ORIGINAL ARTICLE
Extended fuzzy analytic hierarchy process approach in waterand environmental management (case study: Lake Urmia Basin,Iran)
Ali Azarnivand • Farkhondeh Sadat Hashemi-Madani •
Mohammad Ebrahim Banihabib
Received: 16 January 2014 / Accepted: 26 May 2014 / Published online: 14 June 2014
� Springer-Verlag Berlin Heidelberg 2014
Abstract Recent researches reveal that many global
attempts have been made to protect water resources;
however, substantial environmental concerns have not yet
been sufficiently addressed. Lake Urmia in Iran is plagued
by natural and anthropogenic driving forces and its water
level has fallen by about 3 m below the minimum eco-
logical water level. To deal with the divergent interests and
multiple objectives associated with the lake’s water
resources, a multidisciplinary and flexible approach is then
required. The present research phases included: (1) deter-
mining the effective internal and external factors along
with formulating strategic alternatives for reviving the
lake’s water resources via strength–weakness–opportunity–
threat (SWOT–TOWS) matrix; (2) prioritizing the alter-
natives according to sustainable development criteria via
extended fuzzy analytic hierarchy process (FAHP) tech-
nique, and (3) applying sensitivity analysis to monitor the
robustness of the ranking. Representatives of the stake-
holders, managers and experts participated in the process of
decision making. To consider the different viewpoints and
the overall possibility distributions of fuzzy numbers, three
ranking procedures were developed through the extended
FAHP, reflecting neutral, optimistic, and pessimistic
viewpoints. According to the final prioritization, human
resources management and promotion of stakeholders’
participation stood superior to the other strategic
alternatives. In this framework, SWOT–TOWS analysis
performed as an appropriate prerequisite to formulate
practical strategies for supporting of a sustainable devel-
opment vision. The extended FAHP made a valid contri-
bution to the proposed framework and sensitivity analysis
of the results proved capability of the extended FAHP as a
robust tool for decision making in comprehensive water
problems.
Keywords Decision making � Fuzzy AHP � Lake Urmia �Stakeholders
Introduction
Crucial to human health and progress, food security, sus-
tainable development and to a protection of the environ-
ment, water resources management (WRM) constitutes an
area of policy-makers’ priority in formulating strategies on
the development (Singh et al. 2009). However, developing
countries are plagued by bad governance and unwise
allocation of natural resources (Cosgrove and Rijsberman
2000). Insufficient knowledge regarding the way aquifers,
rivers, lakes and dams function (Alley et al. 2002) along
with an overlooking of the capacity of water resources at
the time of land development planning (Mencio et al. 2010)
has resulted in substantial environmental crises associated
with water management.
In recent years, mean water level of Lake Urmia, a vast
hyper saline lake in north–west of Iran and one of the
largest Iranian Ramsar Sites, has descended to its lowest
level during the last century (Ghorbani-Aghdam et al.
2013). The lake is the main habitat for the endemic Iranian
brine shrimp, Artemia urmiana as the main food source of
host water birds (Karbassi et al. 2010). Increase in salinity
A. Azarnivand (&) � F. S. Hashemi-Madani � M. E. Banihabib
Department of Irrigation and Drainage Engineering,
College of Aburaihan, University of Tehran, Tehran, Iran
e-mail: [email protected]
F. S. Hashemi-Madani
e-mail: [email protected]
M. E. Banihabib
e-mail: [email protected]
123
Environ Earth Sci (2015) 73:13–26
DOI 10.1007/s12665-014-3391-6
from 170 up to 400 g/L (Zarghami 2011) is the root cause
of a severe decline of Artemia urmiana population in the
lake. Increasing salinity, water scarcity, land degradation,
biodiversity loss, a decrease of available water for the
development along with likely salt storms from the lake
constitute some series of environmental deteriorations that
would adversely affect agriculture sector, public health and
economic growth in the vast area of the region (Eimanifar
and Mohebbi 2007; Zarghami 2011; Hassanzadeh et al.
2012). Hassanzadeh et al. (2012) emphasized that overuse
of surface water resources is responsible for some 65 % of
the crisis, constructing dams has had 25 % effect on
decreasing the lake’s level, while about 10 % being related
to reduced precipitation. The continuous drop in mean
water level of Lake Urmia has started since 1996 (Ozyavas
and Khan 2012). The current water level of the lake has
fallen for about 3 m below its minimum ecological water
level (Department of Environment of Iran 2010). Under
such circumstances, the minimum annual ecological water
requirement of the lake would be 3.1 billion cubic meters
(Abbaspour et al. 2012).
The assessment as based upon the driving force–pres-
sure–state–impact–response (DPSIR) sustainability frame-
work, demonstrated that without a consideration of the
ethical, cultural and institutional indicators and as well
stakeholders’ engagement, Integrated Water Resources
Management (IWRM) plans cannot be successfully
implemented within the basin (Hashemi et al. 2010). Thus,
the demands and visions of stakeholders from different
socioeconomic backgrounds must be reflected in the
inclusive plans (D’Souza and Nagendra 2011). To hinder
the environmental deteriorations and boost public partici-
pation, an application of the strategic framework at the
regional level would be a serious action that must be taken
(Bryan et al. 2010). However, the stakeholders differ in
values, goals and socioeconomic interests (Ganoulis et al.
2008) and WRM must consider the environmental, eco-
nomic and social criteria simultaneously (Yilmaz and
Harmancioglu 2010). The complexity of WRM lies in the
root of these divergent interests and multiple objectives. As
a result, it is difficult to make a recommendation that fits all
the contexts and solves all the complex water management
and planning problems. In an addressing of the issue, the
pivotal role of prioritization approaches is highlighted.
Nowadays, utilization of multidisciplinary approaches,
particularly in developing countries where improving liv-
ing standards and human development are challenging
issues, is highly recommended (Garfi et al. 2011). Multi-
Criteria Decision Making (MCDM) models are popular
among researchers due to their capabilities in: (1) dealing
with a limitation of water, financial and human resources;
(2) considering the combination of multiple criteria; (3)
resolving conflict among the stakeholders, and (4)
simplifying the administration of the projects (Zarghami
and Szidarovszky 2011). In comparisons of techniques,
different MCDM tools can be used to determine the criteria
weights and evaluate the overall scores of alternatives
(Roman et al. 2004). Although there are no such better or
worse models, some models better suit to particular deci-
sion problems than others do (Mergias et al. 2007). The
ability to handle qualitative criteria, applicability to the
case of group decision making and synthesizing different
points of view, capacity to handle various criteria and
alternatives, low requirements on time, and consistency
and robustness of results are some vital requirements for
choosing proper MCDM model (MacCrimmon 1973).
Hence, before selecting the MCDM model, decision mak-
ers must understand the problem, the feasible alternatives,
different outcomes, conflicts among the criteria, and the
level of the data uncertainty (Salminen et al. 1998). In this
research, prioritization of strategic alternatives is a com-
plex decision making problem containing subjectivity,
uncertainty and ambiguity throughout the assessment pro-
cess. The analytic hierarchy process (AHP) developed by
Saaty is a mathematical technique of converting linguistic
assessments to a set of weights by making pairwise com-
parisons among the criteria into a hierarchical structure
(Saaty 1980). AHP allows the decision makers to incor-
porate judgments on tangible and intangible qualitative
criteria, and also provides a mechanism for analysts to
check the consistency of the results (Badri 2001). More-
over, AHP is well suited in group decision making (Lai
et al. 2002) and can accommodate both tangible and
intangible criteria, individual and shared values in the
group decision making process (Dyer and Forman 1992).
There is a large volume of published studies describing the
role of AHP in such different water and environmental
fields as water quality (Martin-Ortega and Berbel 2010),
river basin planning (Calizaya et al. 2010), environmental
assessment of water programs (Garfi et al. 2011), IWRM
(Gallego-Ayala and Juızo 2011), waste water treatment
(Pires et al. 2011), irrigation (Gallego-Ayala 2012), urban
water demands (Panagopoulos et al. 2012), assessment of
waste dumps on soil and water (Adibee et al. 2013) and
groundwater vulnerability (Sener and Davraz 2013).
