Spatial multicriteria decision support for robust land-use suitability: The case of landfill site selection in Northeastern Greece

Accepted Manuscript

Spatial multicriteria decision support for robust land-use suitability: The case of landfill site selection in Northestern Greece Olympia E. Demesouka , Konstantinos P. Anagnostopoulos , Eleftherios Siskos PII: DOI: Reference:

S0377-2217(18)30610-6 10.1016/j.ejor.2018.07.005 EOR 15243

To appear in:

European Journal of Operational Research

Received date: Revised date: Accepted date:

8 March 2016 26 June 2018 4 July 2018

Please cite this article as: Olympia E. Demesouka , Konstantinos P. Anagnostopoulos , Eleftherios Siskos , Spatial multicriteria decision support for robust land-use suitability: The case of landfill site selection in Northestern Greece, European Journal of Operational Research (2018), doi: 10.1016/j.ejor.2018.07.005

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Highlights 

The UTASTAR method is applied for the first time in land-use suitability analyses.



The Spatial UTASTAR is applied to identify areas for placing a Municipal Solid Waste landfill. The Stochastic Multiobjective Acceptability Analysis is applied to aid decision

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making process.

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Spatial multicriteria decision support for robust land-use suitability: The case of landfill site selection in Northestern Greece Olympia E. Demesouka1*, Konstantinos P. Anagnostopoulos1, Eleftherios Siskos2 1

Abstract

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Production Engineering and Management Department, Democritus University of Thrace, Vas. Sofias 12, 67100 Xanthi, Greece ({odemesou; kanagn}@pme.duth.gr) 2 National Technical University of Athens, 9, Iroon Polytechniou Str., 15780 Zografou, Athens, Greece ([email protected])

Multicriteria spatial decision support systems (MC-SDSS) have emerged as an

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integration of geographical information systems (GIS) and multiple criteria decision aid (MCDA) methods for incorporating conflicting objectives and decision makers’ preferences into spatial decision models. In this paper, we present spatial UTASTAR (S-UTASTAR), a raster-based MC-SDSS for land-use suitability analysis. The multicriteria component of the system is based on the UTA-type disaggregation-

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aggregation approach. S-UTASTAR is applied in a raster-based case study concerning land-use suitability analysis to identify appropriate municipal solid waste landfill

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(MSW) sites in Northeast Greece. Moreover, robustness analysis tools are implemented to guarantee robust decision support results. More specifically, during

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the aggregation phase, the Stochastic Multiobjective Acceptability Analysis (SMAA) is used to indicate the frequency at which a site achieves the best ranking positions

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within a large set of alternative landfill sites.

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Key words: Multiple Criteria Decision Analysis, UTASTAR Method, Robustness Analysis, Geographical Information Systems, Land-Use Suitability Analysis.

*Corresponding author 2

ACCEPTED MANUSCRIPT 1. Introduction Over at least the past 100 years, land-use suitability analysis, i.e., the identification of the most appropriate spatial pattern for future land use, has been widely used by architects, planners and managers for urban/regional/environmental planning and management (Collins et al., 2001; Malczewski, 2004). Suitability mapping and analysis enable public authorities and private developers to use the maps to set

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policies and make decisions in a wide variety of situations: land-use plan development (Nguyen et al., 2015; Romano et al., 2015; Montgomery et al., 2016; Santé et al., 2016; van Niererk et al., 2016), environmental impact assessment (Rahman et al., 2014; Iyalomhe et al., 2015; Cervelli et al., 2016; Zhang et al., 2016; Cervelli et al., 2017), and site selection studies for public and private facilities

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(Alvarado et al., 2016; Cradden et al., 2016; Eldemir et al., 2016; Guerreiro et al., 2017; Rodríguez et al., 2017; Veronesi et al., 2017).

The proliferation of suitability analyses is indisputable due to the advancements in Geographical Information Systems (GIS) technology. Based on map layering (McHarg,

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1992) and embodied with sophisticated overlay procedures, modern GIS are very powerful systems for capturing, storing and managing spatially referenced data

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(Jankowski, 1995; Leao et al., 2004; de Winnaar et al., 2007; Nas et al., 2010; Pourebrahim et al., 2011).

