Siting MSW landfills using MCE methodology in GIS environment (Case study: Birjand plain, Iran)

Waste Management xxx (2015) xxx–xxx

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Sitting MSW landfills using MCE methodology in GIS environment (case study: Birjand plain, Iran) Zeynab Karimzadeh Motlagh, Mohammad Hossein Sayadi ⇑ Environmental Sciences Department, University of Birjand, Birjand, Iran

a r t i c l e

i n f o

Article history: Received 16 March 2015 Revised 11 July 2015 Accepted 10 August 2015 Available online xxxx Keywords: Municipal solid waste landfill Site selection Ordered Weighted Averaging Weighted Linear Combination Analytical Network Process

a b s t r a c t The rapid municipal solid waste growth of Birjand plain causes to find an appropriate site selection for the landfill. In order to reduce the negative impacts of waste, the use of novel tools and technologies to gain a suitable site for landfill seems imperative. The present paper aimed to exhibits the Multi Criteria Evaluation (MCE) for the landfill site selection of the Birjand plain because till date a suitable action has not been implicated. In the present research, the parameters such as environmental and socio-economical factors have been used. The factors like slope, water resources, soil parameters, landuse, fault and protected areas in the model of effective environmental criteria and the factors viz. distance from road, urban areas, village, airport, historical place, and industries in the model of socio-economic criteria were investigated and with the use of Weighted Linear Combination (WLC) and Analytical Network Process (ANP) models were compounded and according to the Ordered Weighted Averaging (OWA) and Fuzzy Linguistic Quantifier (LQ) were aggregated. The paper focuses on the OWA method as well as an approach for integrating Geographic Information System (GIS) and OWA. OWA has been developed as a generalization of multi-criteria combination. In this study we attained comparable data via the technique of ANP and five scenarios of OWA method were used. The results of field studies, fifth scenario for the study area proposed. Based on the research findings, OWA method had a great potential and flexibility in the modeling of the complex decision-making problems. Ó 2015 Elsevier Ltd. All rights reserved.

1. Introduction The urbanized population growth along with the increase in the per capita consumption and waste production possesses the highest effect on the destruction of the environment; therefore waste production plays a vital role in polluting waters and open spaces. Today with drastic population growth and as its outcome the increased waste and extreme environmental pollution via human activities, the selection of a suitable site for the sanitary waste disposal is an unavoidable affair. The rapid population growth of Birjand plain has led to an ever-increasing proliferation of the waste and expansion of industries and urban development which in turn has made the subject of urban waste collection and disposal a complex issue. On the other hand, the special condition of this city increases the need of an appropriate site selection for the landfill. In order to reduce the negative impacts of municipal solid waste in Birjand plain (Sayadi et al., 2015a, 2015b), the use of novel tools and technologies to gain a suitable site for landfill seems ⇑ Corresponding author. E-mail addresses: (M.H. Sayadi).

[email protected],

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imperative. The site selection for landfill is associated with specific factors which encounter a special importance and even produces limitations in the selection. The ultimate goal of the project is find the optimal location that has the lowest possible detrimental environmental and socio-economical impacts on the natural environment surrounding the landfill. Therefore, the site selection process was implemented with emphasis on the complete information of the area to assure that a viable establishment of the landfill site has taken place. On the other hand, the course of utilizing this information and decision-making in the landfill site selection is an important issue and has an abundance of importance (Shepard, 2005). Therefore, in the selection of a suitable site, the attempt is made to choose an optimal option which in turn generates the least possible undesirable effects. For this reason it is required to make use of a special tool for the determination of the best site. For the site selection in GIS systems the effective factors, criteria and constraints availed as map layers should be processed and analyzed. In other words, in the project execution of a suitable sanitary landfill site selection in each area, the different environmental and socio-economical aspects should be regarded and considering these aspects the selection of a suitable site should be ventured.

http://dx.doi.org/10.1016/j.wasman.2015.08.013 0956-053X/Ó 2015 Elsevier Ltd. All rights reserved.

Please cite this article in press as: Motlagh, Z.K., Sayadi, M.H. Sitting MSW landfills using MCE methodology in GIS environment (case study: Birjand plain, Iran). Waste Management (2015), http://dx.doi.org/10.1016/j.wasman.2015.08.013

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Several studies carried out to find the suitable site for disposal of solid wastes with help of the Multi Criteria Decision Analysis (MCDA) and GIS. (Ajit et al., 2008; Abessi and Saeedi, 2009; Nas et al., 2010; Pires et al., 2011; Jafari et al., 2012; Demesouka et al., 2013; Davami et al., 2014; Beskese et al., 2014; Khan and Samadder, 2014). Salman Mahini and Gholamalifard (2006) and Moeinaddini et al. (2010), carried out the landfill site selection and for the weighted overlay used Weighted Linear Combination (WLC). Javaheri et al. (2006) accomplished Analytical Hierarchy Process (AHP). Khan and Faisal (2008) in a research using Analytical Network Process (ANP) measured the alternatives comparative suitability of urban solid waste disposal by employing Super matrix and value judgments of individual decision makers approaches. They reported that ANP is analytically flexible and assists decision makers to find the best possible solution for complex issues via decision issue analysis in the model of an ordered network from the internal and different peculiarities terms. Bottero et al. (2011) considered the Analytical Hierarchy Process and Analytical Network Process in the site selection. Yal and Akgun (2014) in Turkey did not consider the important factors such as water sources, urban areas and the soil parameters. However, in the present research, an attempt was made to use the entire factors of the case study area for the effective completion of the works carried out. Similarly, to search a suitable landfill area, Isalou et al. (2012), depicted the fuzzy logic integrated development and Analytical Network Process (ANP) in a region of Iran. Their findings demonstrated that the integration of fuzzy logic and ANP can provide better ideas in comparison to the other models such as ANP and fuzzy logic. Akbari et al. (2008), Curcic et al. (2011) and Rezazadeh et al. (2014) utilized the fuzzy logic. Whereas, Isalou et al. (2012) carried out the landfill site selection for the city of Qom located in Iran using fuzzy logic and Analytical Network Process (F-ANP). A limited research has been carried out in reference to the Fuzzy OWA algorithm (Zarghami et al., 2008; Boroushaki and Malczewski,

