CC BY
49Magazine of Civil Engineering. 2022. 114(6). Article No. 11405
Magazine of Civil Engineering issn
2712-8172
journal homepage: http://engstroy.spbstu.ru/
Research article UDC 528.01:004.428:519.816 DOI: 10.34910/MCE.114.5
AHP-TOPSIS hybrid decision support system for dam site selection
S. Rodriguez Vazquez " , N. Mokrova
National Research Moscow State Civil Engineering University, Moscow, Russia M solrusita85@gmail. com
Keywords: river basins, selection of areas, dams, multicriterial analysis, decision making, AHP, TOPSIS
Abstract. Inadequate selection of an area for the location of dams brings about social and economic disadvantages due to the non-fulfillment of its objective, as well as causes significant damage to the ecosystem of the river basin. This type of selection depends on a set of different criteria and variables, so it is necessary to develop a tool to support decision-making that allows reducing the collateral damage that a project of this type entails and increasing the project effectiveness. This paper proposes the development of a hybrid method of multicriterial analysis using the hierarchical analysis technique (AHP) and the technique for the order of preferences by similarity to the ideal solution (TOPSIS) with a specific focus on the selection of sites for dams. Modifications to traditional methods were established by eliminating the decision-maker as an evaluator of alternatives. The proposed method is based on the hierarchical ordering of alternatives taking into account the subjective judgments of decision-makers when considering the uncertainties of the selection process. The ordering of the alternatives is preceded by the analysis of a series of hydrological, geological, topographic and land use parameters extracted from a digital elevation model. According to the results obtained, it was possible to order hierarchically (better-worse) each of the river basins evaluated according to the established parameters.
Citation: Rodriguez Vazquez, S., Mokrova, N. AHP-TOPSIS hybrid decision support system for dam site selection. Magazine of Civil Engineering. 2022. 114(6). Article No. 11405. DOI: 10.34910/MCE.114.5
1. Introduction
The social and economic contributions of dams in most cases outweigh the damages and costs provided by the construction of dams worldwide. The selection of the best location for the construction of a dam is one of the most complex and controversial decisions in water supply management [1]. Just as optimal site selection can improve reservoir safety and groundwater regeneration in a region, poor site selection can undermine them. A well-selected site will not only provide direct benefits, but its careful design can also provide the additional benefit of a recreation area surrounding the reservoir. Conversely, a poorly selected site could cause harmful influences such as negative biophysical, socio-economic and geopolitical impacts, often through the loss of ecosystem services provided by fully functioning aquatic systems [2-5]. Therefore, for the selection of dam sites, it is necessary to conduct an accurate study of the area of interest considering the factors affecting this selection. However, this procedure is expensive and time-consuming. With advances in Informatics and Information Technologies, the determination of competitive solutions in terms of cost, time and a variety of other objective variables is greatly facilitated.
A powerful tool that plays a notable role in this process is the Geographic Information System (GIS) and its applications in hydrology. In addition to GIS, Multi-Criteria Decision Making (MCDM) helps decision makers select from alternative solutions, in this case sites for dam construction, where there are many criteria [6]. Two of the most commonly used MCDM techniques are known as analytical hierarchy process (AHP) [7] and technique for order of preference by similarity to the Ideal solution (TOPSIS) [8-11]. The integration of MCDM and other data analysis tools such as GIS have been commonly used by previous
© Rodriguez Vazquez, S., Mokrova, N., 2022. Published by Peter the Great St. Petersburg Polytechnic University.
researchers, particularly in dam site selection studies [12-15]. It should be noted that GIS is powerfully used in the selection of dam sites [16-18]; however, its capacity in a certain region may differ from other locations. The AHP approach is an effective tool for System Analysis and solves decision problems by reducing complex decisions to a series of peer comparisons. AHP is an effective multi-criteria decisionmaking technique that has been used to solve decision problems in a variety of fields [19-20]. In addition, the AHP includes an effective technique for checking the consistency of the decision maker's assessments, thus decreasing the bias in the decision-making process [21]. The TOPSIS method is based on the idea that the best alternative has the shortest distance from the ideal solution and the furthest distance from the negative ideal solution. The ideal solution is assumed to be an alternative that has the best values for all criteria considered, while the negative ideal solution is identified as a hypothetical alternative that has the worst criteria values [8]. Many articles have been published in the field of dam site selection using MCDM techniques to choose a viable location to build a dam. However, the authors of the present research are not aware of any studies using TOPSIS and AHP as a hybrid method to solve dam site selection problems.
A wide range of risk or performance analysis studies have been conducted in multiple watersheds, dams or tunnels around the world with various hydroclimatological regimes [22-26]. Piadeh F. et al. (2012) carried out a study to prioritize the best locations for irrigation with treated wastewater (TWW) in Tunisia [26]. Potential viable locations were identified based on resource conflicts, cost-effectiveness, land suitability, social acceptance and environmental factor. Several researchers have applied fuzzy systems in decision-making methods [27-30]. Using fuzzy AHP combined with GIS, they were able to map and prioritize appropriate sites for different purposes. Reliable data and advanced technologies are two necessary elements for efficient classification management based on its overall performance using the TOPSIS technique. Kim et al. (2012) used the TOPSIS method in a diffuse environment to classify the best of ten sites for treated wastewater (TWW) in an urban Basin of South Korea [27]. They considered four main criteria, including technical, social, economic and environmental criteria. Uncertainty of weighting values and input data were considered using triangular fuzzy numbers, and data were collected through individual interviews.