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However, decisions regarding land use have become more and more urgent and difficult to make in recent decades as a consequence of the rapid population growth

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and the increasing concern regarding sustainable development. As a result, separate environmental protection facilities for the treatment or disposal of residuals, such as

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landfills and natural systems for wastewater treatment (constructed wetlands), have been considered for implementation around the world. The selection of appropriate sites for placing such a facility is particularly critical in terms of the impact that a facility may have on the surrounding environment, the public health and the community in general (Jensen & Christensen, 1986; Tchobanoglous et al., 1993). For this reason, the candidate sites should comply with both strict national and international regulations.

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ACCEPTED MANUSCRIPT Except for the serious legislation enacted to prevent air and water contamination in an area, the major problem that decision makers (DMs) have to address is citizens’ opposition (Geneletti, 2010). Better known as NIMBY (Not In My BackYard) syndrome, this opposition is due to the social opposition to the placement of facilities for treatment or disposal of residuals near residential areas (Erkut & Moran, 1991; Lober & Green, 1994). Thus, residents’ ignorance related to the health and

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social benefits offered by these facilities makes the task of placing disposal sites even more difficult (Noble, 1992; McBean et al., 1995; Siddiqui et al., 1996).

In addition, numerous factors (technical, environmental, economic and social) must also be taken into consideration by DMs when deciding on the location of an environmental protection facility to, on the one hand, maximize the efficiency and

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economic value and, on the other hand, minimize the risk of environmental contamination and social obstructions (Bagchi, 1990; MacDonald, 1996; Mahini & Gholamalifard, 2006; Delgado et al., 2008; Demesouka et al., 2013a). It is therefore crucial to use multicriteria decision methods to integrate DMs’ preferences into the

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facility siting process. However, the spatial nature of selecting an appropriate site for locating this kind of facility demands the use of GIS.

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According to Cowen (1988), GIS are often referred as a decision support system for integrating spatially referenced data, due to their embodied techniques and

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procedures. However, multicriteria decision methods provide frameworks for structuring decision problems, designing, evaluating and prioritizing alternative

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decisions (Malczewski, 2006; Marttunen et al., 2017). As a result, this combination of methods leads to the development of Multicriteria Spatial Decision Support Systems

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(MC-SDSS) that rank the feasible solutions by combining spatial data and DMs’ preferences according to a selected decision rule (Anagnostopoulos & Vavatsikos, 2012).

The above synergy of GIS-based tools and decision analysis methods enhances the capabilities of the research area, as it provides a consistent framework for handling conflicting objectives and structured or semi-structured problems involving participants expressing different preferences (stakeholders, DMs and technical experts). The consideration of diversified preferences aims to clarify this spatial 4

ACCEPTED MANUSCRIPT decision problem with regard to the needs, the goals and the determination of the criteria, resulting in the reduction of the problem’s complexity to guarantee rational decision making and minimize the risk of inciting public opposition. This paper proposes S-UTASTAR (S denotes the spatial data that are applied in the raster-based analysis), a new MC-SDSS for suitability analyses that implements the well-known multicriteria disaggregation method UTASTAR (Siskos & Yannacopoulos,

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1985, Siskos et al., 2016) together with a widely used commercial GIS software package. Although the proposed S-UTASTAR method supports any type of rasterbased land-use suitability analysis, this paper focuses on land-use suitability analysis related to finding the most and least propitious locations for environmental protection facilities, especially municipal solid waste (MSW) landfills. Recently some

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countries/states have already banned all new landfills, and many more are planning to do this. Also, in most countries waste must be sorted into different segments for recycling, composting, energy production etc which should imply dramatically decreasing amounts of waste to be placed in landfills. In addition, robustness

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analysis tools are implemented to guarantee robust decision-support results. More specifically, during the aggregation phase of the UTASTAR method, the Stochastic

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Multiobjective Acceptability Analysis (SMAA) is implemented to get robust recommendations taking under consideration all the analysis parameters in