On the other hand, the limited and unbalanced scale of
judgment in conventional (crisp) AHP cannot fully con-
sider the inherent uncertainty and ambiguity associated
with respondents’ judgments (Yang and Chen 2004). To
overcome the shortcoming, decision makers have found
interval judgments as more accurate than the fixed value
judgments (Nazari et al. 2012). In this regard, an integra-
tion of the fuzzy set theory with AHP has been recom-
mended by some scholars (Van Laarhoven and Pedrycz
1983; Buckley 1985; Chang 1996). The fuzzy set theory
makes it possible to incorporate the unquantifiable,
14 Environ Earth Sci (2015) 73:13–26
123
incomplete and non-obtainable information into the pro-
cess of decision making (Kulak et al. 2005). Fuzzy MCDM
and pairwise comparison play central roles in water policy
assessment, boosting of infrastructure and as well in stra-
tegic planning and management (Hajkowicz and Collins
2007). There have been lots of studies in the literature
using fuzzy analytic hierarchy process (FAHP) for the
solution of such water and environmental problems as
assessment of water management plans (Srdjevic and
Medeiros 2008), geo-environmental impact assessment
(Huang et al. 2012), flood risk evaluation (Yang et al.
2013), and groundwater pollution assessment (Aryafar
et al. 2013).
Therefore, this paper is aimed at prioritizing the strate-
gic alternatives for reviving the lake’s water resources
through a MCDM model, on the basis of strategies’ capa-
bility of satisfying sustainable development criteria. To
structure a democratic process, direct involvement of
groups of stakeholders, experts and managers is high-
lighted and weights of criteria and overall ranks of alter-
natives are evaluated as based upon respondents’ votes.
The first step would be environmental scanning and an
identification of the internal strategic factors vs. external
ones in a group decision making process. With constructing
strengths–weaknesses–opportunities–threats (SWOT–
TOWS) matrix, the fundamental internal and external
factors are determined and the alternatives containing
managerially and structurally based strategies formulated
by matching the internal strengths and weaknesses as
against external opportunities and threats. Then, the
appropriate evaluation criteria are selected based upon
compatibility with operational circumstances of the basin
with their weights evaluated through pairwise comparisons
of FAHP technique. To prioritize the strategic alternatives,
extended FAHP is employed to develop three ranking
procedures, reflecting neutral, optimistic, and pessimistic
viewpoints. Finally, sensitivity analysis is applied to
monitor the robustness of the end ranking.
Materials and methods
Case study
Located within the domains of the provinces of East and
West Azerbaijan and Kurdistan, Lake Urmia Basin covers
an area of 51,440 km2 (Ghaheri et al. 1999). The area lies
approximately between 37�400–39�290N latitude and
44�130–47�530E longitudes (Ghorbani-Aghdam et al.
2013), (Fig. 1). Lake Urmia as a National Park and its
important brackish and freshwater satellite wetlands are
recognized by UNESCO as a Biosphere Reserve (UNEP
and GEAS 2012). Average precipitation amounts to
341 mm (Djamali et al. 2008) with the mean annual tem-
perature varying between 6.5 to 13.5 �C (Department of
Environment of Iran 2010). Delju et al. (2013) analyzed
climate variability and change in the basin for the period
1964–2005. They suggested that mean precipitation had
decreased by 9.2 %, while the average maximum temper-
ature increased by 0.8 �C over the last four decades. The
average annual inflow into the lake is estimated at 5,300
million cubic meters. The largest river emptying into the
basin is Zarinneh Rood (Ghaheri et al. 1999), and apart
from the 14 rivers with permanent flows and a number of
waterways with seasonal flows; the lake receives water
from groundwater seepage flows and also from direct
precipitation over the lake (Department of Environment of
Iran 2010).
Strategic management framework
Strategic management is a process, built up of three major
components: (1) strategy formulation to determine the
future trend of the system; (2) strategy implementation for
designing the system’s structure, and (3) strategic evalua-
tion including evaluation and review of the system’s pro-
gress (Danaee Fard et al. 2011). Although a prediction of
the future is close to impossible, the range of possible
change and likely future events can be anticipated from the
strategic planning and management context [United Nation
(UN) 2004].
Group decision making is an effective approach for an
implementation of good governance in water disciplines
(Zarghami 2011). Hence, a panel of experts was gathered
to determine the key strategic factors and to develop the
strategies for the basin. All the ten members of panel of
experts are PhD holders either in water resources engi-
neering or natural resources management. They had been
invited from the Iranian academic and research institutions
for their educational and operational backgrounds in deal-
ing with challenges of Lake Urmia Basin. According to
Iran’s Department of Environment (2010), the vision
toward Lake Urmia is: ‘‘Lake Urmia will have adequate
water to sustain an attractive landscape and rich biodi-
versity where people and local communities can make wise
use of its resources, and will enhance cooperation between
the involved provincial organizations’’. The next step
would be a distinction between internal vs. external envi-
ronment. The realm is shared among east and west Azer-
baijan plus Kurdistan provinces. The factors, not in the
authority of the basin management are assumed as the
external factors. In other words, the factors that can be
controlled by basin management are considered as internal,
while those that can affect basin management but man-
agement cannot have influence on them, are classified as
the external ones.
Environ Earth Sci (2015) 73:13–26 15
123
SWOT–TOWS matrix
SWOT–TOWS matrix is a practical matching tool for
determination of the effective internal vs. external factors
of a system, creating ideal solutions and implementing
strategic management. The fundamental factors are derived
from large numbers of responses in a group decision
making process. Strategies are formulated through sys-
tematic analysis of the feasible connections between
SWOT factors in the conceptual TOWS framework. In
fact, matching the internal factors as against the external
ones formulates four groups of strategies (Weihrich 1982):
1. SO: these Maxi–Maxi strategies use the internal
strengths to take advantage of external opportunities.
2. ST: these Maxi–Mini strategies avoid impact of the
external threats by applying the internal strengths.
3. WO: these Mini–Maxi strategies aim at eliminating
internal weaknesses by an exploitation of the external
opportunities.
4. WT: these Mini–Mini strategies are defensive tactics
directed at minimizing the internal weaknesses, while
avoiding the external threats.
Although SWOT–TOWS is a helpful tool for defining
and structuring the problem and an exploration of the
present strategic position of the system, it does not reveal
any information concerning the importance of the factors.
Hence, this analysis can bear a degree of subjectivity that
may affect the formulation of the strategic plan (Gallego-
Ayala and Juızo 2011). Since a sole utilization of SWOT–
TOWS method is not sufficient, a different approach is
applied to find the most conclusive strategy. In present
research, sustainable development is the concept that links
strategic management and group MCDM together.
Assessment of TOWS-based alternatives via FAHP method
with regard to the concept of sustainable development
would enable the policy makers to draw an ideal context
for a determination of the most attractive strategy.
Sustainable development criteria
Numerous sustainable development criteria have been clas-
sified in general (economic, social, and environmental) and
technical/comprehensive categories (Zarghami and Szidar-
ovszky 2011). Appropriate criteria in water programs are
often selected based upon the compatibility with operational
circumstances of the study areas (Garfi et al. 2011). To
mention a few, the criteria in some researches as state-of-art-
review for ranking of water resources projects in Iran (Ar-
dakanian and Zarghami 2004; Zarghami and Szidarovszky
2011), sustainable agricultural development of Iran (Rezaei-
Moghaddam and Karami 2008), evaluation of strategic
urban water reuse alternatives in Thailand (Sa-nguanduan
and Nititvattananon 2011), water shortage mitigation policy
making in South Korea (Choi et al. 2012) and an assessment
of non-conventional water supply alternatives in Barcelona
(Domenech et al. 2013) are hereby presented (Table 1).
Throughout the present research, and due to the unfavorable
Fig. 1 Map of the study area
16 Environ Earth Sci (2015) 73:13–26
123
circumstances of the basin, a conjunction of the sustainable
development paradigm and the current strategic condition is
highlighted. On this basis, the sustainable development cri-
teria are selected by considering the effective strategic fac-
tors in SWOT framework.
Determination of criteria weights in FAHP
The following scheme expresses the steps of MCDM process
throughout the research, as follows: defining feasible alter-
natives, setting the evaluation criteria and their weights,
evaluation of the alternatives, and finally, performing the
sensitivity analysis to analyze the experimental results.