Zyoud et al. (2016) used AHP and TOPSIS methods within a diffuse environment to create a framework for Water Loss Management in developing countries [29]. They proposed a hierarchical structure of the decision problem consisting of four levels: overall objective; main criteria; evaluation criteria; and options. In this study, the weightings of the criteria were determined by AHP fuzzy, and TOPSIS fuzzy was also used to rank the options in terms of their potential to meet the overall goal based on the assessments and preferences of decision makers. The most important option was supposed to be a pressure management and control strategy. In addition, the use of advanced techniques and the establishment of District measurement areas were identified as the second and third most important, respectively. In addition, based on the results of the sensitivity analysis, the stronger and weaker options were less sensitive to changes in the weightings of the evaluation criteria. Ozcan et al. (2017) applied the AHP and TOPSIS methods for the selection of maintenance strategies in hydropower plants in Turkey [31]. In their study, a combined AHP-TOPSIS methodology was used to choose the most critical equipment. Nine units critical for hydropower plants were identified. A goal programming (GP) model was proposed to obtain combinations of maintenance strategies for the team. The results showed that there was a 77.1 % improvement in the frequency of plant failures as a result of employing an incorrect maintenance strategy on critical equipment compared to the period when the model was not used. Onüt and Soner (2018) conducted a comparison between AHP and TOPSIS techniques to select an optimal transshipment site in Istanbul, Turkey [32]. They used fuzzy sets to account for uncertainties in different criteria and derived criteria weightings based on a peer comparison using the AHP method. Mulliner et al. (2016) conducted a comprehensive analysis of five different MCDM techniques, including TOPSIS and AHP, to assess sustainable housing affordability using different economic, social and environmental criteria in Liverpool, UK [33]. Their results show that the overall classification of alternatives varies from method to method, and there is no perfect technique for this problem. Therefore, when possible, applying a selection of different methods to the same problem is ideal. Balioti et al. (2018) applied the AHP and TOPSIS methods with fuzzy logic to select the optimal type of Spillway on a dam in northern Greece [34]. They concluded that these tools are necessary to take into account additional parameters beyond technical and construction costs. Table 1 shows a quick review of a recent research study on dam site selection using AHP and TOPSIS techniques.
Table 1. The optimal dam site selection using a group decision-making method through fuzzy TOPSIS model [35].
Reference Year_Site Selection for_Applied Method
[36] 2012 Underground Dam AHP
[37] 2013 Dam AHP
[38] 2013 Dam TOPSIS
[39] 2013 Underground Dam AHP
[39] 2014 Subsurface dams AHP
[40] 2015 Small underground dams AHP
[41] 2015 Dam AHP
[18] 2016 Dam AHP
[42] 2017 Dam AHP
[43] 2018 Dam AHP
[1] 2018 Dam TOPSIS
In order to prevent the damage caused by floods and intense rains due to climatic phenomena Cuba has proposed to build new dams and reservoirs. In addition, the stages of drought deprive that part of the population of the necessary and continuous supply of water. The construction of new dams in strategic locations can contribute to improving these aspects. This article presents a study based on the selection of suitable dam sites in Manicaragua municipality, Villa Clara, Cuba. Therefore, a study on two MCDM models (TOPSIS and AHP) in the GIS environment is applied to determine the proposed locations for dam construction. For this, based on the literature and similar research experiences, several criteria were used in the selection procedure. Therefore, the main objectives of this study are as follows:
• Propose several factors that affect the selection of the dam site in Manicaragua municipality, Villa Clara province, Cuba, based on previous experiences around the world.
• Integrate MCDM and GIS into the study area for the purpose of selecting the dam site.
• Consider topographic and morphological conditions in the selection of the dam site.
• Assist decision makers in the construction of new dams in the area of interest.
The present research is organized as follows. At the beginning, the hybrid method and the way of combining the AHP and TOPSIS techniques to choose the best locations for dam construction is described. Subsequently, the results of the implementation of the proposed hybrid method are discussed and finally the conclusions obtained from the validation of the AHP-TOPSIS hybrid method are provided.
2. Methods
The hybrid method used in this study is based on the integration of hierarchical ordering methods AHP and TOPSIS. First, the AHP method is applied in order to determine what are the criteria and sub-criteria that should be taken into account and the weight that these should have when evaluating possible alternatives. Following this, the TOPSIS method is applied in order to select the closest solution to the ideal before all possible alternatives that exist, making use of a similarity index that is constructed by combining the proximity to the positive ideal and the distance from the negative ideal. Decision making in this integrated method involves several essential steps.
Step 1: Structure the problem as a hierarchy
The first step of the AHP method is to model the decision problem that is intended to be solved as a hierarchy. In this step, the effective criteria for locating the dam site are determined by using a thorough review of the literature and expert opinions. The hierarchy is then modeled as a graphical representation of a complex problem in which the objectives, criteria and alternatives are at the highest, intermediate and lowest levels, respectively (Fig. 1).
Figure 1. Outline of the AHP analysis regarding the context of the research.
Step 2: Prioritization among criteria
The fundamental idea of this study is to facilitate the work of the experts so the proposal in this step by the author is that the experts should only evaluate the criteria according to their opinion and not weigh each of the criteria in correspondence with the rest of the criteria. For this purpose, the author proposes to use two comparison scales, one that will be used by the expert to evaluate the criteria (Table 2) and another so that the GIS system itself creates the peer comparison matrix using the criteria established by the expert. The latter scale corresponds to the scale proposed by Saaty [7] (Table 3).
Table 2. Scale proposal for the direct assignment made by the expert.
Qualitative assessment Quantitative valuation
Very Low Importance 1
Low Importance 3
Moderately important 5
Strong importance 7
Highest importance 9
Table 3. Fundamental paired comparison scale.
FUNDAMENTAL PAIRED COMPARISON SCALE
Numerical scale Verbal comparison scale Explaining
1 Equal importance Two activities contribute equally to the objective
3 Moderate importance of one element over another Experience and judgment are in favor of one element over another
5 Strong importance of one element over another One element is strongly favored
7 Very strong importance of one element over another One element is very dominant
9 Extreme importance of one An element is favored by at least an
element over another order of magnitude difference
In order to establish priorities between the elements at each level, a measurement methodology proposal is made that allows connecting the scale proposed in Table 2 (expert assessments) and the scale proposed by Saaty in Table 3 for the paired comparison. For this the methodology is aided by 2 fundamental steps:
Step 2.1. The expert determines the importance of each criteria with respect to the objective and the sub-criteria with respect to each criteria. This is done by making use of Table 2.