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conjunction with the DMs preferences (Corrente et al., 2014; Angilella et al., 2015). The present paper provides the methodological framework and new useful practical

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results to involved DMs/stakeholders, who are interested in solving the problem of siting a new MSW landfill in the region of Thrace, Greece. The fact that in the region

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of Thrace there are only two MSW landfills in Xanthi and Rodopi prefectures to meet the municipal waste depositional needs, justifies the need for finding new places for siting such kind of facilities. Moreover, the rapid population growth means continuing increase in the expected waste amount at the same time, implying that the waste disposal sites that are already in use cannot fulfill regional landfill needs. For that reason, technocrats including civil engineers, planners, technical experts, academic staff, and local government staff are served as experts to define the factors and constraints that should be taken into account according the legislation in force

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ACCEPTED MANUSCRIPT and study area’s characteristics during landfill siting process. As a result, a new MSW landfill is planned to be sited in the Evros prefecture in the next few years, to meet the Evros’ prefecture waste disposal needs. The rest of the paper is organized as follows. In section 2, a brief summary of the state-of-the-art is presented regarding the application of GIS to land suitability analysis. Section 3 outlines the methodology of the paper, i.e., the UTASTAR method

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coupled with robustness analysis tools, including the SMAA method. Section 4 describes the case study, while section 5 presents the results of the method’s implementation. Finally, section 6 summarizes what can be learned from this feasibility study and section 7 concludes the paper and presents some guidelines for

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future research.

2. GIS and land suitability analysis systems

In site suitability analysis, the DM addresses numerous factors with a view toward

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identifying appropriate places for locating an infrastructure that comply with the related legal framework and the preferences of stakeholders (Joerin & Musy, 2000;

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Sumathi et al., 2008; Tavares et al., 2011). The complexity of the conflicting factors, which are involved in the planning, design and management of land use, has signaled

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the need for an integrated approach to manage such decision problems (Beinat & Nijkamp, 1998).

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Given the spatial nature of the site selection problem, the use of GIS-based tools, considering their ability to store, analyze and display spatially distributed data from a

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variety of sources, is essential (Malczewski, 1999; Nisar Ahamed et al., 2000; Moeinnadini et al., 2010). Specifically, according to Cox and Gifford (1997), the application of GIS to site suitability analyses is done mainly due to its ability to:

(a) integrate different databases into a single database environment, as every layer can represent an integrated database; (b) be dis-integrated to visualize spatial data, unlike classical databases;

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ACCEPTED MANUSCRIPT (c) quickly produce specialized spatial maps through the production of databases, thus allowing the production of statistical data; (d) quickly execute complex spatial analyses that comply with the spatial data definition, and (e) create new data from the existing data or through the analysis of the

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interrelations between the datasets. In addition, the integration of Boolean logic map algebra into the GIS commercial software packages has been found to indisputably enhance their contribution to land-use suitability analysis and mapping (Malczewski, 2004). Since then, practitioners, academics and planners have widely implemented Boolean overlays to support spatial decisions, with the use of exclusionary criteria. In that manner, under

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 

k constraints, the suitability index S aj for the j th cell is considered as the algebraic product (logical AND) of the binary raster datasets c ji (Equation 1). However, the suitability of sites falling within the feasible solutions set cannot be evaluated using

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the provided Boolean overlay tools.

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where

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S   c ji a j

1 if x ji satisfies the constra int i c ji   otherwise 0

(1)

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i 1

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where x ji stands for the attribute value of the j th cell under the i th constraint and k

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is the number of constraints under consideration.