2010; Amiri et al., 2013). In these researches, the approach of Yager (1988) was used, for an example Ferretti and Pomarico (2013) exhibited MCE approach development and advantages of the OWA approach through the different scenarios in a region of Europe. They showed that OWA scenarios are dependent on the quality of risk acceptance level (optimistic, pessimistic and neutral) and are involved in the decision-making process for the better facility and understanding of the patterns that originate from the decision-making displacements. The results of their research studies exhibited that the aforementioned approach is an effective tool to deliver support as well as stability assessment of the space decision-making programs. The aim of the present research was to employ Multi Criteria Evaluation (MCE) emphasizing on the ANP algorithm and OWA in the landfill site selection considering the complexity of multi criteria decision-making issues. 2. Background information The Birjand plain in the southern Khorasan province situated in Iran with an area of around 342,500 hectares was the case study confine located in geographical coordinates; 58°400 –59°400 E longitude and 32°400 –33°100 N latitude (Fig. 1). The average elevation of this area above the sea level is approximately 1800 m. The population of Birjand city based on the 2011 census was 178,020 individuals that daily produce around 192 tons of domestic wastes. The transported waste is 114 tons per day. 3. Methodology In the present research, for the site selection of suitable landfill sites via MCE method, the effective factors; criteria (objectives and attributes) and constraints were prepared as map layers and the entire maps containing format, projection system (UTM-40N), were equated based on the column and row numbers and pixel size

Fig. 1. The case study area location in the southern Khorasan province situated in Iran.

Please cite this article in press as: Motlagh, Z.K., Sayadi, M.H. Sitting MSW landfills using MCE methodology in GIS environment (case study: Birjand plain, Iran). Waste Management (2015), http://dx.doi.org/10.1016/j.wasman.2015.08.013

Z.K. Motlagh, M.H. Sayadi / Waste Management xxx (2015) xxx–xxx

3

Fig. 2. Land cover map.

Fig. 3. Land cover classification map.

(Malczewski, 1999). For the preparation and reclassification of some of the research needed layers, the raster Digital Elevation Model (DEM) layer extracted from the ASTER satellite projections in ENVI software was used. After the preparation of the required vector layers from the land use planning project of South Khorasan province which has been made by university of Birjand, the entire layers were converted to raster format. For example, the map of land cover types and their evaluation in Figs. 2 and 3 and Table 1 is shown. Later, for carrying out MCE method, the entire raster layers with ASCII format were fed into the IDRISI software environment and the required analysis was carried out for the preparation of the fuzzy layers maps, later for the weighting of layers, the ANP algorithm and Super Decision software was used. In the present research, for the site selection of suitable landfill sites, the analysis consists in using ANP for eliciting criterion weights, criterion maps standardization is performed using Fuzzy Membership Functions and OWA with linguistic quantifiers is performed as decision rule. Finally, the suitability map for the

landfill site selection was obtained. The performed research stages are described as follows: 3.1. Determination of factors and constraints The assessment criteria set that included the factors and constraints, were determined (Tables 2 and 3). A factor is a criterion that enhances or detracts from the suitability of a specific alternative for the activity under consideration. A constraint serves to limit the alternatives under consideration constraints classify the areas into two classes: unsuitable (value 0) or suitable (value 1) (Salman Mahini and Gholamalifard, 2006). 3.2. Standardization of factors and analysis of constraints Each map layer criterion (attribute map) was introduced as a standard, the standardization of factors was carried out based on the fuzzy logic in bytes scale (0–255) and the analysis of

Please cite this article in press as: Motlagh, Z.K., Sayadi, M.H. Sitting MSW landfills using MCE methodology in GIS environment (case study: Birjand plain, Iran). Waste Management (2015), http://dx.doi.org/10.1016/j.wasman.2015.08.013

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Table 1 Land cover classes of study area. Class name in information layer

Land cover types

Land cover classes

SH PF R1 R2 R3 BL

Woodland and shrubbery Forest under cultivation Dense pastures Semi-dense pastures Less dense pasture Land without vegetation and stony protrusion Clay pit Aqua farming and orchards Dry farming Riverbed Residential areas

Forest/wood land Range land

TK IF DF RB ST

Barren land

Constraints

Span with zero value

Span with 1 value

Riverbed Wells, Springs, Kanats Fault Buffer urban Rural areas Airport

Less Less Less Less Less Less

More than 250 m More than 250 m More than 100 m More than 3000 m More than 800 m Outside of airport and 3000 m area surrounding it

250 m 250 m 100 m 3000 m 800 m 3000 m

S¼

n X

W iXi

Y

Ci

ð1Þ

i¼1

In this relation S: landuse suitability, Wi: factor weight, Xi: fuzzy factor value and Ci: criterion limitation point. 3.4. Factors weight determination

Agricultural area Water bodies Settlements

Table 2 The constraints of landfill site selection.

than than than than than than

Linear Combination (WLC) method is used for the layering of raster layers and layers compilation and as Eq. (1) are integrated in a linear manner.

constraints was carried out based on the Boolean logic (0, 1). The Fuzzy theory, introduced for the first time by Lotfi Zadeh (Zadeh, 1965), has established several applications in different engineering fields viz. for the values with ambiguous information. In this model, in contrast to the Boolean model, suitable absolute and unsuitable absolute units were not considered. For this reason, the given weights are neither zero nor one, but it is variable between these values. For carrying out the fuzzy logic 4 Membership function type, sigmoidal (S-Shape), J-Shape, Linear and User defined were observed in the IDRISI software and the curve type fuzzy membership was in monotonically increasing, monotonically decreasing and symmetric configurations (Fig. 4). We used the sigmoidal function (see Fig. 4 and Table 3) because it is more commonly used. When the relationship between the value and the fuzzy membership does not follow any of the sigmoidal function, the user-defined function is the most applicable. Control points in the sigmoidal function are shown in Fig. 4. When ‘‘monotonically increasing” or ‘‘monotonically decreasing” curves are chosen, only two control points are needed to define the fuzzy set membership function. In the first case, they are point a and point b, while in the second case they are point c and point d. When the ‘‘symmetric” curve is chosen, however, all four control points are needed and must be entered in the following order: a, b, c and d (Eastman, 2003). For example, ‘‘Distance from fault” (Table 3) with a sigmoidal function has values of a = 100 m and b = 1000 m. The map layers were standardized by the fuzzy approach relevant to the criteria. Output data format in IDRISI is real (0–1 value range) or byte (0–255). The latter is mainly for use with modules such as MCE that require byte data format (Eastman, 2003). The presented method evaluates the entire study area using a grading scale from 0 to 255 (Byte Scale), where 0 denotes a site fully unsuitable for landfill while 255 shows a site optimum for landfill. 3.3. Factors and constraints integration, WLC method At the end of the criteria preparative stage and extraction of constraints and indexes (objectives and attributes), the Weighted