Step 2.2. An intermediate value scale (Table 4) is created using Table 2 and 3. This scale will be used by the GIS system internally and will allow to know the values that are obtained from the paired comparison between the valuations given by the expert to each of the criteria. (These values are used to complete paired comparison arrays).
Table 4. Value comparison scale using Tables 2 and 3. (Source: Author's creation).
Comparison scales of the values assigned by the expert Values obtained by comparison Comparison scales of the values assigned by the expert Values obtained by comparison
(1) to (n ) -;( n = 1,3,5,7,9) n (n ) to (1) n;( n = l, 3,5,7,9)
(3) to (5) (5) to (7) (7) to (9) l 3 (5) to (3) (7) to (5) (9) to (7) 3
(3) to (7) (5) to (9) l 5 (7) to (3) (9) to (5) 5
(3) to (9) l 7 (9) to (3) 7
On the basis of the information collected with the measurement methodology outlined above, we construct a matrix R of dimension m x n (Eq. (1)), where rij represents the priority between factor i and
V
factor j, and the values of the lower half with respect to the diagonal values (reciprocal) correspond to the
f \
inverse values of the upper half
j
l
r. , being rj
'J
l rl2
R = r2l l
rnl rn2
rln
'2n
l
(1)
In the comparison matrix R, the columns represent the relative weights of each factor with respect to the others. To determine the factor of greatest preference, for a certain criterion, the values are normalized by dividing each element of column j by the sum of all elements of that column (Equation (2))
and then, estimating a vector of weights w = [ wi, W2,..., wn ]. This weight vector is obtained by averaging
each row of the normalized matrix (Equation (3)) and its value indicates the relative importance of each factor in a range between 0 and 1.
xj =■
r-
'j
1 rij
i=1
n x,
(2)
(3)
Wi = 1^. j =1 n
Subjective valuations may cause inconsistencies. Because of this it becomes necessary to measure the consistency of the paired comparison matrix. If a matrix is consistent it must be verified that, if the judgments are consistent, the matrixR would have a single eigenvalue X = n, but since it is not possible
for a person to be perfectly consistent then inconsistency will always exist. The important thing is that a certain permissible limit is not exceeded.
The above implies that the paired comparison matrix will have more than one eigenvalue Xi. The maximum eigenvalue (X max ) allows us to estimate the degree of consistency of the paired comparison matrix using the consistency index (CI) (Eq. (6)).
To obtain the value of Xmax first of all it is necessary to multiply the comparison matrix, row R by
the vector of priorities w (Equation (4)) to obtain a vector of consistency Xw = [Xwi,Xw2,...,Xwn]. If the
matrix R were perfectly consistent, the sum of the elements of the obtained vector should be equal to n . The situation above does not usually happen, so to determine the degree of inconsistency it is necessary
to divide each element of the consistency vector Xw by its vector w corresponding in the priority and in this way all possible values Xi, are obtained. Then to finally obtain Xmax we proceed to perform Eq. (5):
"11 "12 "13 w1 Xw1
'"21 "22 "23 X w2 = Xw2
'31 "32 "33 w3 Xw3
Xr
n X-
i =1
n
X — n CI = max "
" n -1 '
(4)
(5)
(6)
Then to check if the degree of consistency is permissible, a Random Consistency Index (RI) is used as a reference (Table 5) [44, 45].
Table 5. Random Consistency Index (RI) as a function of the dimension (n) of the matrix.
n 1 2 3 4 5 6 7 8
RI 0 0 0.525 0.882 1.115 1.252 1.341 1.404
n 9 10 11 12 13 14 15 16 <
RI 1.452 1.484 1.513 1.535 1.555 1.570 1.583 1.595
The consistency ratio (CR) measures the degree of inconsistency of the paired comparison matrix and is calculated as follows:
CR =
Ci RI
(7)
If CR = 0 then the matrix is consistent, if CR < 0.10 the matrix has an allowable inconsistency, which means that it is considered consistent and therefore the vector of weights w is also accepted as valid. But if CR > 0.10, the inconsistency is inadmissible and it is advisable to review the assessments made.
Step 3: Establishing local priorities among sub-criteria
In the modeling of the decision problem as a hierarchy, the decomposition of all criteria into sub-criteria has been considered. The procedure is the same as that described in the previous step, but in this case paired comparisons between sub-criteria should be made to determine their relative importance with respect to the criteria immediately higher in the hierarchy.
Step 4: Setting local and global priorities among alternatives
The procedure is the same as explained in step 2, but this time the priority level of one alternative over another is established taking the degree of compliance or satisfaction of each criteria or sub-criteria as a basis of comparison.
After we obtained the weight vectors of each alternative with respect to each sub-criteria, they are coupled in a matrix of weight vectors. This matrix is multiplied by the vector of weights of the sub-criteria of the corresponding criteria, and this action is repeated for each criteria, obtaining the vector of local weights of each alternative with respect to the criteria that encompasses the respective sub-criteria: [46-48]. Let us illustrate what has been said by i.e. Eq. (1)
A
n
(1)
Priority Vector of each alternative with respect to sub-criteria
Vector of priority of t he sub-criteria over the criteria
Priority Vector of each alternative wit h respect to the criteria
Finally, the evaluation matrix of the alternatives with respect to the criteria is obtained (Table 6). Table 6. Valuation matrix.
