions A: Civil Engineering 22 (2015) 319{330 329

Engineers Company for providing the required infor- on urban water supply", European Journal of Opera-
mation and data. tional Research, 206(1), pp. 209-220 (2010).
15. Afshar, A., Mari~no, M.A., Saadatpour, M. and Afshar,
References A. \Fuzzy TOPSIS multi-criteria decision analysis
applied to Karun reservoirs system", Water Resources
1. Saaty, T.L., The Analytic Hierarchy Process, McGraw- Management, 25(2), pp. 545-563 (2011).
Hill, New York (1980).
16. Nitirach, S. and Vilas, N. \Strategic decision making
2. Van Laarhoven, P.J.M. and Pedrycz, W. \A fuzzy for urban water reuse application: A case from Thai-
extension of Saaty's priority theory", Fuzzy Sets and land", Desalination, 268(1-3), pp. 141-149 (2011).
Systems Journal, 11(1-3), pp. 229-241 (1983).
17. Opricovic, S. \Fuzzy VIKOR with an application
3. Buckley, J.J. \Fuzzy hierarchical analysis", Fuzzy Sets to water resources planning expert", Systems with
and Systems journal, 17(3), pp. 233-247 (1985). Applications, 38(10), pp. 12983-12990 (2011).
4. Chang, D.Y. \Applications of the extent analysis 18. Wei, L.H., Jing, L.Y., Wu, W. and Mou, L. \Research
method on fuzzy AHP", European Journal of Oper- on security assessment model of water supply system
ational Research, 95(3), pp. 649-655 (1996). based on leakage control", Procedia Environmental
5. Cheng, C.H. \Evaluating naval tactical missile systems Sciences, 11, pp. 749-756 (2011).
by fuzzy AHP based on the grade value of membership
function", European Journal of Operational Research, 19. Chen, Z., Ngo, H.H., Guo, W.S., Listowski, A.,
96(2), pp. 343-350 (1997). O'Halloran, K., Thompson, M. and. Muthukaruppan,
M. \Multi-criteria analysis towards the new end use
6. Deng, H. \Multicriteria analysis with fuzzy pairwise of recycled water for household laundry: A case study
comparison", International Journal of Approximate in Sydney", Science of the Total Environment, 438(1),
Reasoning, 21(3), pp. 215-231 (1999). pp. 59-65 (2012).
7. Chu, M.T., Shyu, J., Tzeng, G.H. and Khosla, R. 20. Panagopoulos, G.P., Bathrellos, G.D., Skilodimou,
\Comparison among three analytical methods for H.D. and. Martsouka, F.A. \Mapping urban water
knowledge communities group-decision analysis", Ex- demands using multi-criteria analysis and GIS", Water
pert Systems with Applications, 33(4), pp. 1011-1024 Resources Management, 26(5), pp. 1347-1363 (2012).
(2007).
21. Razavi, S.L., Toosi, J. and Samani, M.V. \Evaluating
8. Opricovic, S. \Multi-criteria optimization of civil en- water transfer projects using Analytic Network Process
gineering systems", Faculty of Civil Engineering, Bel- (ANP)", Water Resources Management, 26(7), pp.
grade (1998). 1999-2014 (2012).
9. Abrishamchi, A., Ebrahimian, A. and Tajrishi, M.
\Case study: Application of multi criteria decision 22. Zou, Q., Zhou, J., Zhou, C., Song, L. and Guo,
making to urban water supply", Journal of Water J. \Comprehensive ood risk assessment based on
Resources Planning and Management, ASCE, 131(4), set pair analysis-variable fuzzy sets model and fuzzy
pp. 326-335 (2005). AHP Stochastic", Environmental Research and Risk
Assessment, 27(2), pp. 525-546 (2013).