In the present research, with the use of ANP method which is a component of the Multi Criteria Decision-making Analysis (MCDA) methods, a model for the landfill site selection was based. Analytical Network Process (ANP) is a mathematical theory that was involved as a systematic with varied dependences and has been successfully employed in different fields. This method was developed via Saaty (1996) to avail priorities for the decisions without the formation of an assumption about the one sided terms of Analytical Hierarchy Process (AHP) between the decision levels. The ANP method based on the human brain analysis of the complex issues was introduced as a non-categorized structure to modify the AHP method. In this method for an issue modeling, a network is drawn in which the existing knots are equivalent to goal, criteria (objectives and attributes) and alternatives. The directional vectors that attach these knots with each other, indicate the influences of knots directions on one another. ANP technique with comprehensive and pervasive framework can consider the entire interactions and relations between the decision-making levels to form a network structure. The clusters relate the decisionmaking levels and arrows depict the interactions between these levels. The direction of arrows determines the dependence. As it is observed, the Analytical Hierarchy Process (AHP) structure is a specific and special state of network structure. Fig. 5 shows the comparison between Analytical Hierarchy Process (AHP) and Analytical Network Process (ANP) structures. In the present research, the course of criteria and sub-criteria selection and employment of Analytical Network Process (ANP) was as follows: According to Figs. 6 and 7, the criteria and sub-criteria (or attributes) were identified and the model was designed and it states that the ANP model is made up of 4 levels. The first level relates to goal and second level relates to the criteria (objectives and attributes) and between these factors the internal and external terms exist. Sub-criteria (attributes) were in the third level of the model; this level consisted of secondary factors for each of the main environmental and socio-economical factors. Besides, the last level indicates the considered factors. The clusters introduce decision-making levels and the arrows show the interactions between decision-making levels. The direction of arrows determines the dependence. The main innovation of the ANP is its network structure, which enables interactions between elements situated in different clusters and dependencies between the elements in the same cluster to be taken into account (Tseng, 2009; Azizi et al., 2014). The conceptual model (Fig. 6) is constructed and the relationships (shown by arrows in the conceptual model) between/among clusters, and nodes are determined. In the modeling stage, the goal of decision-making, decisionmaking indexes (objectives and attributes) and the possible option were determined. Through the Paired Comparison (PC) the criteria (objectives and attributes) and sub-criteria (attributes) comparative weight can be determined. The elements paired comparison in each level considering its comparative importance in relation to control criterion, was carried out similar to AHP method. Criteria are compared, using Super Decisions software, in the

Please cite this article in press as: Motlagh, Z.K., Sayadi, M.H. Sitting MSW landfills using MCE methodology in GIS environment (case study: Birjand plain, Iran). Waste Management (2015), http://dx.doi.org/10.1016/j.wasman.2015.08.013

Criterion

Sub-criterion

Environmental factors

Hydrology

Geomorphology

Factor

Suitability

Membership function shape and type

Control points

 Distance from ground waters (Wells, Springs and Kanats)

250–2500 equivalent to 0–255

Increasing – S-Shape

 Distance from surface waters (Rivers)

250–5000 equivalent to 0–255

Increasing – S-Shape

 Soil texture

User defined User defined

–

 Slope

Granular loamy texture, sandy loamy, deep sandy and heavy clay, equivalent to 0–255 Very low depth, low depth to semi deep, semi deep, semi deep to deep, very deep, equivalent to 0–255 3–40% equivalent to 0–255

a = 250 b = 2500 a = 250 b = 2500 –

Reducing – S-Shape

 Distance from fault

100–1000 equivalent to 0–255

Increasing – S-Shape User defined

c=3 d = 40 a = 100 b = 1000 –

 Soil depth

Socio-economic factors

Land use

Land use

Protected areas

Distance from the protected areas

 (Riverbed, forest under cultivation, Residential areas) equivalent to 0  (Aqua farming and orchards, dry farming, dense pastures, woodland and shrubbery) equivalent to 25  (Semi-dense pastures) equivalent to 120  (Land without vegetation and stony protrusion, less dense pasture) equivalent to 225  (Clay pit) equivalent to 255 500–1000 m equivalent to 0–255

Accessibility

Distance from urban areas

3000–10,000 m equivalent to 0–255

Increasing – J-Shape

Distance from villages

800–3000 m equivalent to 0–255

Increasing – J-Shape

Distance from main road

300–2000 m equivalent to 0–255

Reducing – J-Shape

Distance from airport

3000–7000 m equivalent 0–255

Increasing – Linear

Distance from historical centers

500–3000 m equivalent to 0–255

Increasing – Linear

Distance from industries

1000–6000 m equivalent to 0–255

Increasing – Linear

Increasing – S-Shape

a = 500 b = 1000 a = 3000 b = 10,000 a = 800 b = 3000 a = 300 b = 2000 a = 3000 b = 7000 a = 500 b = 3000 a = 1000 b = 6000

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Table 3 Factors and their suitability rate and membership function type for landfill site selection.