Wi w2 ■ Wj • W rvn
Ci C2 • ■ Cj • C ^n
A x11 x12 ■ x1j • x1n
A x21 x22 ■ x2 j • x2n
A xV1 xi 2 ■ xij • xin
A m xm1 xm2 • ' xmj • x ^mn
where:
w
= [ w\, w2wn ] is the vector of relative weights or priorities associated with the criteria;
xj =
XU
X2 j
mj
is the vector of local priorities of the alternatives established based on the criteria C ;
• xi = [xn,xi2,...,xin] is the local priority vector associated with the alternative Ai, whose components are the local priorities associated with this alternative according to each of the criteria. Standardization of the decision matrix
Let us move on to considering a combination of AHP and TOPSIS methods. The TOPSIS method evaluates the decision matrix obtained in the previous step (Table 6), which refers to m alternatives Aj,
i = 1,2,...,m, which are evaluated based on n criteria Cj, j = 1,...,n. Where Xj denotes the valuation of the ith alternative in terms of jth criteria. And where w = [wb W2,..., wn ] is the vector of weights associated with C j.
The dimensions of the various criteria are then converted to non-dimensional criteria. An element
is calculated as:
nof the normalized decision matrix N =
n
2
r, i = 1,...,m; j = 1,...,n.
nij =~i— Mjf
V =1
Step 5: Creation of weighted normalized decision matrix
The weighted normalized value vij of the weighted normalized decision matrix V =
(9)
vij
is
calculated as:
Vj = Wj ®nij, i = 1,...,m; j = 1,...,n,
(10)
where wj is such that 1 e ^ wj is the weight of the jth attribute or criteria.
j=1
Step 6: Definitions of the positive ideal solution (PIS) and negative ideal solution (NIS)
The positive ideal solution minimizes cost criteria and benefit criteria; conversely, the negative ideal
solution maximizes cost criteria and minimizes benefit criteria. The set of positive ideal values A+ and the set of negative ideal values A are determined as:
, IY V Yl
A+ = jv1 + ^^ vn + \ = <
A =^v1 vn
max vif, j e J
V i
f
min vij, j e J'
\f
V I
min vü, j e J
V i
; i = 1,2,..., m,
max vif, j e J'
Vi
; i = 1,2,..., m,
(11)
(12)
where J is associated with the profit criteria and J' is associated with the cost criteria.
Step 7: Determination measures of distances between positive and negative ideal alternatives and solutions
In this step, the separation of each alternative from the positive ideal solution and the negative ideal
-+
solution is calculated and then two different GIS layers A and A are created, similar findings are found in [49-51].
+
The separation of each alternative from the positive ideal solution A is given as:
di +=' I/vu -vij +
1 1 2
; i = 1,...,m.
(13)
And the separation of each alternative of the negative ideal solution A is as follows:
di =
n f.
I
j=1V
v - — v-
; i = 1,...,m.
In this case the Euclidean m-multidimensional distance is used. Step 8: Calculation of the performance of each alternative
The relative proximity of Ri to the ideal solution can be expressed as follows:
(14)
1
R = _+dj __ ; j = 1,...,m, (15)
dj + dj
• If R = 1 ^ A = A+
• If R = 0 ^ A = A"
The closer the value of Rj is to 1, it implies a higher priority of the jth alternative.
Step 9: Ranking alternatives
In the last step, the values of the layer (the relative proximity to the positive ideal solution) created in step 7 are determined for the selected sites based on topographic conditions. The set of sites can now be
sorted by the descending order of the value of R. The best sites are those that have higher R values
and as they are closer to the positive ideal solution, they are preferable and should be chosen as is the case of the outputs obtained in [50].
3. Result and Discussion
The case study focuses on the selection of suitable sites for the construction of dams in the municipality of Manicaragua, Cuba. The objectives for the construction of these dams can be: support for the development of agriculture and industry, drinking water supply, power generation, fishing.
The municipality of Manicaragua has a surface area of 1064.4 km2, of which 309 km2 constitute a mountain area with limitations for habitat development, due to various risk factors that this area has. Only the mountainous area of the municipality represents approximately 29 % of the territorial total, standing out within the provincial statistics of Villa Clara. It is bordered to the north by the municipalities of Santa Clara, Ranchuelo and Placetas; to the South by the provinces of Cienfuegos and Sancti Spiritus; to the East by the province of Sancti Spiritus; and to the West by the municipality of Ranchuelo and the province of Cienfuegos.
Its relief is characterized by a fluvial plain and slightly dissected pre-mountain heights, the highest above mean sea level is the Pico Tuerto, with 919 meters, which in turn is the highest elevation of the province. Brown soils with carbonates, grayish brown and leached red ferralites prevail.
Within its hydrographic characteristics it can be commented that it presents a group of areas considered to be at risk for flooding, highlighting the area included within the Jibacoa Valley, which occupies an area of 12 km2. Here, intense rains alone cause the incommunication of the area affecting 176 homes with a total of 486 people having to evacuate. Two more areas, popular councils of Las Cajas and Nicaragua I, are also subject to some significant damage, albeit not as severe as the popular council of Jibacoa.
In the first step, to determine the effective factors in the selection of an appropriate site for the construction of dams in the municipality of Manicaragua, an exhaustive review of the literature [52-56] was carried out. The most important criteria were selected and used in the current research. Below is a brief explanation of the selected criteria and the sub-criteria associated with each of them.
Hydrology (C1) : main stream length (Q1), mainstream slope (C12), time of concentration (C13), maximum flow estimate (Q4), runoff coefficient (Q5), rainfall intensity (C16), real evapotranspiration (Q7 ), average annual rainfall of the basin (Q8), sinuosity of water currents (C19), average annual rainfall volume of the basin (Q10 ), constant stability of the river (Cm ), order of rivers (C112 ), torrential coefficient (Q13 ), calculation of runoff coefficient (Q14 ).
Topography (C2 ): basin area (C21), compactness index (C22 ), form factor (C23 ), middle slope of the basin (C24 ), drainage density (C25 ), average elevation of the basin (C26 ), watershed width (C27 ), elongation index (C28 ).
Geology (C3 ) : coefficient of massiveness of the basin (C31), orographic coefficient (C32).
Land Use (C4 ): delimitation of areas suitable for location (C41).