No matter how important the contribution of GIS technology to land suitability analyses may be, the use of mainly overlay procedures and buffer zones has been considered a major shortcoming (Carver, 1991). Specifically, their efficiency as decision support systems is debatable (Laaribi et al., 1996; Chakhar & Martel, 2003) due to their inability to (a) encompass a DM’s preferences into the analysis, (b) rank the alternatives with respect to the analysis objective, considering that all solutions fulfilling the constraints are of equal importance, (c) include evaluation criteria, and 7

ACCEPTED MANUSCRIPT (d) provide a framework for the spatial distribution of solutions that satisfy the analysis goal. As a result, there is a high possibility that the DM will reach an inappropriate conclusion; hence, either the process should be repeated, incurring money and time losses, or the new facility will lack efficiency and convenience. For all these reasons, efforts have been made over the last thirty years to combine GIS with multiple criteria decision aid (MCDA) methods to overcome the

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aforementioned disadvantages (Laaribi et al., 1996; Demesouka et al., 2013b). This synergy has led to the development of the MC-SDSS approach. MC-SDSS provides a consistent framework in which to evaluate alternative locations according to a DM’s preferences and multiple criteria evaluation, with the use of a decision rule, thereby increasing the efficiency of the spatial decision-making process (Malczewski, 1999;

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Sharifi & Retsios, 2004). Figure 1 shows the depiction of Anagnostopoulos et al. (2010) of an adaptation in spatial analyses of the well-known Simon’s decisionmaking model (Simon, 1960; Turban, 1993; Demesouka et al., 2013b) in three stages

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(Intelligence-Design-Choice).

Figure 1: Spatial multicriteria decision support process

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ACCEPTED MANUSCRIPT 3. UTASTAR method and related robustness analysis 3.1 Proposal of an additive value model The additive value model is one of the prevailing preference models in Multiple Criteria Decision Aiding (MCDA). Its indirect elicitation through pairwise questions is often applied due to lowering the cognitive effort on the part of a Decision Maker (Siskos et al., 2016).

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The methodological approach adopted in this paper suggests the evaluation of landfill sites using multiple criteria and globally by aggregating these criteria with the aid of a synergy of MCDA methods. Initially, the aggregation of the criteria to a global value for each alternative is performed through the assessment of a multicriteria additive value system. Its parameters are estimated using the UTASTAR

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disaggregation method (see Siskos et al., 2016, for a recent survey on UTA-type methods). A disaggregation approach is deemed as more efficient in this study, compared to the conventional direct assessment technique, due to the heterogeneity of the criteria and the cognitive style of DMs.

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However, the use of such an approach does not guarantee a single solution. In contrast, it specifies multiple inter-criteria parameters, all compatible with the DMs’

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preferences, that should be considered before obtaining the final results. Careful planning beforehand and careful implementation of the UTASTAR method are

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therefore required, as well as a robustness analysis of the final ranking results. Specifically, the Stochastic Multiobjective Acceptability Analysis (SMAA) is used to

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indicate the frequency at which a site achieves the top ranking positions within a large set of alternative landfill sites, aiding DMs in discrete decision making problems

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(Kangas et al., 2005; Tervonen et al., 2009). In the case where the DM is not satisfied, UTASTAR is again implemented using additional preferential information, which is expected to increase the robustness. The additive value model is described by the following mathematical formulae:

,

(2)

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ACCEPTED MANUSCRIPT ,

where and

is the performance vector of a landfill site on the n criteria; are the least and most preferable levels of the criterion

, respectively;

, i=1,2,…,n, denotes non-decreasing marginal value functions of the performances

,

and

is the relative weight of the i-th function

Accordingly, for a given site a,

and

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represent the multicriteria vector of

performances and the global value of site a, respectively. The marginal and global value functions, like the criteria, are monotonous. Consequently, in the case of two

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different alternatives a and b (landfill sites), given their global values, the following properties hold:

(3)

(Indifference)

(4)

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(Preference)

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An additive value model is valid in the case of an individual decision maker (DM) if the criteria are preferentially independent of each other (e.g., see Keeney and Raiffa, 1976, Keeney, 1992). A number of different methods may be utilized to assess and

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structure a value system (see Keeney and Raiffa, 1976, Keeney, 1992, Greco et al.,

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2016). In this study, the disaggregation or ordinal regression UTASTAR method (Siskos and Yannacopoulos, 1985) is implemented to assess and construct the additive value system due to its ability to help DMs to implicitly define their priorities

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regarding a set of heterogeneous (quantitative and/or qualitative) and difficult-toassess criteria.