Zadeh (1965), Van Laarhoven and Pedrycz (1983),
Buckley (1985) and Kaufmann and Gupta (1985) devel-
oped different fuzzy definitions. Buckley method is hereby
applied as based on the fact that, fuzzy weights for each
fuzzy matrix are determined through the geometric mean
method that can simplify the calculations. In addition,
Buckley (1985) proved:
Considering ~A1 ¼ ~aij
h i; where ~aij ¼ ðaij; bij; cij; dijÞ and
let bij� ~aij� dij for all ij; if A1 ¼ aij
h iis consistent, then
~A1 ¼ ~aij
h iis consistent too. To check the consistency of
the comparison matrix, the consistency index (CI) can be
calculated through the following formula (Saaty 1980):
Table 1 Various criteria used for the evaluation of alternatives in
water and environmental problems (the same numbers refer to the
criteria suggested by the researchers)
Economic criteria
Priority of usages (1 & 3)
Benefit minus cost (1 & 3)
Benefit/cost ratio (1 & 3)
Extent of investments (1 & 3)
Risk of investments (1 & 3)
Development and improvement ratio in agricultural area (1 & 3)
Base for supplementary projects (1 & 3)
Diversification of financial resources (1 & 3)
Level of construction technology (1 & 3)
Capabilities of phased operation (1 & 3)
Simplicity of operation and maintenance (1 & 3)
Level of studying phases (1 & 3)
Productivity (2)
Profitability (2)
Investment cost (4)
Economic benefit (4)
Construction costs (5)
Estimated damage(5)
Capital cost (6)
Operation and maintenance cost (6)
Environmental criteria
Consistency with climate (1 & 3)
Less damages to ancient and cultural heritage (1 & 3)
Range of environmental impacts (1 & 3)
Studies of watershed conservation (1 & 3)
Studies on supply and demand management (1 & 3)
Environmental protection (2)
Wise use of resources (2)
Product quality (2)
Water consumption demand and supply in next 20 years (4)
Water consumption demand and supply in present (4)
Environmental impact (4 & 6)
Sustainability (5)
Surface water quality (5)
Energy consumption (6)
Social criteria
Employment and migration (1 & 3)
Public participation (1, 2 & 3)
Social equity (1, 2 & 3)
Recreation, tourism and additional facilities (1 & 3)
Social casualties and damages of dam project (1 & 3)
Natural disasters management ‘‘Flood and Drought’’ (1 & 3)
More settlement in border regions (1 & 3)
Priority of shared waters (1 & 3)
Reducing the conflicts among stakeholders (1 & 3)
Health impacts (4 & 6)
Public acceptance (4 & 6)
Table 1 continued
Water shortage duration (5)
Employment increase (5)
Technical criteria
Consistency with policies (1 & 3)
Consistency with logistic plan (1 & 3)
Impacts on other projects (1 & 3)
Management capacities in basin (1 & 3)
Comprehensive study in basins (1 & 3)
Quality of effluent (4)
Quantity of effluent (4)
Reliability of wastewater treatment operator(4)
Regulation/policy/support from central government (4)
Institutional cooperation (4)
Technological simplicity (6)
Reliable water supply (6)
Researchers
(1): Ardakanian and Zarghami (2004)
(2): Rezaei-Moghaddam and Karami (2008)
(3): Zarghami and Szidarovszky (2011)
(4): Sa-nguanduan and Nititvattananon (2011)
(5): Choi et al. (2012)
(6): Domenech et al. (2013)
Environ Earth Sci (2015) 73:13–26 17
123
CI ¼ kmax � n
n� 1; ð1Þ
where kmax is the maximum eigenvalue that can be
obtained from the priority matrix. The Consistency Ratio
(CR) illustrates the judgment concerning consistency. In
the following formula, (RI) is a Random Index set for a
randomly generated n� n matrix. The results will be
accepted if CR is less than or equal to 10 %.
CR ¼ CI
RI
� �� 100 ð2Þ
Although Buckley (1985) employed trapezoidal mem-
bership functions for fuzzifying the comparison ratios, by
accordingly selecting the parameters of membership func-
tion, trapezoidal shape can be converted to triangular. The
triangular fuzzy numbers (TFNs) are indicated as~A ¼ ða; b; cÞ, where a and c are the lower and upper bounds
of the fuzzy number and b is the midpoint (Fig. 2). A (TFN)
is defined by its basic particulars as follows (Chang 1996):
lAðxÞ ¼
x� a
b� afor a� x� b
c� x
c� bfor b� x� c
0 Otherwise
8>>><>>>:
ð3Þ
Considering two TFNs ~A1 ¼ ða1; b1; c1Þ and
~A2 ¼ ða2; b2; c2Þ, their operational laws (addition, subtrac-
tion and multiplication) would be as follows (Kaufmann
and Gupta 1985):
~A1 � ~A2 ¼ a1 þ a2; b1 þ b2; c1 þ c2ð Þ ð4Þ~A1H~A2 ¼ a1 � c2; b1 þ b2; c1 � a2ð Þ ð5Þ
~A1 � ~A2 ¼ a1 � a2; b1 � b2; c1 � c2ð Þ ð6Þ
Linguistic variables (Table 2) are variables whose val-
ues are represented by either words or sentences in a nat-
ural or artificial language (Fig. 3).
The first step would be the construction of pairwise
comparison matrices among all the criteria, within the
dimensions of the hierarchy system.
A1 ¼
1 ~a12 . . . ~a1n
1=~a12 1 . . . ~a2n
. . . . . . . ..
. . .1=~a1n 1=~a2n . . . 1
26664
37775 ð7Þ
The aggregated fuzzy judgment matrix (AFJM) for the
criteria is computed from the geometric mean evaluation of
the decision-makers’ individual fuzzy judgment matrices
(IFJM) as follows:
y
x
a b c0
1
Fig. 2 A triangular fuzzy number ~A
Table 2 Membership functions of linguistic scale
Fuzzy
number
Linguistic scales to obtain
importance
Membership
function
ðu ¼ e ¼ 1Þ
1 Equal importance (1, 1, 1)
2 Between equal and weak importance (1, 2, 3)
3 Weak importance (2, 3, 4)
4 Between weak and strong importance (3, 4, 5)
5 Strong importance (4, 5, 6)
6 Between strong and very strong
importance
(5, 6, 7)
7 Very strong importance (6, 7, 8)
8 Between very strong and absolute
importance
(7, 8, 9)
9 Absolute importance (8, 9, 10)
E
2 6 109
1
4 831 5 7
W S VS A
Fig. 3 Membership functions of linguistic values for criteria rating
18 Environ Earth Sci (2015) 73:13–26
123
~Aij ¼Ymk¼1
akij
!1m
;Ymk¼1
bkij
!1m
;Ymk¼1
ckij
!1m
0@
1A
8~Akij ¼ ak
ij; bkij; c
kij
� �For i; j ¼ 1; . . .; n and k ¼ 1; . . .;m;
ð8Þ
where ~Akij is a fuzzy number that represents the vote of
particular decision maker and k ¼ k1; k2; . . .; kmf g the set
of decision makers.