After collecting and evaluating the required information based on the selected criteria (mentioned above), 29 feasible alternatives were proposed for the dam site in Manicaragua. Three of them A1, A4, A28, were not be included within the alternatives to be evaluated: A1 turned out to be a false
positive, while the analysis showed that the area of A4 and A28 did not include tributaries of rivers. The
locations of the proposed alternatives are show in Fig. 2 and are identified by numbers. Each of these alternatives represents the basins present in the municipality. This way the best area would be evaluated along with the best locations in each of these areas.
Figure 2. Alternatives for the site of the dam in the municipality of Manicaragua, Cuba.
After selecting the criteria for locating the dam site and considering alternatives (Fig. 2), the combination of AHP and TOPSIS methods for paired comparison was applied to select the best site. Fig. 3 shows the problem of site selection of the Manicaragua dam using a hierarchical structure. The structure has four levels: Objective (location of the dam site), criteria (Q to C4), sub-criteria (Q1 to Q14, C21 to
C28, C31 to C32, C41) and alternatives (A1 to A29).
Figure 3. Selection of the dam site of the municipality of Manicaragua using a hierarchical structure.
To evaluate the relevance of the criteria incorporated in the AHP method, a questionnaire was developed, and an E expert, involved in the dam construction project, was asked to express the
importance of each criterion using linguistic variables established in Table 2. Then, a pair comparison matrix was formed to determine the weights of the criteria according to the description made in step 2. Once the paired comparison matrices are obtained and the local and global priorities for each of the criteria, sub-criteria and alternatives are established, the weighted normalized decision matrix is constructed, obtaining the distribution of values according to the graphs shown in Fig. 4.
(c) (d)
Figure 4. Prioritization of the weights of the alternatives according to the criteria in the normalized matrix. a) Criteria Ci, b) Criteria C2, c) Criteria C3, d) Criteria C4.
The distances between alternatives and ideal positive and negative solutions are subsequently determined. The Euclidean distance is calculated for the best ideal value I d + I and for the worst ideal
value I di |, Fig. 5. Then the relative proximity Ri to the ideal solution is calculated, Fig. 6.
Figure 5. Best ideal value
d I and worst ideal value
V
d I of each alternative.
V
Figure 6. Graph of the relative proximity Ri to the ideal solution.
Finally, the best alternatives are sorted according to R in descending order. Table 7. Hierarchical order of the alternatives evaluated.
Alternative Weight Ranking Alternative Weight Ranking
A5 0.8401 1 A23 0.3241 14
A27 0.6259 2 A17 0.3213 15
A22 0.4959 3 A14 0.2729 16
A2 0.4871 4 A9 0.2104 17
A10 0.4825 5 A19 0.1943 18
A16 0.4751 6 A12 0.1869 19
A29 0.4691 7 A11 0.1683 20
A15 0.346 8 As 0.1681 21
A 0.3453 9 A3 0.1642 22
A7 0.3453 10 A13 0.1642 23
A21 0.33 11 A20 0.1642 24
A26 0.3272 12 A1S 0.0167 25
A4 0.3241 13 A25 0 26
Figure 7. Hierarchical order of the alternatives evaluated.
As a result of using the proposed hybrid system, it was obtained that the best evaluated basin or its alternative turned out to be basin number 5, while the worst evaluated one was basin number 25. It is very important in all this type of research that the assessment of the expert(s) on each of the criteria and sub-criteria is correctly performed because a change or mistake in said assessment will change the results obtained. Taking into account the results of the conducted analysis and local surveys, the experts involved in the design of the dam confirmed the robustness of the research methodology and findings.
Figure 8. Analysis of one of the points determined by the model as a possible area for the location of dams.
4. Conclusion
This research presents the application of an AHP-TOPSIS hybrid method, integrating GIS, in the selection of dam sites in the Manicaragua municipality of the province of Villa Clara, Cuba. Based on the review of recent studies on dam site selection using MCDM, it can be commented that many researchers have mainly used AHP and TOPSIS in the application of dam site selection, but independently. Therefore, a method was implemented that worked with the advantages offered by each one in a study area in Cuba to evaluate its capacity for the selection of dam sites in a new local context. Based on previous experience and literature, several factors (criteria) were presented, including geology, land use, hydrology and topography. Once this hybrid method was implemented in the GIS environment, relatively suitable sites for dam construction were located in the area of interest. Finally, to verify the results obtained, the actual location of the dams built was used as a study area. The main conclusions of this study can be drawn as follows:
• The results show that the proposed hybrid method AHP-TOPSIS, as well as the modifications made for the adaptation of these methods to this type of problem is suitable for the selection of areas for the location of the dam with respect to the study area.
• A methodology has been proposed to take into account the blurred preferences of decision-makers when using the AHP-TOPSIS method based on the establishment of relationship intervals when assessing the importance of criteria and subcriteria. A quantitative indicator has been proposed that reflects the trend of decision makers in assessing criteria by translating utility (value) estimates
of criteria and sub-criteria into estimates of relative importance. This reduces the number of assessments by the decision maker by completing matrices of peer comparisons and ensures consistency.
• A procedure for comparing alternatives has been developed through the analysis of the subcriteria and subsequent construction of paired matrices eliminating the decision-maker as an evaluator object, something that is not contemplated within the standard methods.
• The use of MCDM provides an overview of the initial calculations to reduce expenses and arrive at a thorough study of the selection of the dam site; however, there is an evident need to collect accurate data to provide a correct assessment by the method implemented.
• To corroborate and verify the results obtained, an area in which there are dams built in advance was used as a study area.
• Correctly choosing the location of dams provides economic, social and ecological benefits. The economic benefits include a decrease in the costs allocated for direct studies on the areas, to the staff employed, as well as in services to other institutions or the purchase of support tools. The social benefits are evidenced by the improvement of the services provided by the dam being located in a hydrologically beneficial area. In addition, the construction of these dams in strategic areas allows the collection of water and the prevention of economic, social and environmental damage caused by floods. On the other hand, the ecosystem is benefited through the soil use criterion that is responsible for offering only areas of analysis that do not belong to the set of protected areas (cities, forests, industries, etc.) according to the laws of the country.