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Fig. 4. Control points in sigmoidal functions (1 – monotonically increasing, 2 – monotonically decreasing, 3 and 4 – symmetric curves) (Eastman, 2003).

whole network in order to form an unweighted supermatrix by pairwise comparisons (the same as the AHP). In this phase, decision makers compare two elements. Pairwise comparisons are made with the grades ranging from 1 to 9. A reciprocal value of each number is used to express the inverse comparison. The values of pairwise comparisons are allocated in the comparison matrix and local priority vector is derived from eigenvector. Consistency of pairwise matrix like the AHP must be less than 0.1. In this stage, due to a reduction in the volume of calculations, only the environmental factors binary matrix comparison (Table 4) is shown. In the next stage, since there was dependence between the criteria (objectives and attributes) and sub-criteria (attributes), the internal dependencies and inner weights of criteria and sub-criteria determined in the modeling stage, were calculated. At this stage, the internal dependencies and the feedback were considered.

Supermatrix was used to analyze the internal dependencies between the system components. The supermatrix components obtained from the matrixes paired comparison of the internal dependencies were replaced in it. Any value except zero in the supermatrix column indicates the obtained comparative weight importance from the paired comparison matrixes of the internal dependencies. A supermatrix in reality is a component-classified matrix in which each matrix section shows the relationship between two (decision-making level) in the total decision issue. The standard form is a supermatrix that is introduced via Saaty (1996), in Fig. 8, it is visible that C indicates decision-making levels and e indicates the elements inside these levels. Moreover, the W vectors inside matrix are the resultant weighted vectors from paired comparisons of decision-making level elements with one another. As it was mentioned, each of the model clusters in the ANP modeling, possess three configurations unweighted supermatrix (matrix containing priorities obtained from binary comparison), weighted supermatrix (matrix element is multiplied by the cluster weight) and limit supermatrix (obtained from exponential of a weighted matrix till the period that the entire elements become equal and reach an answer), that are exhibited in Tables 5–7. Finally, the importance degree of factors, which is the resultant of this study, is presented in Fig. 11 and Table 8.

3.5. OWA method The Ordered Weighted Averaging (OWA) method, as a generalization of the Boolean coverage operations and Weighted Linear Combination (WLC) has been developed by Yager (1988). The

Fig. 5. Comparison of the Analytical Hierarchy Process (AHP) and Analytical Network Process (ANP) structures (Saaty, 1996). (a) Analytical Network Process. (b) Analytical Hierarchy Process.

Fig. 6. The designed ANP model.

Please cite this article in press as: Motlagh, Z.K., Sayadi, M.H. Sitting MSW landfills using MCE methodology in GIS environment (case study: Birjand plain, Iran). Waste Management (2015), http://dx.doi.org/10.1016/j.wasman.2015.08.013

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Z.K. Motlagh, M.H. Sayadi / Waste Management xxx (2015) xxx–xxx

Fig. 7. The designed ANP model in the Super Decision software.

Table 4 Environmental factors binary comparison matrix.

Hydrology Geomorphology Landuse Protected area

Hydrology

Geomorphology

Landuse

Protected area

Weight

1

1.8 1

3 1.66 1

1.28 0.71 0.42 1

0.374 0.208 0.125 0.291

Fig. 8. The general structure of Super matrix (Saaty, 1996).

Please cite this article in press as: Motlagh, Z.K., Sayadi, M.H. Sitting MSW landfills using MCE methodology in GIS environment (case study: Birjand plain, Iran). Waste Management (2015), http://dx.doi.org/10.1016/j.wasman.2015.08.013

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Z.K. Motlagh, M.H. Sayadi / Waste Management xxx (2015) xxx–xxx

Table 5 Unweighted Super matrix obtained in the Super Decision software. Cluster node labels

1Goal

2Criteria

Landfill site selection

Environmental factors

3Subcriteria Socio economical factors

Access

Geomorphology

4Alternatives Hydrology

Landuse

Protected areas

Airport

Fault. . .

Super Decisions Main Window: 1.mod:formulaic: Unweighted Super matrix 1Goal

Landfill site selection

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

2Criteria

Environmental factors Socio economical factors

0.692

0.528

0.471

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.307

0.471

0.528

0.000

0.000

0.000

0.000

0.000

0.000

0.000

3Subcriteria

Access Geomorphology Hydrology Landuse Protected areas

0.000 0.000 0.000 0.000 0.000

0.000 0.208 0.374 0.125 0.291

0.000 0.000 0.000 0.000 0.000

0.313 0.117 0.352 0.039 0.176

0.233 0.0399 0.359 0.119 0.248

0.279 0.154 0.358 0.0399 0.167

0.280 0.120 0.359 0.040 0.199

0.318 0.090 0.408 0.136 0.045

0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000

4Alternatives

Airport Fault

0.000 0.000

0.000 0.000

0.000 0.000

0.147 0.000

0.000 0.125

0.000 0.000

0.000 0.000

0.000 0.000

0.000 0.000

0.000 0.000

Socio economical factors

Access

Hydrology

Landuse

Protected areas

Airport

Table 6 Weighted Super matrix obtained in the Super Decision software. Cluster node labels

1Goal

2Criteria

Landfill site selection

Environmental factors

3Subcriteria Geomorphology

4Alternatives Fault. . .

Super Decisions Main Window: 1.mod:formulaic: Weighted Super matrix 1Goal

Landfill site selection

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

2Criteria

Environmental factors Socio economical factors

0.692

0.264

0.471

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.307

0.236

0.528

0.000

0.000

0.000

0.000

0.000

0.000

0.000

3Subcriteria

Access Geomorphology Hydrology Landuse Protected areas

0.000 0.000 0.000 0.000 0.000

0.000 0.104 0.187 0.062 0.146

0.000 0.000 0.000 0.000 0.000

0.156 0.059 0.176 0.019 0.088

0.116 0.020 0.179 0.060 0.124

0.139 0.077 0.179 0.019 0.083

0.140 0.060 0.179 0.020 0.099

0.159 0.045 0.204 0.068 0.022

0.000 0.000 0.000 0.000 0.000

0.000 0.000 0.000 0.000 0.000

4Alternatives

Airport Fault

0.000 0.000

0.000 0.000

0.000 0.000

0.073 0.000

0.000 0.062

0.000 0.000

0.000 0.000

0.000 0.000

0.000 0.000

0.000 0.000

Table 7 Limit Super matrix obtained in the Super Decision software. Cluster node labels

1Goal

2Criteria

Landfill site selection

Environmental factors

3Subcriteria Socio economical factors

Access

Geomorphology

4Alternatives Hydrology

Landuse

Protected areas

Airport

Fault. . .