For the next step, consider ~A1 ¼ ðaij; bij; cijÞ and i; j ¼1; . . .; n, then:
atij ¼
Yn
j¼1
aij
!1n
; btij ¼
Yn
j¼1
bij
!1n
and ctij ¼
Yn
j¼1
cij
!1n
ð9Þ
at ¼Xn
i¼1
atij; bt ¼
Xn
i¼1
btij and ct ¼
Xn
i¼1
ctij ð10Þ
The criteria weights can be calculated from the equation
below:
~Wi ¼at
ij
ct;bt
ij
bt;ct
ij
at
� �ð11Þ
Determination of alternatives’ weights
The AFJM for the alternatives under each criterion is computed
from the IFJM suggested by decision makers. Later, ~rij, the
fuzzy weights of alternative j to criterion i, should be evaluated
through the three formulas of (9)–(11). In these formulas, TFNs
of alternatives should be alternated and the fuzzy utility cal-
culated through formula (12) (Bonissone 1982):
~Uj ¼Xn
j¼1
~wi � ~rij ð12Þ
Since prioritizing is in demand of quantifiable results,
defuzzification should be adopted. Considering two trape-
zoidal fuzzy numbers ~A1 ¼ ða1; b1; c1; d1Þ and ~A2 ¼ða2; b2; c2; d2Þ, their operational laws would be as follows
(Bonissone 1982):
~A1 � ~A2 ¼ ða1 þ a2; b1 þ b2; c1 þ c2; d1 þ d2Þ ð13Þ~Q¼ ~A1� ~A2 ¼ a1a2 L1;L2½ �b1b2;c1c2;d1d2 R1;R2½ �ð Þ ð14Þ
where L1, L2, R1 and R2 can be calculated from the fol-
lowing formulas:
L1 ¼ ðb1 � a1Þðb2 � a2Þ ð15ÞL2 ¼ a2 b1 � a1ð Þ þ a1 b2 � a2ð Þ ð16ÞR1 ¼ d1 � c1ð Þ d2 � c2ð Þ ð17ÞR2 ¼ � d2ðd1 � c1Þ þ d1ðd2 � c2Þ½ � ð18Þ
To convert trapezoidal numbers to TFNs, terms b1 ¼ c1
and b2 ¼ c2 should be assumed. If ~Q0 ¼ ~Q1 þ ~Q2, then the
membership function would be defined as:
l ~Q0ðxÞ ¼
0 If x� ða1 þ a2Þ or x ðd1 þ d2Þ1 If ðb1 þ b2Þ � x� ðc1 þ c2Þa 2 0; 1½ � If ða1 þ a2Þ � x� ðb1 þ b2Þa 2 0; 1½ � If ðc1 þ c2Þ � x� ðd1 þ d2Þ
8>>><>>>:
ð19Þ
When ða1 þ a2Þ� x�ðb1 þ b2Þ, xi would be:
xi ¼ Li1a2þLi2aþ ai for i ¼ 1; 2 ð20Þ
As a result, x can be calculated from the equation below:
x ¼ L11 þ L21ð Þa2þ L12 þ L22ð Þaþ a1þa2ð Þ ð21Þ
Similarly, when ðc1 þ c2Þ� x�ðd1 þ d2Þ, the equation
below would be used:
x ¼ R11 þ R21ð Þa2þ R12 þ R22ð Þaþ d1þd2ð Þ ð22Þ
Prioritization of the alternatives
Center of gravity (COG) is an appropriate defuzzification
operator that computes the COG of the area under the
membership function. COG can be calculated as follows
(Broekhoven and Baets 2006):
Ev~U� ¼R d
ax � l ~UðxÞdxR d
al ~UðxÞdx
ð23Þ
where Evð ~UÞ is a non-fuzzy value of ~U and l ~UðxÞ the
membership function of ~U.
This formula reflects a general/neutral viewpoint. To
consider the overall possibility distributions of fuzzy num-
bers; Lee-Kwang and Jee-Hyong (1999) suggested the fol-
lowing two ranking formulas, reflecting optimistic vs.
pessimistic viewpoints in evaluating the alternatives’ values:
Ev optimistic~U� ¼R d
ax2 � l ~UðxÞdxR d
al ~UðxÞdx
ð24Þ
Ev pessimistic~U� ¼R d
að2x� x2Þ � l ~UðxÞdxR d
al ~UðxÞdx
ð25Þ
Results and discussion
TOWS strategies and evaluation criteria
According to the environmental scanning, the panel of
experts obtained 26 relevant factors to the strategic man-
agement in the Lake Urmia Basin (Table 3). The strengths
(four factors) are categorized in the natural, organizational
and educational groups. Weaknesses (13 factors) are
derived from mismanagement and unjustified human
Environ Earth Sci (2015) 73:13–26 19
123
Ta
ble
3S
WO
Tm
atri
x
Inte
rnal
(str
eng
ths)
Inte
rnal
(wea
kn
esse
s)
Un
iver
siti
esan
dre
sear
chin
stit
ute
sas
clo
ud
seed
ing
cen
ter
(S1
)
Ex
iste
nce
of
inte
gra
ted
man
agem
ent
pla
nfo
rL
ake
Urm
iaB
asin
(S2
)
Imp
oss
ibil
ity
of
allo
cati
ng
the
salt
yla
ke
wat
erfo
rag
ricu
ltu
ral
wat
ersu
pp
ly(S
3)
En
vir
on
men
tal,
com
mer
cial
,in
du
stri
al,
com
mu
nic
atio
n
and
eco
tou
rism
po
ten
tial
s(S
4)
Imp
rop
ersu
per
vis
ion
on
surf
ace
wat
er’s
allo
cati
on
asw
ell
on
ov
er-e
xp
loit
atio
no
fth
eg
rou
nd
wat
erre
sou
rces
(W1
)
Wea
kH
yd
ro—
clim
ato
log
ym
on
ito
rin
gsy
stem
sal
on
gw
ith
sho
rtag
eo
fb
asic
dat
a(W
2)
Rap
idch
ang
esin
pro
vin
cial
wat
erm
anag
emen
t(W
3)
Inex
iste
nce
of
op
tim
alcr
op
pin
gp
atte
rnad
apte
dto
the
chan
gin
gcl
imat
ean
dw
ater
reso
urc
es(W
4)
Lan
du
sech
ang
e,d
egra
ded
pas
ture
san
der
osi
on
(W5
)
To
rece
de
the
lak
ean
dcr
eati
on
of
the
salt
mar
sh(W
6)
Inex
iste
nce
of
dia
lect
icco
nn
ecti
on
,co
nsu
ltat
ion
and
coo
rdin
atio
nam
on
gth
est
akeh
old
ers,
NG
Os,
acad
emic
cen
ters
and
go
ver
nm
enta
lo
rgan
izat
ion
s(W
7)
Lac
ko
fso
cial
lear
nin
gan
dp
ub
lic
edu
cati
on
con
cern
ing
effi
cien
tw
ater
use
pat
tern
s(W
8)
Sh
ort
age
of
was
tew
ater
coll
ecti
on
and
trea
tmen
tsy
stem
sin
urb
anan
dru
ral
area
s(W
9)
Ou
to
fd
ate
wat
erd
istr
ibu
tio
nn
etw
ork
sin
urb
anan
dru
ral
area
s(W
10
)
Inad
equ
acy
of
pre
ssu
rize
dir
rig
atio
nsy
stem
san
dm
od
ern
farm
mec
han
izat
ion
(W1
1)
Lac
ko
fm
ain
ten
ance
of
the
irri
gat
ion
chan
nel
s(W
12
)
Dam
san
da
hig
hw
ayco
nst
ruct
edw
ith
ou
tco
nsi
der
ing
of
the
env
iro
nm
enta
lw
ater
rig
hts
and
asw
ell
the
lak
e’s
hy
dro
dy
nam
ic(W
13
)
Ex
tern
al(o
pp
ort
un
itie
s)E
xte
rnal
(th
reat
s)
Th
ep
oss
ibil
ity
of
uti
lizi
ng
ren
ewab
leen
erg
ies
and
mo
der
nte
chn
olo
gy
(O1
)
Wat
erre
sou
rces
leg
isla
tio
ns
and
reg
ula
tio
ns
(O2
)
Fu
nd
sb
yg
ov
ern
men
t(O
3)
Wat
erre
sou
rces
inad
jace
nt
bas
ins
(O4
)
Gro
win
gin
tern
atio
nal
,n
atio
nal
and
loca
lco
nce
rns
ov
erth
ed
ryin
gu
po
fth
ela
ke
(O5
)
Inap
pro
pri
ate
spat
ial
and
tem
po
ral
dis
trib
uti
on
of
pre
cip
itat
ion
(T1
)
Dir
eim
pac
tso
fcl
imat
ech
ang
ean
dd
rou
gh
t(T
2)
Mar
ked
flu
ctu
atio
ns
inth
en
atio
nal
eco
no
my
alo
ng
wit
hin
exis
ten
ceo
f
inv
estm
ent
secu
rity
for
pri
vat
ese
cto
r(T
3)
Lac
ko
fd
yn
amic
con
nec
tio
nb
etw
een
exp
erts
and
leg
isla
tors
(T4
)
20 Environ Earth Sci (2015) 73:13–26
123
intervention in nature. Opportunities (five factors) are
related to facilities and budget allocation by the govern-
ment, public concerns as well as modern technology.