5. Recommendations for future research
1. Comparison of the results of the implementation of fuzzy TOSIS and fuzzy AHP with the results
obtained in this investigation.
2. Collect more accurate reports on the selection of the dam site to improve and update the criteria
and sub-criteria.
3. Consideration of more complete criteria can lead to more accurate results.
References
1. Noori, A., Bonakdari, H., Morovati, K., Gharabaghi, B. The optimal dam site selection using a group decision-making method through fuzzy TOPSIS model. Environment Systems and Decisions. 2018. 38 (4). Pp. 471-488. DOI: 10.1007/s10669-018-9673-x.
2. Beck, M.W., Claassen, A.H., Hundt, P.J. Environmental and livelihood impacts of dams: common lessons across development gradients that challenge sustainability. International Journal of River Basin Management. 2012. 10 (1). Pp. 73-92. DOI: 10.1080/15715124.2012.656133.
3. Fearnside, P.M. Environmental and Social Impacts of Hydroelectric Dams in Brazilian Amazonia: Implications for the Aluminum Industry. World Development. 2016. 77. Pp. 48-65. DOI: 10.1016/J.WORLDDEV.2015.08.015.
4. Shafieardekani, M., Hatami, M. Forecasting Land Use Change in suburb by using Time series and Spatial Approach; Evidence from Intermediate Cities of Iran. European Journal of Scientific Research. 2013. 116 (2). Pp. 199-208. URL: https://www.re-searchgate.net/publication/324215686_Forecasting_Land_Use_Change_in_suburb_by_using_Time_series_and_Spatial_Appro ach_Evidence_from_Intermediate_Cities_of_Iran
5. Mulatu, C., Crosato, A., Moges, M., Langendoen, E., McClain, M. Morphodynamic Trends of the Ribb River, Ethiopia, Prior to Dam Construction. Geosciences. 2018. 8 (7). Pp. 255. DOI: 10.3390/geosciences8070255.
6. Belton, V., Stewart, T.J. Multiple Criteria Decision Analysis : An Integrated Approach. Springer US, 2002.
7. Saaty, T.L. The Analytic Hierarchy Process: Planning, Priority Setting, Resource Allocation. McGraw-Hill International Book Co, 1980.
8. Hwang, C.-L., Yoon, K. Methods for Multiple Attribute Decision Making. Methods and Applications A State-of-the-Art Survey. Springer, Berlin, Heidelberg, 1981. Pp. 58-191.
9. Tsolaki-Fiaka, S., Bathrellos, G., Skilodimou, H. Multi-Criteria Decision Analysis for an Abandoned Quarry in the Evros Region (NE Greece). Land. 2018. 7 (2). Pp. 43. DOI: 10.3390/land7020043.
10. Rahman, M.A., Jaumann, L., Lerche, N., Renatus, F., Buchs, A.K., Gade, R., Geldermann, J., Sauter, M. Selection of the Best Inland Waterway Structure: A Multicriteria Decision Analysis Approach. Water Resources Management. 2015. 29 (8). Pp. 2733-2749. DOI: 10.1007/s11269-015-0967-1.
11. Afshari, A., Vatanparast, M., Cockalo, D. Application of multi criteria decision making to urban planning: A review. Journal of Engineering Management and Competitiveness. 2016. 6 (1). Pp. 46-53. DOI: 10.5937/jemc1601046A.
12. Mollalo, A., Alimohammadi, A., Shirzadi, M.R., Malek, M.R. Geographic information system-based analysis of the spatial and spatio-temporal distribution of zoonotic cutaneous leishmaniasis in Golestan Province, north-east of Iran. Zoonoses and public health. 2015. 62 (1). Pp. 18-28. DOI: 10.1111/zph.12109.
13. Mollalo, A., Sadeghian, A., Israel, G.D., Rashidi, P., Sofizadeh, A., Glass, G.E. Machine learning approaches in GIS-based ecological modeling of the sand fly Phlebotomus papatasi, a vector of zoonotic cutaneous leishmaniasis in Golestan province, Iran. Acta tropica. 2018. 188. Pp. 187-194. DOI: 10.1016/j.actatropica.2018.09.004.
14. Theilen-Willige, B., Aher, S., Gawali, P., Venkata, L. Seismic Hazard Analysis along Koyna Dam Area, Western Maharashtra, India: A Contribution of Remote Sensing and GIS. Geosciences. 2016. 6 (2). Pp. 20. DOI: 10.3390/geosciences6020020.
15. Nsanziyera, A., Rhinane, H., Oujaa, A., Mubea, K. GIS and Remote-Sensing Application in Archaeological Site Mapping in the Awsard Area (Morocco). Geosciences. 2018. 8 (6). Pp. 207. DOI: 10.3390/geosciences8060207.
16. Koohbanani, H., Barati, R., Yazdani, M., Sakhdari, S., Jomemanzari, R. Groundwater recharge by selection of suitable sites for underground dams using a GIS-based fuzzy approach in semi-arid regions. Progress in River Engineering & Hydraulic Structures. International Energy and Environment Foundation. Najaf, Iraq, 2018. Pp. 11-38.
17. Jamali, A.A., Randhir, T.O., Nosrati, J. Site Suitability Analysis for Subsurface Dams Using Boolean and Fuzzy Logic in Arid Watersheds. Journal of Water Resources Planning and Management. 2018. 144 (8). Pp. 04018047. DOI: 10.1061/(ASCE)-WR.1943-5452.0000947.
18. Dai, X. Dam Site Selection Using an Integrated Method of AHP and GIS for Decision Making Support in Bortala, Northwest China. Lund University, Lund, Sweden, 2016.
19. Tung, S.L., Tang, S.L. A comparison of the Saaty's AHP and modified AHP for right and left eigenvector inconsistency. European Journal of Operational Research. 1998. 106 (1). Pp. 123-128. DOI: 10.1016/S0377-2217(98)00353-1.