Super Decisions Main Window: 1.mod:formulaic: Weighted Super matrix 1Goal

Landfill site selection

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

0.000

2Criteria

Environmental factors Socio economical factors

0.179

0.179

0.179

0.179

0.179

0.179

0.179

0.179

0.179

0.179

0.186

0.186

0.186

0.186

0.186

0.186

0.186

0.186

0.186

0.186

3Subcriteria

Access Geomorphology Hydrology Landuse Protected areas

0.066 0.051 0.130 0.031 0.071

0.066 0.051 0.130 0.031 0.071

0.066 0.051 0.130 0.031 0.071

0.066 0.051 0.130 0.031 0.071

0.066 0.051 0.130 0.031 0.071

0.066 0.051 0.130 0.031 0.071

0.066 0.051 0.130 0.031 0.071

0.066 0.051 0.130 0.031 0.071

0.066 0.051 0.130 0.031 0.071

0.066 0.051 0.130 0.031 0.071

4Alternatives

Airport Fault

0.008 0.004

0.008 0.004

0.008 0.004

0.008 0.004

0.008 0.004

0.008 0.004

0.008 0.004

0.008 0.004

0.008 0.004

0.008 0.004

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Z.K. Motlagh, M.H. Sayadi / Waste Management xxx (2015) xxx–xxx Table 8 Final factors weight for the landfill site selection. Goal

Criterion

Sub-criterion

Factor

Landfill site selection

Environmental factors

Hydrology

Distance from Distance from Soil texture Soil depth Slope Distance from Landuse Distance from Distance from Distance from Distance from Distance from Distance from Distance from

Geomorphology

Socio-economical factors

Landuse Protected area Accessibility

Fig. 9. Decision Strategy Space in the Ordered Weighted Average (Eastman, 2012).

weighted averaging method is a complete spectrum of space strategy decision that in an extension delivers the primary gradation dimensions between the involved criteria and risk measure in the solution. Fig. 9 demonstrates the Decision Strategy Space in which x axis indicates a circuit of maximum caution that is extended from the point that nil kind of risk exists in it to the point where the risk factor has been accepted in a complete manner and y axis indicates a circuit of trade-off between criteria that extends from the point that has nil trade-off toward the criterion with the highest trade-off measure (Eastman, 2012). The trade-off is a degree that a criterion can compensate the other criteria. Weighted average method is an interesting method since with change in criterion’s order and parameters an expansive spectrum of different maps and predictive forms are produced. The use of this method permit is obtained the wide spectrum evaluation of different management strategies (Ferretti and Pomarico, 2013). The weighted average method consists of two weight sets: relative criterion and order weight, the first collection, factor or criterion weight, controls the relative share of a criterion, whereas the second collection, order weight, controls the criteria set. Through determination of a suitable set of order weight it is possible to produce a wide spectrum of result maps (strategy decision) which indicates the obtained results from different attitudes of decision-taker in relation to the risk (Malczewski, 2006b). The order weights allow for control of the degree of trade-off among criteria, thus providing control of the degree of optimism (attitude to risk), allowed into the planning process (Malczewski, 1999; Makropoulos and Butler, 2006). If [w1, w2, . . ., wn] is the set of ordered weighting factors, then the elements of the set can be assigned values ranging from [1, 0, . . ., 0] which is the MIN fuzzy operator (the logical AND), to [0, 0, . . ., 1], which is the MAX fuzzy

Weight the underground water sources (Wells, Springs and Kanats) the surface waters (Rivers)

fault the protected area urban areas villages main road airport historical areas industries

0.171 0.134 0.093 0.111 0.025 0.015 0.073 0.168 0.052 0.046 0.037 0.029 0.026 0.014

operator (the logical OR). In the first extreme case, the criteria are compared at each point (or spatial location) and the largest value is selected as the composite value. The rest of the criteria are given a value of 0. This constitutes an optimistic approach, thus incorporating maximum risk-taking. At the other extreme, the lowest criterion value is selected as the composite value. This is evidently a pessimistic (low risk taking) approach. Both these extreme cases involve no trade-off among criterion scores. In between, there exists a large number of alternative ordering sets with varying degrees of trade-off. A special case is when the set is [1/n, 1/n, . . ., 1/n], where n is the number of criteria to be considered. This case results in maximum trade-off between criterion values. A high value (score) in one would compensate for a low value (score) in another criterion in the same point (x, y). A measure of ANDness, ORness and trade-off, associated with a particular set of weights can be obtained by the following equations (Yager, 1988; Makropoulos and Butler, 2006). In the continuum of stages, implementation of the aforementioned method is described: 3.5.1. First step According to the WLC method, the assessment criteria sets are determined, each map layer criterion (attribute map) is brought forward as a standard, the obtained criterion weights from the Analytical Hierarchical process is determined. 3.5.2. Second step Through the Fuzzy Linguistic Quantifier (LQ) the multi criteria assessment OWA is carried out which consists of three main stages: determination of the kind of linguistic quantifier Q, production of a group of the Order weights related to Q and location assessment of each cell with the use of combinational function OWA. The Linguistic Quantifier has provided an ability to convert the linguistic quotations into the mathematical terms and in general two kinds of linguistic quantifier exist: absolute linguistic quantifier and relative linguistic quantifier (Malczewski and Rinner, 2005; Yager, 1996). It cannot be exactly stated which of the linguistic quantifiers are a suitable concept for the multi criteria assessment (Malczewski, 2006a). In this research, the relative linguistic quantifier according to Table 9 was selected among the Regular Increasing Monotone (RIM) linguistic quantifier. For defining these linguistic quantifiers the below equation is used:

QðpÞ  Q ðpÞ ¼ Pa ; a > 0

ð2Þ

With the change of feature a, the different types of quantifier and their operators can be obtained. If a = 1, Q(p) will be proportionate with a and accordingly correspondent to ‘half’ quantifier. With tendency of a toward zero, the quantifier Q(p) will be the indicator of one of the limits (‘At least one’) that is equivalent to operator

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Z.K. Motlagh, M.H. Sayadi / Waste Management xxx (2015) xxx–xxx