Finally, threats (four factors) are caused by climate change,
economic crisis and institutional conflicts. Based upon a
systematic review of several literature reports, the causes of
environmental crisis in the Lake Urmia are mainly mana-
gerially and structurally based issues (Garousi et al. 2013).
As a result, the strategic alternatives of the lake should
include both categories. The formulated strategies of
present research are summarized in Table 4.
As stated earlier, the strategic factors are effective in the
process of choosing sustainable development criteria. On
this basis, six criteria are as follows:
C1: resistance against economic fluctuations (eco-
nomic), C2: acceptability by stakeholders and desirability
of participation (social), C3: natural resources conservation
(environmental), C4: effectiveness in water supplying or
conservation (technical), C5: operational feasibility (tech-
nical) and C6: flexibility in long-term and short-term
changes (technical).
FAHP results and discussions
A questionnaire was prepared to determine the relative
weights of each criterion and of each alternative by use of
pairwise comparisons. Throughout the ongoing research,
more than 50 decision makers constituted the participants,
while a total of 38 valid and consistent questionnaires
being applied. The decision makers were chosen from the
representatives of local stakeholders, environmental
activists, water managers, university teachers and academic
researchers.
The AFJM of criteria as well as final TFNs of alterna-
tives under each criterion are presented in Table 5. The
consistency ratio of AFJM for the criteria amounts to 0.01.
The aggregated fuzzy weights (AFW) of sustainable
development criteria and consistency ratio of decision
matrices of alternatives under each criterion are also shown
in Table 5.
Prior to calculating the values of alternatives, the
required components should be determined (Table 6). As
noted earlier, three neutral, optimistic, and pessimistic
formulas (23–25) are applied to calculate the non-fuzzy
value for each alternative. The evaluated components are
alternated in the formulas below:
~Ui !
Ev neutralðAÞ ¼R b
axðL1x2 þ L2xþ aÞ dxþ
R d
cxðR1x2 þ R2xþ dÞ dxR b
aðL1x2 þ L2xþ aÞ dxþ
R d
cðR1x2 þ R2xþ dÞ dx
Ev optimisticðAÞ ¼R b
ax2ðL1x2 þ L2xþ aÞ dxþ
R d
cx2ðR1x2 þ R2xþ dÞ dxR b
aðL1x2 þ L2xþ aÞ dxþ
R d
cðR1x2 þ R2xþ dÞ dx
Ev pessimisticðAÞ
¼R b
að2x� x2ÞðL1x2 þ L2xþ aÞ dxþ
R d
cð2x� x2ÞðR1x2 þ R2xþ dÞ dxR b
aðL1x2 þ L2xþ aÞ dxþ
R d
cðR1x2 þ R2xþ dÞ dx
8>>>>>>>>>>>>>><>>>>>>>>>>>>>>:
The results related to three neutral, optimistic, and
pessimistic viewpoints are presented in Table 7. Apart
from the fact that all these aspects produced similar rank-
ings, the most striking results to emerge from the data are
as follows:
Table 4 TOWS matrix
SO WO
A1: inter-basin water transfer with an addition of the financial and legal
supports and as well employing of the operational and academic
skills (S1-O2, 3, 4)
A2: cloud seeding with an addition of the financial and legal supports
and as well employing of the operational and academic skills
(S1, 2-O2, 3)
A4: promotion, establishment, and institutionalization of integrated
system of operation, protection, monitoring and maintenance of soil
and water resources of the basin, with an addition of the financial and
legal supports, public concern and new technologies
(W1, 2, 5-O1, 2, 3, 5)
A5: promoting stakeholders’ participation in the process of training,
planning and implementation of efficient water use pattern with an
empowerment and developing of NGOs (W7, 8, 10-O5)
A6: implementation of optimal cropping pattern according to the
regional climate with employing financial and legal supports and as
well guiding of the public concerns (W4-O2, 3, 5)
A7: improvement of the efficiency and reduction of water losses in
irrigation and water distribution systems with employing financial
and legal supports and as well proper use of facilities
(W9, 10, 11, 12-O2, 3)
ST WT
A3: spatial planning with regard to environmental,
commercial and industrial potentials to overcome security,
economic, political, social and environmental conflicts (S4-T3)
A8: optimization of water resources allocation
with considering the lake’s ecological capacity to implement
climate change adaptive programs (W13-T2)
Environ Earth Sci (2015) 73:13–26 21
123
Promoting stakeholders’ participation in the process of
training, planning and implementation of efficient water use
pattern with an empowerment and developing of NGOs (A5)
is by far the most demanding strategy among all the for-
mulated strategies. The research of Abdullaev and Rak-
hmatullaev (2013) has demonstrated that the significant
difference between current approaches of WRM in the
Central Asia and former Soviet Communist period lies in the
participation and collaboration of stakeholders into the
processes of planning, implementation, monitoring and
decision making in WRM. Due to their research work, sus-
tainable water management is highly dependent upon the
role of water users. It is worth noting that, without an initi-
ation of social learning among responsible authorities,
NGOs and stakeholders, moving toward structuring the
involvement process is difficult. Social learning in WRM
requires the right level of expectations, considerable finan-
cial resources and a high level of time commitment (Muro
and Jeffrey 2012). The extensive, systematic and structured
participation is of a great benefit to modern water resource
governance (Lennox et al. 2011). The high rank of this
strategy indicates that the crucial role of human resources
management has been understood by the respondents.
Due to population growth, limitation of water resources,
and huge costs of water supply, efficient management and
operation of available water resources must be highlighted
(Bozorg Haddad and Marino 2007). The orientation of 2nd
to 6th ranked strategies is managerially based, with their
targets of supply and/or conservation of water resources
through:
1. Adjusting water and soil resources with the basin’s
potentials.
2. Empowering the surface and ground water
supervising.
3. Control of supply and demand relationship in agricul-
tural fields.