20. Atiq, M.Z., Arslan, M., Baig, Z., Ahmad, A., Tanveer, M.U., Akhtar, A., Naeem, K., Mahmood, S.A. Dam Site Identification Using Remote Sensing and GIS (A case study Diamer Basha Dam Site). International Journal of Agriculture & Sustainable Development. 2019. 1 (4). Pp. 168-178. DOI: 10.33411/ijist/2019010412.
21. Abdullah, T., Ali, S., Al-Ansari, N., Knutsson, S. Possibility of Groundwater Pollution in Halabja Saidsadiq Hydrogeological Basin, Iraq Using Modified DRASTIC Model Based on AHP and Tritium Isotopes. Geosciences. 2018. 8 (7). Pp. 236. DOI: 10.3390/geosciences8070236.
22. Nikvar Hassani, A., Katibeh, H., Farhadian, H. Numerical analysis of steady-state groundwater inflow into Tabriz line 2 metro tunnel, northwestern Iran, with special consideration of model dimensions. Bulletin of Engineering Geology and the Environment. 2016. 75 (4). Pp. 1617-1627. DOI: 10.1007/s10064-015-0802-1.
23. Nikvar Hassani, A., Farhadian, H., Katibeh, H. A comparative study on evaluation of steady-state groundwater inflow into a circular shallow tunnel. Tunnelling and Underground Space Technology. 2018. 73. Pp. 15-25. DOI: 10.1016/J.TUST.20-17.11.019.
24. Sadoughi, A., Kibert, C., Sadeghi, F.M., Jafari, S. Thermal performance analysis of a traditional passive cooling system in Dezful, Iran. Tunnelling and Underground Space Technology. 2019. 83. Pp. 291-302. DOI: 10.1016/J.TUST.2018.09.024.
25. Piadeh, F., Alavi Moghaddam, M.R., Mardan, S. Present situation of wastewater treatment in the Iranian industrial estates: Recycle and reuse as a solution for achieving goals of eco-industrial parks. Resources, Conservation and Recycling. 2014. 92. Pp. 172178. DOI: 10.1016/J.RES0DNREC.2014.06.004.
26. Piadeh, F., Alavi-moghaddam, M.R., Mardan, S. Assessment of sustainability of a hybrid of advanced treatment technologies for recycling industrial wastewater in developing countries: Case study of Iranian industrial parks. Journal of Cleaner Production. 2018. 170. Pp. 1136-1150. DOI: 10.1016/J.JCLEPRO.2017.09.174.
27. Kim, Y., Chung, E.-S., Jun, S.-M., Kim, S.U. Prioritizing the best sites for treated wastewater instream use in an urban watershed using fuzzy TOPSIS. Resources, Conservation and Recycling. 2013. 73. Pp. 23-32. DOI: 10.1016/J.RESCON-REC.2012.12.009.
28. §engül, Ü., Eren, M., Eslamian Shiraz, S., Gezder, V., §engül, A.B. Fuzzy TOPSIS method for ranking renewable energy supply systems in Turkey. Renewable Energy. 2015. 75. Pp. 617-625. DOI: 10.1016/J.RENENE.2014.10.045.
29. Zyoud, S.H., Kaufmann, L.G., Shaheen, H., Samhan, S., Fuchs-Hanusch, D. A framework for water loss management in developing countries under fuzzy environment: Integration of Fuzzy AHP with Fuzzy TOPSIS. Expert Systems with Applications. 2016. 61. Pp. 86-105. DOI: 10.1016/J.ESWA.2016.05.016.
30. Lahijanian, B., Zarandi, M.H.F., Farahani, F.V. Proposing a model for operating room scheduling based on fuzzy surgical duration. 2016 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS). IEEE, 2016. Pp. 1-5.
31. Ozcan, E.C., Ünlüsoy, S., Eren, T. A combined goal programming - AHP approach supported with TOPSIS for maintenance strategy selection in hydroelectric power plants. Renewable and Sustainable Energy Reviews. 2017. 78. Pp. 1410-1423. DOI: 10.1016/J.RSER.2017.04.039.
32. Onüt, S., Soner, S. Transshipment site selection using the AHP and TOPSIS approaches under fuzzy environment. Waste Management. 2008. 28 (9). Pp. 1552-1559. DOI: 10.1016/J.WASMAN.2007.05.019.
33. Mulliner, E., Malys, N., Maliene, V. Comparative analysis of MCDM methods for the assessment of sustainable housing affordability. Omega. 2016. 59. Pp. 146-156. DOI: 10.1016/J.OMEGA.2015.05.013.
34. Balioti, V., Tzimopoulos, C., Evangelides, C. Multi-Criteria Decision Making Using TOPSIS Method Under Fuzzy Environment. Application in Spillway Selection. Proceedings. 2018. 2 (11). Pp. 637. DOI: 10.3390/proceedings2110637.
35. Jozaghi, A., Alizadeh, B., Hatami, M., Flood, I., Khorrami, M., Khodaei, N., Ghasemi Tousi, E. A Comparative Study of the AHP and TOPSIS Techniques for Dam Site Selection Using GIS: A Case Study of Sistan and Baluchestan Province, Iran. Geosciences. 2018. 8 (12). Pp. 494. DOI: 10.3390/geosciences8120494.
36. Esavi, V., Karami, J., Alimohammadi, A., Niknezhad, S.A. Comparison the AHP and FUZZY-AHP Decision Making Methods in Underground DAM Site Selection in Taleghan Basin. Journal of Geoscience. 2012. 22 (85). Pp. 27-34. DOI: 10.22071/GSJ.2012.54018.
37. Yasser, M., Jahangir, K., Mohmmad, A. Earth dam site selection using the analytic hierarchy process (AHP): a case study in the west of Iran. Arabian Journal of Geosciences. 2013. 6 (9). Pp. 3417-3426. DOI: 10.1007/s12517-012-0602-x.