Table 9 The used linguistic quantifiers and correspondent a. Linguistic quantifier (LQ)

At least one

Few

Some

Half

Many

Most

All

a

0.0001 OR(MAX) Extremely optimistic

0.1 – Very optimistic

0.5 – Optimistic

1 WLC Neutral

2 – Pessimistic

10 – Very pessimistic

1000 AND(MIN) Extremely pessimistic

Combinational strategy Decision-making strategy

Table 10 Order weights obtained from the fuzzy linguistic quantifier Q. (a) Decision Strategy Space Order weights t1 t2 t3 t4 t5 t6 t7 t8 t9 t10 t11 t12 t13 t14 P

a = 0.0001

a = 0.1

a = 0.5

a=1

a=2

a = 10

a = 1000

Scenario 1

Scenario 2

Scenario 3

Scenario 4

Scenario 5

Scenario 6

Scenario 7

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000 1.000

0.002 0.001 0.057 0.002 0.004 0.037 0.005 0.007 0.01 0.014 0.02 0.03 0.059 0.838 1.000

0.007 0.007 0.013 0.013 0.015 0.020 0.025 0.029 0.043 0.059 0.076 0.105 0.169 0.413 1.000

0.014 0.015 0.025 0.026 0.029 0.037 0.046 0.052 0.073 0.093 0.111 0.134 0.168 0.171 1.000

0.027 0.029 0.047 0.048 0.052 0.064 0.076 0.081 0.104 0.117 0.118 0.109 0.085 0.029 1.000

0.124 0.116 0.161 0.131 0.112 0.102 0.082 0.053 0.036 0.015 0.004 0.000 0.000 0.000 1.000

1.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 1.000

MAX. On the other hand, with tendency of a toward infinite, the quantifier Q(p) delivers its second limit (‘All’) which is equivalent to operator MIN. In order to determine order weights through weighted average method in a summarized manner the relative mathematical definitions are stated: For a given set of criterion (attribute) maps, OWA is defined as a map combination operator that associates with an ith location (object) a set of order weights v = v1, v2, . . ., vn (vj° [0, 1], j = 1, 2, P . . ., n, and nj¼1 wj ¼ 1 and a set of criterion weights w = w1, w2, Pn wn (wj° [0, 1], and j¼1 wj ¼ 1 (Boroushaki and Malczewski, 2008; Malczewski and Rinner, 2015). In this research, considering the obtained criterion weights from ANP method (Fig. 11 and Table 8) and selected linguistic quantifier (Table 9) and using the relation (3) (Malczewski, 2006c), the order weights were calculated (Table 10):

tj ¼

n X wj

!a

j¼1



n1 X wj

!a

In the space decisions with determination and implementation of a suitable set of order weights, the vast scope of the results (maps) can be obtained. In other words, with the delivery of different results of risk level and different compensability (trade-off), this method is encountered with a high flexibility for the fulfillment of the needs and priorities of decision makers. The operator OWA is defined as follows:

OWAi ¼

j¼1

! wjtj Pn Zij j¼1 wj tj

Orness ¼

n 1 X ðn  iÞ  ti n  1 i¼1

ð5Þ

The second character, operator OWA shows the exchange measure or effectiveness of one index from the other indexes and is stated as follows:

sffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi  2 n Xn 1 Trade off ¼ 1  t i  i¼1 n1 n

ð6Þ

In which ti is the criterion order weight with degree r and n is the criterion number. 3.5.3. Third step: design and definition of scenarios

ð3Þ

j¼1

n X

OWA between the relation AND (minimum) and OR (maximum) and the indicator of the risk aversion and risk seeking of a decision-taker. Orness is defined as follows:

ð4Þ

In which Zin P    P Zi1 obtained from ordering the values of a criterion (xij). tj is the order weight and wj is the same criterion weight that is ordered based on the order z. The operator OWA consists of two main characters which indicate the manner and position of an operator: 1 – Orness and 2 – Permutation trade-off relation measure. An Orness defines the location of the operator

I. First scenario: Maximum level of risk and no trade-off: based on the linguistic word ‘At least one’ (a = 0.0001): The fuzzy linguistic quantifier ‘At least one’ is introduced as the most optimistic scenario in the fuzzy quantifier circuit. In this scenario, the decision results lead to the maximum risk and the lowest recoverability. The total weight is allocated to the last ordered grade (the maximal suitable mark in the entire factors for each pixel), the results are exactly similar to the operation OR in MCE. II. Second scenario: High level of risk and some trade-off: based on the linguistic word ‘Few’ (a = 0.1): The linguistic word ‘Few’ is applicable on the very optimistic strategy decision. In this scenario, the decision results will lead to high risk and some trade-off. III. Third scenario: Strategy with relatively high level of risk and some trade-off: based on the linguistic word ‘Some’ (a = 0.5):

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Z.K. Motlagh, M.H. Sayadi / Waste Management xxx (2015) xxx–xxx

The linguistic word ‘Some’ is applicable on the optimistic decision strategy. In this scenario, the decision results will lead to some risk and high trade-off. IV. Fourth scenario: Strategy with an average level of risk and full trade-off: based on the linguistic word ‘Half’ (a = 1): In this state, an operator has turned into Weighted Linear Combination (WLC) and a total recoverability and average risk Orness = 0.5 is implemented. V. Fifth scenario: Strategy with a relatively low level of risk and some trade-off: based on the linguistic word ‘Many’ (a = 2): The quantum ‘Many’ references pessimistic decision strategy. In this scenario, the decision results lead to low risk and some tradeoff. VI. Sixth scenario: Low level of risk – some trade-off: based on the linguistic word ‘Most’ (a = 10): The quantum ‘Most’ references pessimistic decision strategy. In this scenario, the decision results lead to low risk and some tradeoff. VII. Seventh scenario: Minimum level of risk – no trade-off: based on the linguistic word ‘All’ (a = 1000): Doubtlessly is the decision in a very pessimistic condition. This scenario leads to the worst decision state and in it the highest values existing in each situation are selected. This scenario is applicable on the logical operator AND besides the operations is carried out without risk and any kind of trade-off, and in it, the point that realizes all the used criteria, is selected. In this method, the criterion which is the least important criterion of the research is given the highest priority. This means that 100% Orness weights are given to this criterion. In reality, in this method, the combination of layers like Boolean method is with the use of operator AND. In this method, the criterion with higher weight importance has a lower Orness and a criterion with lesser weight importance has a higher Orness.