10. Wang, L., Wei, M., Huaicheng, G., Zhenxing, Z., Yong,
L. and Yingying, F. \An interval fuzzy multiobjective 23. Opricovic, S. and Tzeng, G.H. \The compromise
watershed management model for the lake Qionghai solution by MCDM methods: A comparative analysis
Watershed China", Water Resources Management, of VIKOR and TOPSIS", European Journal of Opera-
20(5), pp. 701-721 (2006). tional Research, 156(2), pp. 445-455 (2004).
11. Bojan, S. \Linking analytic hierarchy process and so- 24. Opricovic, S. and Tzeng, G.H. \Extended VIKOR
cial choice methods to support group decision-making method in comparison with outranking methods",
in water management", Decision Support Systems, European Journal of Operational Research, 178(2), pp.
42(4), pp. 2261-2273 (2007). 514-529 (2007).
12. Zarghami, M., Abrishamchi, A. and Ardakanian, R.
\Multi-criteria decision making for integrated urban Biographies
water management", Water Resources Management,
22(8), pp. 1017-1029 (2008). Yasser Minatour has received his BSc from Mining
13. Chung, E.S. and Lee, K.S. \Prioritization of wa- Engineering Department at Shahid Bahonar Kerman
ter management for sustainability using hydrologic University in September 2008. He nished his MSc
simulation model and multi criteria decision making at Razi University of Kermanshah, Iran, in March
techniques", Journal of Environmental Management, 2012. His research interests span a wide range of topics
90(3), pp. 1502-1511 (2009). including numerical modeling in water engineering, wa-
14. Kodikara, P.N., Perera, B.J.C. and Kularathna, ter reservoir management, evolutionary optimization,
M.D.U.P. \Stakeholder preference elicitation and mod- decision making, fuzzy logic and genetic algorithm
elling in multi-criteria decision analysis - A case study applications in mining.
330 Y. Minatour et al./Scientia Iranica, Transactions A: Civil Engineering 22 (2015) 319{330

Jahangir Khazaie has graduated as a civil engineer mining, mine management, operation research and
from Bu-Ali Sina University in September 1994. He optimization in mine operations, computer applications
nished MSc at Amikabir University of technology in mining, open pit design, decision making, fuzzy logic
in February 1997. He obtained his PhD degree in and genetic algorithm applications in mining. He is the
Numerical Analysis of Soil and Large Scale Foundation author of 8 books. Dr. Ataei has published 130 journal
Statically Interactions from Amirkabir University of papers and more than 150 conference papers. He is
technology in September 2008. Currently he is an currently holding 2 patents.
Assistant Professor at Razi University Kermanshah,
Iran. His research interests span a wide range of topics Akbar Javadi received his BSc degree in Civil Engi-
including nite element modeling, improvement soil, neering in 1989 and MSc degree in Hydraulic Structures
deep excavation, unsaturated soils, decision making, in 1991, both from Tabriz University, and his PhD
modeling and control of seawater intrusion and evo- in Geotechnical Engineering in 1998 from Bradford
lutionary optimization. The results of his research University in UK. He is currently a Professor of
have been published in over 35 papers in international Geotechnical Engineering and Head of the Computa-
journals, conference proceedings, book chapters and tional Geomechanics at the University of Exeter in
research reports. the UK. His research interests span a wide range of
topics including nite element modeling, unsaturated
Mohammad Ataei has received his BSc from Mining soils, ow and contaminant transport in saturated and
Engineering Department at Shahid Bahonar Univer- unsaturated soils, modeling and control of seawater
sity in September 1995, and his MSc from Amikabir intrusion, fracture mechanics, biomechanics, evolution-
University of Technology in August 1997. He obtained ary optimization, engineering applications of arti cial
his PhD degree in modeling of optimum cuto grade intelligence and data mining and storm water collec-
for multi-metal deposit from Amirkabir University of tion systems. The results of his research have been
Technology in July 2003. Currently he is a Professor published in over 230 papers in international journals,
at Shahrood University of Technology, Shahrood, Iran. conference proceedings, book chapters and research
His specialists are about open pit and underground reports.