38. Tzimopoulos, C., Balioti, V., Evangelides, C. Fuzzy multi-criteria decision making method for dam selection. Proceedings of the 13th International Conference on Environmental Science and Technology, Athens, Greece 2013. Pp. 5-7.
39. Rezaei, P., Rezaie, K., Nazari-Shirkouhi, S., JamalizadehTajabadi, M.R. Application of Fuzzy Multi-Criteria Decision Making Analysis for evaluating and selecting the Best Location for Construction of Underground Dam. Acta Polytechnica Hungarica. 2013. 10 (7). Pp. 187-205. DOI: 10.12700/aph.10.07.2013.7.13
40. Barkhordari, J. The pre-selection of suitable sites for small underground dams in arid areas using GIS (A case study in Yazd_Ardakan watershed). International Geoinformatics Research and Development Journal. 2015. 6 (1). Pp. 18-27.
41. Minatour, Y., Khazaei, J., Ataei, M., Javadi, A.A. An integrated decision support system for dam site selection. Scientia Iranica, Transactions A: Civil Engineering. 2015. 22 (2). Pp. 319-330.
42. Choo, T.H., Ahn, S.H., Yang, D.U., Yun, G.S. A Study on the Estimating Dam Suitable Site based on Geographic Information using AHP. 6th International Conference on Developments in Engineering and Technology (ICDET-2017) 2017. Pp. 50-56.
43. Njiru, F.M., Siriba, D.N., Njiru, F.M., Siriba, D.N. Site Selection for an Earth Dam in Mbeere North, Embu County—Kenya. Journal of Geoscience and Environment Protection. 2018. 06 (07). Pp. 113-133. DOI: 10.4236/gep.2018.67009.
44. Aguaron, J., Moreno-Jiménez, J.M. The geometric consistency index: Approximated thresholds. European Journal of Operational Research. 2003. 147 (1). Pp. 137-145. DOI: 10.1016/S0377-2217(02)00255-2.
45. Tutygin, A.G., Korobov, V.B., Menshikova, T.V. Problems of consistency of expert judgments in the method of hierarchy analysis. Bulletin of civil engineers. 2019. 76 (5). Pp. 291-297. DOI: 10.23968/1999-5571-2019-16-5-291-297.
46. Djenadic, S., Jovancic, P., Ignjatovic, D., Miletic, F., Jankovic, I. Analysis of the application of multi-criteria methods in optimizing the selection of hydraulic excavators on open-cast coal mining. Tehnika. 2019. 74 (3). Pp. 369-377. DOI: 10.5937/TEHNIKA1903369D.
47. Ibrahim, G.R.F., Rasul, A., Hamid, A.A., Ali, Z.F., Dewana, A.A. Suitable site selection for rainwater harvesting and storage case study using Dohuk governorate. Water (Switzerland). 2019. 11 (4). DOI: 10.3390/w11040864
48. Xiao, Q., Shan, M., Xiao, X., Rao, C. Evaluation Model of Industrial Operation Quality Under Multi-source Heterogeneous Data Information. International Journal of Fuzzy Systems. 2020. 22 (2). Pp. 522-547. DOI: 10.1007/s40815-019-00776-x.
49. Konstantinos, I., Georgios, T., Garyfalos, A. A Decision Support System methodology for selecting wind farm installation locations using AHP and TOPSIS: Case study in Eastern Macedonia and Thrace region, Greece. Energy Policy. 2019. 132. Pp. 232-246. DOI: 10.1016/J.ENPOL.2019.05.020.
50. Katarzhina, H. Selecting a technology using the method TOPSIS. Foresight and STI Governance. 2020. 14 (1). Pp. 85-96. DOI: 10.17323/2500-2597.2020.1.85.96.
51. Kazazi Darani, S., Akbari Eslami, A., Jabbari, M., Asefi, H. Parking Lot Site Selection Using a Fuzzy AHP-TOPSIS Framework in Tuyserkan, Iran. Journal of Urban Planning and Development. 2018. 144 (3). Pp. 04018022. DOI: 10.1061/(asce)up.1943-5444.0000456.
52. Moges, G., Bhole, V. Morphometric Characteristics and the Relation of Stream Orders to Hydraulic Parameters of River Goro: An Ephemeral River in Dire-dawa, Ethiopia. Universal Journal of Geoscience. 2015. 3(1). Pp. 13-27. DOI: 10.13189/ujg.2015.030102.
53. Shumie, M.C. River Slope and Roughness Impact on Downstream Hydraulic Structures. Journal of Earth Science & Climatic Change. 2018. 9 (11). Pp. 1-7. DOI: 10.4172/2157-7617.1000500.
54. Solodovnikov, A.B. Methods of estimating the time of concentration in small watersheds. Design of development of regional railway network: journal of science. 2018. (6). Pp. 32-57. URL: https://elibrary.ru/download/elibrary_36408133_12154366.pdf
55. Solodovnikov, A.B. Description of rational method of calculation of rain drain. Design of development of regional railway network: journal of science. 2014. (2). Pp. 93-101. URL: https://www.elibrary.ru/item.asp?id=22706011
56. Ponkina, E., Rossova, A., Herpsumer, K., Bondarovich, A., Schmidt, G., Illiger, P. Approaches to the assessment of potential evapotranspiration. Proceedings of the seminar on Geometry and Mathematical Modeling. 2019. (5). Pp. 125-133. URL: http://journal.asu.ru/psgmm/article/view/7251
Information about authors:
Solangel Rodriguez Vazquez,
ORCID: https://orcid.org/0000-0001-6250-1108 E-mail: solrusita85@gmail. com
Natalia V. Mokrova,
Doctor in Technical Science
ORCID: https://orcid.org/0000-0002-8444-2935
E-mail: natali vm@mail.ru
Received 04.02.2021. Approved after reviewing 24.01.2022. Accepted 16.02.2022.