4. Results 4.1. Factors fuzzification In this research, the different environmental and socioeconomical factors are used for the landfill site selection, and in continuation, some of these parameters are addressed and in Fig. 10 their Fuzzy maps can be observed. Slope: The landfill sites should not be on the hills that have unsuitable slopes. The better points and or worthier for landfill are the elevated areas without the slope (elevated plains). The areas with the hasty slope are of lower values for landfill since during the rainfall and water penetration this possibility and probability remains that we have a horrible danger-producing downfall. The 3–40% slopes are considered suitable for site selection. With slope increase, the suitability of pixels reduces. The decreasing membership function was used. Soil parameters: The soils with high penetrability have low value for landfill sites and are needful of specific technologies. The selected sites for landfill should not be sandy soils since these soils have a very high porosity and the water permeability rate in these soils is high that can cause a disorder in the water quality. The best soil texture is the silty clay and in the next stage silty

11

sandy. The variable soil parameters are broken and the type of their membership function is user defined. Fault: The landfill site should not be in a land that is active in terms of landslide and or the probability of its activation exists in the future, therefore the increasing membership function has been used and is in the distance of around 100 m from the fault (Fig. 10-1). Hydrology: The information related to hydrology is required for the determination of underground water level, soil permeability, rivers, sources of surface waters and drinking water sources around the considered area. The surface and underground water are the most influential indexes in the landfill site selection that can be due to high role in the landfill site selection in the pollution of surface waters, permeability of water inside the landfill site, the increased volume of emulsion production and vessel for the underground waters. With an increased distance from the water sources, the suitability of pixels for the site selection increases. The increasing membership function is used (Fig. 10-2). Urban areas: The minimum distance from urban areas is minimally 3000 m and the increasing membership function is used (Fig. 10-3). Airport: The landfill sites can be an attractive factor for the birds of the same area, therefore suitable areas should not be around distance of 3000 m from an airport. The increasing membership function is used (Fig. 10-4). Protected areas: The landfills should not be located in the protected areas (national legislation). Therefore, these areas should not be around 500 m distance from this area. The increasing membership function is used (Fig. 10-5). Land use: The landfill site should not have any conflict with the other usages. The variable landuse is broken and the type of its membership function is user defined (Fig. 10-8).

4.2. Factors weight determination Tables 4–8 and Fig. 11.

4.3. Factors and constraints integration The primary map obtained from the multi criteria assessment for the landfill site selection in the case study area is the map that is integrated with raster format and its values range from 0 to 206 (Fig. 12). The region with a value over 206 does not exist and is indicative of the fact that the regions with extraordinary capability are not present in the area. The higher suitability is the indicator of the higher power degree and the lower suitability is the indicator of a lower power degree for the landfill. The suitability rate of each pixel is the indicator of the factors suitability rate and even the weights allocated to them. On this map, the complete elements of an image (pixels) are nestled in the colored spectrum that makes the choice of special sites possible for the site selection. According to Fig. 13, the resultant final map from the WLC method based on the natural breaks is divided into 5 suitability categories.

4.4. The results of Ordered Weighted Averaging (OWA) method In this research, the influence of criteria and their weighted degree for the site selection in different situations is stated according to Table 10. With consideration of different OWA method and Orness weights situations, the site selection was implemented whose results are presented in Fig. 14.

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Z.K. Motlagh, M.H. Sayadi / Waste Management xxx (2015) xxx–xxx

Fig. 10. The standardized (fuzzified) maps, distance from: 1 – Fault, 2 – The underground water sources (Wells, Springs and Kanats), 3 – Urban areas, 4 – Airport, 5 – Protected areas, 6 – Main road, 7 – Historical places, 8 – Landuse and 9 – industries.

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Z.K. Motlagh, M.H. Sayadi / Waste Management xxx (2015) xxx–xxx

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5. Discussion

Fig. 11. Factors weight in the Super Decision software.

The present paper proposes an evaluation framework rather than a DSS tool. The later will be the result of future research. The introduced method with reference to the regional case study in the east of Iran relates to the landfill suitability and resultantly delivery of a useful support for the site selection. The landfill suitability arena area is demonstrated in Table 11. According to Table 11, under scenarios 1–7, 62.69%, 32.41%, 21.45%, 18.71%, 13.65%, 4.60% and 0.05% of region area is in the very suitable class. With a value variation toward bigger numerals (from the first scenario to seventh scenario) the area of the very suitable classes has been mitigated. For the final confirmation and accuracy investigation of the present research results, the obtained suitability map for OWA was compared with effective layers such as the distance from the water sources, and it was determined that the supreme site selection classes were in the classes with high value of the top

Fig. 12. The resultant map from the fuzzy logic implementation using the WLC method.

Fig. 13. The classified final map resultant from the fuzzy logic and WLC implementation.

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Z.K. Motlagh, M.H. Sayadi / Waste Management xxx (2015) xxx–xxx

Fig. 14. The final region power maps for the landfill site selection using the OWA model.

Table 11 The landfill site selection arena area in different conditions (hectares).

First scenario Second scenario Third scenario Fourth scenario Fifth scenario Sixth scenario Seventh scenario

Class 1 low

Class 2 moderate

Class 3 strong

Class 4 very strong

Class 5 extremely strong

–

– 11,696 11,421 21,556 41,060 62,400 5994

–

– 83,273 80,017 55,884 48,017 40,721 1257

214,734 111,014 74,480 64,088 46,760 15,786 186

711 6584 11,675 12,757 50,554 191,075

8041 43,233 61,541 66,140 55,373 4356

Table 12 The overall accuracy results obtained from ‘Many’ fuzzy linguistic quantifier using the cross-tabulation operator cross-tabulation of WLC (columns) against many (rows).