4. Optimization of releases from dams.
Furthermore, there is very little difference observed
among the values of these alternatives.Ta
ble
5T
he
agg
reg
ated
fuzz
yju
dg
men
tm
atri
xfo
rth
ecr
iter
iaan
dfi
nal
asse
ssm
ent
of
the
alte
rnat
ives
un
der
each
crit
erio
n
C1
C2
C3
C4
C5
C6
C1
(1,
1,
1)
(0.6
7,
0.8
9,
1.1
2)
(0.4
2,
0.4
9,
0.6
0)
(0.4
3,
0.5
0,
0.6
1)
(0.5
2,
0.5
8,
0.6
7)
(1.2
2,
1.3
7,
1.4
9)
C2
(0.8
9,
1.1
2,
1.4
9)
(1,
1,
1)
(0.4
2,
0.4
9,
0.6
0)
(0.5
0,
0.6
2,
0.8
2)
(0.6
7,
0.7
3,
0.8
2)
(1.2
2,
1.6
7,
2.0
3)
C3
(1.6
7,
2.0
3,
2.3
5)
(1.6
7,
2.0
3,
2.3
5)
(1,
1,
1)
(1.0
2,
1.5
5,
2.1
7)
(1.1
2,
1.4
9,
1.9
3)
(2.3
5,
3.1
3,
3.8
5)
C4
(1.8
1,
2.0
2,
2.1
2)
(1.2
2,
1.6
0,
2.0
0)
(0.4
6,
0.6
5,
0.9
8)
(1,
1,
1)
(0.8
2,
1.0
4,
1.2
4)
(1.3
5,
2.4
2,
3.4
5)
C5
(1.4
9,
1.7
2,
1.9
2)
(1.2
2,
1.3
7,
1.4
9)
(0.5
2,
0.6
7,
0.8
9)
(0.8
1,
0.9
6,
1.2
2)
(1,
1,
1)
(1.4
9,
2.1
0,
2.6
3)
C6
(0.6
7,
0.7
3,
0.8
2)
(0.4
9,
0.6
0,
0.8
2)
(0.2
6,
0.3
2,
0.4
2)
(0.2
9,
0.4
1,
0.7
4)
(0.3
8,
0.4
8,
0.6
7)
(1,
1,
1)
AF
W(0
.08
,0.1
2,
0.1
6)
(0.0
9,
0.1
3,
0.2
0)
(0.1
8,
0.2
7,
0.4
0)
(0.1
3,
0.2
0,
0.3
0)
(0.1
3,
0.1
9,
0.2
7)
(0.0
6,
0.0
8,
0.1
4)
C1
C2
C3
C4
C5
C6
A1
(0.0
23,
0.0
32,
0.0
40)
(0.0
45,
0.0
66,
0.0
86)
(0.0
23,
0.0
29,
0.0
35)
(0.0
39,
0.0
53,
0.0
69)
(0.0
62,
0.0
86,
0.1
17)
(0.0
25,
0.0
35,
0.0
42)
A2
(0.0
33,
0.0
43,
0.0
51)
(0.0
38,
0.0
57,
0.0
72)
(0.0
35,
0.0
42,
0.0
49)
(0.0
27,
0.0
38,
0.0
48)
(0.0
81,
0.1
08,
0.1
41)
(0.0
31,
0.0
41,
0.0
49)
A3
(0.1
40,
0.1
62,
0.1
88)
(0.0
84,
0.1
12,
0.1
37)
(0.1
26,
0.1
36,
0.1
57)
(0.1
38,
0.1
66,
0.2
05)
(0.1
37,
0.1
69,
0.2
10)
(0.1
55,
0.1
82,
0.2
17)
A4
(0.1
40,
0.1
59,
0.1
82)
(0.0
65,
0.0
90,
0.1
10)
(0.1
61,
0.1
72,
0.2
01)
(0.1
10,
0.1
31,
0.1
65)
(0.1
21,
0.1
52,
0.1
91)
(0.0
75,
0.0
90,
0.1
10)
A5
(0.2
34,
0.2
61,
0.2
96)
(0.2
53,
0.3
08,
0.3
63)
(0.1
29,
0.1
38,
0.1
52)
(0.1
44,
0.1
65,
0.1
99)
(0.1
75,
0.2
17,
0.2
73)
(0.2
24,
0.2
54,
0.2
94)
A6
(0.1
40,
0.1
59,
0.1
82)
(0.0
75,
0.1
02,
0.1
23)
(0.1
44,
0.1
50,
0.1
71)
(0.1
25,
0.1
48,
0.1
84)
(0.0
92,
0.1
18,
0.1
50)
(0.1
27,
0.1
49,
0.1
78)
A7
(0.0
75,
0.0
91,
0.1
11)
(0.1
54,
0.1
95,
0.2
30)
(0.0
87,
0.0
92,
0.1
09)
(0.0
86,
0.1
03,
0.1
29)
(0.0
71,
0.0
92,
0.1
19)
(0.1
18,
0.1
37,
0.1
62)
A8
(0.0
80,
0.0
93,
0.1
07)
(0.0
78,
0.1
09,
0.1
34)
(0.1
99,
0.2
06,
0.2
32)
(0.1
55,
0.1
83,
0.2
12)
(0.0
41,
0.0
59,
0.0
80)
(0.0
90,
0.1
11,
0.1
33)
CR
0.0
10.0
00.0
10.0
10.0
00.0
1
Table 6 The components for evaluating the alternatives
a b = c d L1 L2 R1 R2
A1 0.025 0.050 0.095 0.004 0.021 0.007 -0.051
A2 0.029 0.056 0.101 0.004 0.023 0.006 -0.051
A3 0.088 0.152 0.267 0.007 0.057 0.014 -0.129
A4 0.082 0.140 0.247 0.006 0.052 0.013 -0.120
A5 0.123 0.205 0.352 0.008 0.074 0.017 -0.163
A6 0.081 0.138 0.241 0.006 0.051 0.013 -0.116
A7 0.064 0.112 0.199 0.006 0.042 0.011 -0.098
A8 0.082 0.140 0.239 0.006 0.052 0.011 -0.111
22 Environ Earth Sci (2015) 73:13–26
123
Compared with other managerially based strategies, one
unanticipated finding is related to the rank of A7. Although
some 94 % of the basin available water demand is that
allocated to agriculture within a very low efficiency irriga-
tion system (Hashemi et al. 2010), Improvement of the
efficiency and reduction of water losses in irrigation and
water distribution systems with financial and legal supports
and as well proper use of facilities is the least popular
managerially based strategy throughout the basin. This
might be related to a weak performance of agricultural
education around the basin. It seems respondents are not
optimistic as regards implementation and performance of
modern agriculture. Hashemi et al. (2010) demonstrated that,
with the growing urban population, there is a remote prospect
that population of the Lake Urmia Basin would rise from 5.9
(2008) to 7.1 million by 2020. However, little progress has
been made in implementing the optimal irrigation pattern
and there is a great concern that the dry up of Lake Urmia
might face the same destiny as the Aral Sea catastrophe. Aral
Sea in the semi-arid region of Central Asia experienced an
environmental disaster caused by inefficient and non-sus-
tainable irrigation (Cai et al. 2003). Thus, to improve irri-
gation infrastructure, the environmental knowledge and
awareness of the public should be promoted and agricultural
education programs boosted to change the traditional pattern
of agriculture within the basin.
The least popular alternatives are classified in structurally
based strategies. Cloud seeding is an impact-limited strategy
(Morrison et al. 2009) with former experiences in the basin
not satisfying and not effective in mitigating the impacts of
drought. Within the realm of a sustainable development
paradigm, inter-basin water transfer is a controversial issue
that might cause population displacement, water pollution,
and salinization (Zhang 2009). In China, Ran and Lu (2013)
Table 8 Sensitivity analysis of
alternativesEv (A) neutral Rank Ev (A) pessimistic Rank Ev (A) optimistic Rank
u ¼ 0:5
A1 0.0607 8 0.1151 8 0.0037 8
A2 0.0657 7 0.1231 7 0.0042 7
A3 0.1817 2 0.3120 2 0.0303 2
A4 0.1689 3 0.2916 3 0.0261 3
A5 0.2443 1 0.4012 1 0.0533 1
A6 0.1659 5* 0.2862 4 0.0250 4
A7 0.1343 6 0.2375 6 0.0167 6
A8 0.1662 4* 0.2855 5 0.0248 5
u ¼ 1:5
A1 0.0824 8 0.1574 8 0.0074 8
A2 0.0880 7 0.1671 7 0.0083 7
A3 0.2341 2 0.4046 2 0.0590 2
A4 0.2149 3 0.3763 3 0.0496 3
A5 0.3080 1 0.5049 1 0.1019 1
A6 0.2121 4 0.3721 4 0.0483 4
A7 0.1751 6 0.3148 6 0.0330 6
A8 0.2108 5 0.3705 5 0.0477 5
Table 7 Final assessment of
the alternatives/ ¼ 1 Ev (A)
neutral
Rank Ev (A)
pessimistic
Rank Ev (A)
optimistic
Rank
A1 0.0682 8 0.1307 8 0.0049 8
A2 0.0732 7 0.1394 7 0.0056 7
A3 0.1964 2 0.3458 2 0.0394 2
A4 0.1818 3 0.3227 3 0.0337 3
A5 0.2608 1 0.4398 1 0.0688 1
A6 0.1784 4 0.3173 4 0.0324 4
A7 0.1463 6 0.2657 6 0.0219 6
A8 0.1778 5 0.3161 5 0.0321 5
Environ Earth Sci (2015) 73:13–26 23
123
claimed that excessive dependence on water engineering
projects had not sufficiently addressed water problems in that
country and more comprehensive environmental impact
assessments were needed. The possibilities of inter-basin
water transfer from Zab River, Aras River and as well from
the Caspian Sea to Lake Urmia have been studied (UNEP and
GEAS 2012). Also, Zarghami (2011) has applied a group
decision making method to detect the most suitable inter-
basin alternative among the four possible routes of water
transfer to the lake. Due to considerable economic costs, dire
environmental impacts on aquatic ecosystems and as well a
likely dewatering of the surface and groundwater resources
of adjacent areas (which might lead to political and social
conflicts) (Golabian 2011; UNEP and GEAS 2012), The
respondents were not of the inclination to have this strategy
implemented.
Sensitivity analysis
Sensitivity analysis was performed to examine the response
of alternatives when the membership function (u) changed.