3.2 The UTASTAR method The ordinal regression UTASTAR method is an enhanced version of the original UTA model (Jacquet-Lagrèze and Siskos, 1982). It infers one or more piecewise linear value functions from a ranking of m reference sites, which is provided by the DM. 10

ACCEPTED MANUSCRIPT UTASTAR computes an unweighted form of the additive value function, which is strictly equivalent to the weighted one (Equation 2):

n

u  g    ui  g i  i 1

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under the constraints:

 u g   1 i 1

i

* i

(5)

ui g i   0

i  1,2,..., n

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n

The estimation of each marginal value function is performed for a number of discrete points

Assuming that the best and worst values of





every criterion are finite, the criterion scale g i , g  is divided into a i 1 equal





i

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intervals having the form g ij , g ij 1 , where j  1,...., ai  1 , g i  g i1 and g i  g iai . The value of the parameter a i is determined by the analyst and defines the number

j 1 gi   gi i  1



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gij  gi  

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using Equation 6.

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of points that are estimated for every marginal function u i . Points g ij are calculated



(6)

Assuming that the reference sites are rearranged in such a way that the ranking and

,

is the head of

is preferred or indifferent to ak+1, the UTASTAR

algorithm can be summarized as follows:

Step 1: Express the global value of reference sites terms

of

marginal

values

and

then

, k=1,2, …, m, first in in

terms

of

variables

and j, by means of the following expressions: 11

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Step 2: Introduce two error functions

and

by writing, for each pair of

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Step 3: Solve the linear program:

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consecutive sites in the reference ranking, the analytic expressions:

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with δ being a small positive number, δ  [0,1].

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Figure 2 illustrates the correspondence between the unweighted and weighted marginal value function for the criterion g1, named “NATURA 2000” (see sections 4

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and 5); the criterion’s evaluation scale [500 – 20,500] is divided into four equally distanced intervals, and the number of unknown variables is set to 4 (w11, w12, w13, w14); the weight of the criterion is then the sum of all these variables, i.e., p1=w11+w12+w13+w14. The z error function expresses the overestimations or underestimations of the reference sites over the weak order of the DM. It is used as a criterion for checking the consistency of the DM’s initial ranking structure. The optimal value of z is 0.

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ACCEPTED MANUSCRIPT Step 4 (Feedback): When the solution of the linear programming problem leads to non-zero errors, the DM’s ranking of the reference actions needs to be revised by applying several arguments and feedbacks, or a combination of them, e.g., (a) conducting a trade-off analysis between criteria values in terms of compensation, (b) modification of the DM’s initial ranking, (c) modification of certain reference alternatives, (d) modification of the modeling of the criteria, (e) configuration of the

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problem (see Siskos et al., 2016, for more details). Then, the new linear programming problem is solved again until a zero value for the objective function is

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obtained (Figure 3).

Figure 2: Unweighted and weighted (normalized between 0 and 1) marginal value function for criterion g1 “NATURA 2000”

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Define analysis goal Site Selection Criteria

Constraint Analysis

Decision maker

Set of Alternatives Determination

Alternatives’ Reordering

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Linear Programming Solution

Consistency Check

No

Yes

Feedbacks Produced random value funcrions

Trade – off Analysis

Reference Set Ranking’s Modification

No

Robustness check Yes

Set of Alternatives’ Modification

Identification of the alternatives with the best performance

Criteria Modification

Estimation of the best alternatives frequency

Problem’s Determination

Selection of the best alternative according to DMs preferences

SMAA Method Implementation

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Two Error Functions Introduction

UTASTAR Method Implementation

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Reference Set Ranking

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Feasible Solutions Set

Figure 3: Algorithmic synergy of S-UTASTAR and SMAA methods

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ACCEPTED MANUSCRIPT 3.3 Robustness analysis and control The analysis and measurement of the robustness of the results, as already mentioned, is considered an essential step prior to validating the results obtained through the implementation of an ordinal regression method, such as the UTA-type methods. Given that there is not a single additive value function representing the DM's ranking but an infinite amount of such functions, which belong to a convex

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polyhedron, the most representative value function is selected for the evaluation of the set of sites.