0 1 2 3 4 5 Total

0

1

2

3

4

5

Total

396,877 0 0 0 0 0 396,877

0 10,210 3561 41 0 0 13,812

0 2529 26,492 12,517 47 0 41,585

0 18 10,709 39,114 7093 9 56,943

0 0 298 14,365 30,443 4347 49,453

0 0 0 103 10,434 42,404 52,941

396,877 12,757 41,060 66,140 48,017 46,760 611,611

Overall kappa = 80.39%. Overall accuracy = 89.19%.

layers. In addition, after the classification of the land suitability final maps with a cross-tabulation operator in the IDRISI software environment the overall accuracy classification was obtained and the calculated Kappa coefficient (Karatepe and Ikiel, 2013) via the software that in this stage due to the calculation volume reduction only the table related to overall accuracy results (fifth scenario) which had the highest accuracy is introduced (Table 12). (7) Pixels total aggregate/aggregate number of the main diagonal pixels = overall accuracy classification. The results of power maps demonstrate the case study area for the establishment of the landfill sites that the OWA method on ‘Many’ Fuzzy linguistic quantifier (fifth scenario) has the highest accuracy (89.19%), therefore possesses the best result (Table 13). In this scenario sequentially 3.72%, 11.98%, 02.31%, 14.19% and

Table 13 Overall accuracy rates in the comparison of the present study results. Fuzzy linguistic quantifier

At least one

Few

Some

Half

Many

Most

All

Overall accuracy percent

67.14

76.24

78.56

81.84

89.19

75.44

66.96

13.65% of the case study area are in the very unsuitable, unsuitable, medium suitable, suitable, and very suitable classes. Innumerable researchers have tried different modes for the suitable landfill site selection and have carried out several researches. Many factors can be effective in the landfill site selection. But considering the conditions of a region, the suitability factors should be determined. In this research, the factors such as the distance from the underground water sources, distance from the surface waters, texture and depth of soil, slope, distance from the fault, landuse, distance from the protected areas, distance from the urban and village areas, distance from the main road, distance from the airport, distance from the historical places and distance from the industries were used. In the studies carried out worldwide, Nishanth et al. (2010), in the United States of America, have divided their considered factors in two categories viz. physical and socio-economical factors, but have not used the soil parameters. 6. Conclusions The ANP method resulted more suitable than the AHP method because it enhances the function of the AHP to develop a complete

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Z.K. Motlagh, M.H. Sayadi / Waste Management xxx (2015) xxx–xxx

model that can incorporate interdependent relationships between elements from different levels or within levels, which are assumed to be uncorrelated in AHP. The complex ANP method proved to be more preferable to the simple one because it is able to take into consideration all the criteria without sacrificing their relationships, and it can deal with all kinds of dependencies systematically. The proposed approach has shown that the spatial ANP is a powerful tool for solving complex problems with interactions and correlations among multiple objectives. Therefore, the integration of GIS and ANP methods provides a mechanism with which complex issues can be thoroughly explored and immediate feedback for Decision-Makers can be provided. The suitability of each cell for landfill is calculated by means of Weighted Linear Combination (WLC) of multiple criteria in raster GIS. The WLC procedure allows full trade-off among all factors. The amount any single factor can compensate for another is, however, determined by its factor weight In terms of relative risk, a Boolean MCE that uses the AND operation is essentially a very conservative or risk averse operation, and that OR operation is extremely risk taking. These are the extreme on a continuum of risk. WLC lies exactly in the middle of this continuum. WLC, then, is characterized by full trade-off and average risk. The Weighted Linear Combination aggregation method offers much more flexibility than the Boolean approach. It allows for criteria to be standardized in a continuous fashion, retaining important information about degree of suitability. It also allows the criteria to be differentially weighted and to trade off with each other. Use of OWA method permit the evaluation of several decision scenarios to effectually guide planners attain a more satisfied solution by considering the risk and trade-offs in the decision-making assessment process. The final suitability map is an indicator of a space decision-making support approach which is able to identify the suitable areas for the waste disposal. Conclusively, the present research showed that in order to obtain a vast spectrum of decision-making strategies, the OWA operator can be used and besides the procured results delivered a better understanding of the decision-making issue. Thus, based on these findings, it can be mentioned that the cognitive method approach can avail a very useful support for several multi criteria decision-making issues. Acknowledgments Authors are appreciated the authorities of Research Council and Faculty of Natural Resources and Environment, University of Birjand, due to their sincere cooperation. References Abessi, O., Saeedi, M., 2009. Site selection of a hazardous waste landfill using GIS technique and priority processing, a power plant waste in Qazvin province case example. Environ. Sci. 6 (4), 121–134. Akbari, V., Rajabi, M.A., Chavoshi, S.H., Shams, R., 2008. Landfill site selection by combining GIS and fuzzy multi criteria decision analysis, case study: Bandar Abbas, Iran. World Appl. Sci. J. 3 (Supple 1), 39–47. Ajit, P., Singh, A., Vidyarthi, K., 2008. Optimal allocation of landfill disposal site: a fuzzy multi-criteria approach, Iran. J. Environ. Health Sci. Eng. 5 (1), 25–34. Amiri, M.J., Mahiny, A.S., Hosseini, S.M., Jalali, S.Gh., Ezadkhasty, Z., Karami, Sh., 2013. OWA analysis for ecological capability assessment in watersheds. Int. J. Environ. Res. 7 (1), 241–254. Azizi, A., Malekmohammadi, B., Jafari, H.R., Nasiri, H., Amini Parsa, V., 2014. Land suitability assessment for wind power plant site selection using ANP-DEMATEL in a GIS environment: case study of Ardabil province, Iran. Environ. Monit. Assess. Beskese, A., Handan Demir, H., Kurtulus Ozcan, H., Eser Okten, H., 2014. Landfill site selection using fuzzy AHP and fuzzy TOPSIS: a case study for Istanbul. Environ. Earth Sci. Bottero, M., Comino, E., Riggio, V., 2011. Application of the analytic hierarchy process and the analytic network process for the assessment of different waste water treatment systems. Environ. Model. Softw. 26, 1211–1224.

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Please cite this article in press as: Motlagh, Z.K., Sayadi, M.H. Sitting MSW landfills using MCE methodology in GIS environment (case study: Birjand plain, Iran). Waste Management (2015), http://dx.doi.org/10.1016/j.wasman.2015.08.013