To investigate the impact of changing this factor on the
selection of the most approved strategy, two other mem-
bership functions were tested. When u ¼ 0:5, in optimistic
and pessimistic evaluations, ranks of the alternatives were
similar to the results obtained for u ¼ 1; however, in
neutral condition, the ranks of A6 and A8 were substituted.
When u ¼ 1:5, the ranking was similar to the results when
u ¼ 1 (Table 8). To sum up, A6 and A8 are sensitive to
changes in membership functions, while A5 is a superior
alternative for all the scenarios with its top rank constant in
all conditions. Inter-basin water transfer (A1) and cloud
seeding (A2) are finally concluded as the least attractive
strategies within all the tested conditions (Fig. 4).
Conclusions
Throughout the ongoing paper, the merits of FAHP contri-
bution to SWOT–TOWS analysis in a sustainable develop-
ment context were discussed. The strategic alternatives for
reviving water resources and biodiversity rehabilitation of
Lake Urmia Basin were formulated via TOWS procedure
and prioritized by FAHP based on their capability in satis-
fying each selected sustainable development criterion.
From a technical point of view, implementation of
strategic management within the Lake Urmia Basin should
be guided mainly by following human resources manage-
ment and social learning. Effective involvement of stake-
holders in WRM is a democratic step that can promote
transparency of the evaluation process and facilitate the
achievement of compromised solutions. Such manageriallyFig. 4 Overall results of sensitivity analysis for alternatives in
a neutral, b pessimistic, c optimistic viewpoints
24 Environ Earth Sci (2015) 73:13–26
123
based strategies as spatial planning, empowering water
resources supervision and conservation, implementation of
optimal cropping pattern, optimization of allocations from
dams and promotion of irrigation efficiency are placed at
ranks 2nd to 6th. The low scores of two structurally based
strategies, namely inter-basin water transfer and cloud
seeding indicate these alternatives cannot satisfy sustain-
able development concept appropriately, due to limited
strategic impact, considerable economic costs, dire envi-
ronmental impacts on aquatic ecosystems and likely con-
flicts among upstream vs. downstream sectors.
From a methodological point of view and according to
the obtained results, the different viewpoints of extended
FAHP identified the same rankings among strategic alter-
natives when membership function equaled to 1 or 1.5.
When membership function decreased to 0.5, little changes
in middle-ranking alternatives were observed, but high and
low rankings were not sensitive to membership function
changes. Hence, sensitivity analysis demonstrated that
FAHP was a robust tool for decision making in compre-
hensive ecological issues. It can be concluded that FAHP
makes a valid contribution to the strategic management
framework and the application of SWOT–TOWS proce-
dure to the extended FAHP is recommended as a pragmatic
approach to deal with divergent interests, multiple objec-
tives and subjective assessments in cases of crisis water
management.
References
Abbaspour M, Javid AH, Mirbagheri S, Ahmadi-Givi FA, Moghimi P
(2012) Investigation of lake drying attributed to climate change.
Int J Environ Sci Technol 9(2):257–266
Abdullaev I, Rakhmatullaev S (2013) Transformation of water
management in Central Asia: from State-centric, hydraulic
mission to socio-political control. Environ Earth Sci. doi:10.
1007/s12665-013-2879-9
Adibee N, Osanloo M, Rahmanpour M (2013) Adverse effects of coal
mine waste dumps on the environment and their management.
Environ Earth Sci 70(4):1581–1592
Alley WM, Healy RW, LaBaugh JW, Reilly TE (2002) Flow and
storage in groundwater systems. Science 296(5575):1985–1990
Ardakanian R, Zarghami M (2004) Sustainability criteria for ranking
of water resources projects. First national conference on water
resources management. Iranian Water Resources Association,
Tehran (in Persian)
Aryafar A, Yousefi S, Doulati Ardejani F (2013) The weight of
interaction of mining activities: groundwater in environmental
impact assessment using fuzzy analytical hierarchy process
(FAHP). Environ Earth Sci 68(8):2313–2324
Badri MA (2001) A combined AHP-GP model for quality control
systems. Int J Prod Econ 72(1):27–40
Bonissone PP (1982) A fuzzy sets based linguistic approach: theory
and applications. In: Gupta MM, Sanchez E (eds) Approximate
reasoning in decision analysis, North-Holland, pp 329–339
Bozorg Haddad O, Marino MA (2007) Dynamic penalty function as a
strategy in solving water resources combinatorial optimization
problems with honey-bee optimization (HBMO) algorithm.
J Hydroinformatics 9(3):233–250
Broekhoven EV, Baets BD (2006) Fast and accurate center of gravity
defuzzification of fuzzy systems outputs defined on trapezoidal
fuzzy partitions. Fuzzy Sets Syst 157(7):904–918
Bryan BA, Grandgirard A, Ward JR (2010) Quantifying and
exploring strategic regional priorities for managing natural
capital and ecosystem services given multiple stakeholder
perspectives. Ecosystems 13(4):539–555
Buckley JJ (1985) Fuzzy hierarchical analysis. Fuzzy Sets Syst
17(3):233–247
Cai X, McKinney DC, Rosegrant MW (2003) Sustainability analysis
for irrigation water management in the Aral Sea region. Agric
Syst 76(3):1043–1066
Calizaya A, Meixner O, Bengtsson L, Berndtsson R (2010) Multi-
criteria decision analysis (MCDA) for integrated water resources
management (IWRM) in the Lake Poopo Basin, Bolivia. Water
Resour Manag 24(10):2267–2289
Chang DY (1996) Applications of the extent analysis method on
fuzzy AHP. Eur J Oper Res 95(3):649–655
Choi SJ, Kim JH, Lee DR (2012) Decision of the water shortage
mitigation policy using multi-criteria decision analysis. KSCE J
Civil Eng 16(2):247–253
Cosgrove WJ, Rijsberman FR (2000) World water vision: making
water everybody’s business. Earthscan, London, p 108
D’Souza R, Nagendra H (2011) Changes in public commons as a
consequence of urbanization: the Agara Lake in Bangalore,
India. Environ Manage 47(5):840–850
Danaee Fard H, Moshabbaki A, Abbasi T, Hassanpoor A (2011) Strategic
management in the public sector: reflections on it’s applicability to
Iranian Public Organizations. Public Organ Rev 11(4):385–406
Delju AH, Ceylan A, Piguet E, Rebetez M (2013) Observed climate
variability and change in Urmia Lake Basin, Iran. Theor Appl
Climatol 111(1–2):285–296
Department of Environment of Iran (2010) Integrated management
plan for Lake Urmia Basin. 1st edn. prepared in cooperation with
UNEP/GEF: (www.ramsar.org/pdf/wurc/LakeUrmiaManage
mentPlan-I.R.Iran2010.pdf)
Djamali M, de Beaulieu JL, Shah-hosseini M, Andrieu-Ponel V,
Ponel P, Amini A, Akhani H, Leroy SAG, Stevens L, Lahijani H,
Brewer S (2008) A late Pleistocene long pollen record from Lake
Urmia, NW Iran. Quat Res 69(3):413–420
Domenech L, March H, Saurı D (2013) Degrowth initiatives in the
urban water sector? A social multi-criteria evaluation of non-
conventional water alternatives in Metropolitan Barcelona.
J Clean Prod 38:44–55
Dyer RF, Forman EH (1992) Group decision support with the analytic
hierarchy process. Decis Support Syst 8(2):99–124
Eimanifar A, Mohebbi F (2007) Urmia Lake (Northwest Iran): a brief
review. Saline Syst 3(5):1–8
Gallego-Ayala J (2012) Selecting irrigation water pricing alternatives
using a multi-methodological approach. Math Comput Modell
55(3–4):861–883
Gallego-Ayala J, Juızo D (2011) Strategic implementation of
integrated water resources management in Mozambique: an
A’WOT analysis. Phys Chem Earth 36(14–15):1103–1111
Ganoulis J, Skoulikaris H, Monget JM (2008) Involving stakeholders
in transboundary water resources management: the Mesta/Nestos
‘HELP’ basin. Water SA 34(4):461–467
Garfi M, Marti LF, Bonoli A, Tondelli S (2011) Multi-criteria
analysis for improving strategic environmental assessment of
water programmes. A case study in semi-arid region of Brazil.
J Environ Manage 92(3):665–675
Garousi V, Najafi A, Samadi A, Rasouli K, Khanaliloo B (2013)
Environmental crisis in Lake Urmia, Iran: a systematic review of
causes, negative consequences and possible solutions. 6th
Environ Earth Sci (2015) 73:13–26 25
123
international perspectives on water resources and the environ-
ment, Izmir, Turkey, 7–9 Jan
Ghaheri M, Baghal-Vayjooee MH, Naziri J (1999) Lake Urmia, Iran:
a summary review. Int J Salt Lake Res 8(1):19–22
Ghorbani-Aghdam M, Dinpashoh Y, Mostafaeipour A (2013) Appli-
cation of factor analysis in defining drought prone areas in Lake
Urmia Basin. Nat Hazards 69(1):267–277
Golabian H (2011) Urumia Lake: hydro-ecological stabilization and
permanence. In: Badescu V, Cathcart RB (eds) Macro-engineer-
ing seawater in unique environments: arid lowlands and water
bodies rehabilitation, environmental science and engineering.
Springer, Berlin, pp 365–397
Hajkowicz S, Collins K (2007) A review of multiple criteria analysis
for water resource planning and management. Water Resour
Manage 21(9):1553–1566
Hashemi M, O’connell PE, Amezaga JM, Parkin G (2010) A socio-
technical framework for implementing the integrated water resources
(IWRM) plan in Lake Urmia Basin, Iran. BHS third international
symposium: role of hydrology in managing consequences of a
changing global environment, Newcastle, UK, 19–23 July
Hassanzadeh E, Zarghami M, Hassanzadeh Y (2012) Determining the
main factors in declining the Urmia lake level by using system
dynamics modeling. Water Resour Manage 26(1):129–145
Huang S, Li X, Wang Y (2012) A new model of geo-environmental
impact assessment of mining: a multiple-criteria assessment
method integrating fuzzy-AHP with fuzzy synthetic ranking.
Environ Earth Sci 66(1):275–284
Karbassi A, Nabi Bidhendi G, Pejman A, Esmaeili Bidhendi M
(2010) Environmental impacts of desalination on the ecology of
Lake Urmia. J Great Lakes Res 36(3):419–424
Kaufmann A, Gupta MM (1985) Introduction to fuzzy arithmetic: theory
and applications. Van Nostrand Reinhold Co, New York, p 351p
Kulak O, Durmusoglu B, Kahraman C (2005) Fuzzy multi-attribute
equipment selection based on information axiom. J Mater
Process Technol 169(3):337–345
Lai VS, Wong BK, Cheung W (2002) Group decision making in a
multiple criteria environment: a case using the AHP in software
selection. Eur J Oper Res 137(1):134–144
Lee-Kwang H, Jee-Hyong L (1999) A method for ranking fuzzy
numbers and its application to decision-making. IEEE Trans
Fuzzy Syst 7(6):677–685
Lennox J, Proctor W, Russell S (2011) Structuring stakeholder
participation in New Zealand’s water resource governance. Ecol
Econ 70(7):1381–1394
MacCrimmon KR (1973) An overview of multiple objective decision
making. In: Cochrane JL, Zeeny LM (eds), Multiple criteria
decision making. The University of South Carolina Press, pp 7–31
Martin-Ortega J, Berbel J (2010) Using multi-criteria analysis to
explore non-market monetary values of water quality changes in
the context of the water framework directive. Sci Total Environ
408(19):3990–3997
Mencio A, Folch A, Mas-Pla J (2010) Analyzing hydrological sustain-
ability through water balance. Environ Manage 45(5):1175–1190
Mergias I, Moustakas K, Papadopoulos A, Loizidou M (2007) Multi-
criteria decision aid approach for the selection of the best
compromise management scheme for ELVs: the case of Cyprus.
J Hazard Mater 147(3):706–717
Morrison A, Siems S, Manton M, Nazarov A (2009) On the analysis
of a cloud seeding data set over Tasmania. J Appl Meteorol
Climatol 48(6):1267–1280
Muro M, Jeffrey P (2012) Time to talk? How the structure of dialog
processes shapes stakeholder learning in participatory water
resources management. Ecol Soc. doi:10.5751/ES-04476-170103
Nazari A, Salarirad MM, Aghajani-Bazzazi A (2012) Landfill site
selection by decision-making tools based on fuzzy multi-attribute
decision-making method. Environ Earth Sci 65(6):1631–1642
Ozyavas A, Khan SD (2012) The driving forces behind the Caspian
Sea mean water level oscillations. Environ Earth Sci
65(6):1821–1830
Panagopoulos GP, Bathrellos GD, Skilodimou HD, Martsouka FA
(2012) Mapping urban water demands using multi-criteria
analysis and GIS. Water Resour Manage 26(5):1347–1363
Pires A, Chang N, Martinho G (2011) An AHP-based fuzzy interval
TOPSIS assessment for sustainable expansion of the solid waste
management system in Setubal Peninsula, Portugal. Resour
Conserv Recycl 56(1):7–21
Ran L, Lu X (2013) Redressing China’s strategy of water resource
exploitation. Environ Manage 51(3):503–510
Rezaei-Moghaddam K, Karami E (2008) A multiple criteria evalu-
ation of sustainable agricultural development models using AHP.
Environ Dev Sustain 10(4):407–426
Roman F, Rolander N, Fernandez MG, Bras BA, Allen JK, Mistree F,
Chastang P, Depince P, Bennis F (2004) Selection without
reflection is a risky business… 10th AIAA/ISSMO multidisci-
plinary analysis and optimization conference. Albany, New York,
USA, Paper Number: AIAA-2004-4429, August 30–September 1
Saaty TL (1980) The analytic hierarchy process. McGraw Hill, New
York, p 287
Salminen P, Hokkanen J, Lahdelma R (1998) Comparing multicriteria
methods in the context of environmental problems. Eur J Oper
Res 104(3):485–496
Sa-nguanduan N, Nititvattananon V (2011) Strategic decision making
for urban water reuse application: a case from Thailand.
Desalination 268(1–3):141–149
Sener E, Davraz A (2013) Assessment of groundwater vulnerability
based on a modified DRASTIC model, GIS and an analytic
hierarchy process (AHP) method: the case of Egirdir Lake Basin
(Isparta, Turkey). Hydrogeol J 21(3):701–714
Singh A, Singai CB, Srivastava S, Sivam S (2009) Inclusive water
governance: a global necessity. lessons from India. Transit Stud
Rev 16(2):598–608
Srdjevic B, Medeiros YDP (2008) Fuzzy AHP assessment of water
management plans. Water Resour Manage 22(7):877–894
UNEP (United Nations Environment Programme), GEAS (Global
Environmental Alert Service) (2012) The drying of Iran’s Lake
Urmia and its environmental consequences. Environ Dev
2(1):128–137. doi:10.1016/j.envdev.2012.03.011
United Nation (UN) (2004) Guidelines on strategic planning and
management of water resources, environment and development
division, New York, USA. http://www.unescap.org/ttdw/ppp/
files/guideline_spm.pdf
Van Laarhoven PJM, Pedrycz W (1983) A fuzzy extension of Saaty’s
priority theory. Fuzzy Sets Syst 11(1–3):199–227
Weihrich H (1982) The TOWS matrix: tool for situational analysis.
Long Range Plan 15(2):54–66
Yang CC, Chen BS (2004) Key quality performance evaluation using
fuzzy AHP. J Chin Inst Ind Eng 21(6):543–550
Yang XL, Ding JH, Hou H (2013) Application of a triangular fuzzy
AHP approach for flood risk evaluation and response measures
analysis. Nat Hazards 68(2):657–674
Yilmaz B, Harmancioglu NB (2010) Multi-criteria decision making
for water resource management: a case study of the Gediz River
Basin, Turkey. Water SA 36(5):568–574
Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353
Zarghami M (2011) Effective watershed management; case study of
Urmia Lake, Iran. Lake Reserv Manag 27(1):87–94
Zarghami M, Szidarovszky F (2011) Multicriteria analysis: applica-
tions to water and environment management. Springer, Berlin,
p 195
Zhang Q (2009) The south-to-north water transfer project of China:
environmental implications and monitoring strategy. J Am Water
Resour Assoc 45(5):1238–1247
26 Environ Earth Sci (2015) 73:13–26
123
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