Corrente et al. (2013) proposed a general methodological framework called Robust Ordinal Regression (ROR), which can be implemented synergistically with the

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disaggregation methods and aims at enhancing the robustness of the estimated results. ROR is based on the principle according to which the decisions and proposals emerge after considering all those parameters that are compatible with the preferences of the DM. Very recently, Siskos and Psarras (2016) proposed an interactive bipolar robustness control, which manages the robustness in both

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phases/poles of the interactive decision support process, i.e., the disaggregation and the aggregation one. Through this integrated procedure, the analyst has the ability

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to examine, measure and analyze, in a systematic way, the robustness of the decision model’s parameters and the results that emerge after the implementation

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of the additive value model. This robustness control framework, when coupled with any UTA family method, uses two separate sets of robustness indices to judge (i) the

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efficacy of the additive model for the disaggregation pole and (ii) the robustness of the final results, achieved after the extrapolation of the model on the whole set of

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sites A, for the aggregation pole. For the needs of the suitability analysis study, two robustness indices are proposed and used, each acting on a separate control pole. For the disaggregation pole, the average stability index (ASI) is proposed to indicate the average value of the normalized standard deviation of the model parameters, denoted here as pij:

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(7) where m is the number of different parameter instances calculated and n is the number of criteria. ASI ranges from [0-1] and returns a value of 1 when perfect stability is achieved. The use of this index presupposes the production of multiple

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sets of preferential parameters. A typical way to achieve this, when implementing the UTA-type methods, is to use the Max-Min LPs technique. During this procedure, all or part of different parameters are successively minimized and maximized under the UTA constraints and then visualized.

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For the aggregation pole, a random sampling algorithm/technique is proposed to produce and statistically analyze a large number of additive function sets from the UTA polyhedron of compatible additive functions. This technique is the Stochastic Multiobjective Acceptability Analysis (SMAA) initiated by Lahdelma et al. (1998) and

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revisited by Tervonen and Figueira (2008). SMAA performs random samplings within the whole area of the unconstrained parameters and then, after accounting for the

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constraints given by the DM (UTA constraints), discards those samples that violate them. The implementation of an adequately large number of samplings provides

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clear insight regarding the parameters variation and allows the computation of an average value model that could be considered as a representative one. It also

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indicates the frequency at which an alternative site gets a single ranking position in the final ranking and thus provides a meaningful measure of the robustness of the

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final results.

The whole methodological framework proposed in this paper is presented in paragraph 3.4 and is visually depicted in Figure 3.

3.4 S-UTASTAR – SMAA algorithm The spatial S-UTASTAR method in GIS-based suitability analysis is conducted in the following steps (Figure 3):

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ACCEPTED MANUSCRIPT Step 1: Determine the overall goal of the analysis. Step 2: Determine the site selection criteria for the evaluation of the alternatives. Step 3: Apply the constraint analysis. Step 4: Establish the feasible solutions set, i.e., the remaining areas after the implementation of the constraints.

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Step 5: Select a set of reference sites. Step 6: The DM determines a ranking of reference sites by expressing his/her own global preferences.

Step 7: Reorder the alternatives according to the DM’s initial ranking.

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Step 8: Introduce two error functions for every pair of consecutive alternatives.

Step 9: Solve the linear program, which minimizes the under (over) estimation errors of the reference sites, resulted from the DMs’ weak order.

Step 10: Check the consistency of the solution, and if z=z *=0 (implying that more than

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one solution provides the same ranking of the reference set as the one stated by the DM), perform robustness analysis.

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Step 11: In case of inconsistency, return to previous steps and repeat the procedure by requesting feedback by the DM.

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Step 12: When consistency is achieved, perform robustness analysis by means of MAX-MIN technique and the ASI index, and also apply the SMAA method. When the

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robustness is judged as satisfactory by the analyst go to Step 13. Otherwise go to

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Step 5 to enrich the reference set with new preference statements. Step 13: Implement the mean value (barycenter) model, produced by random value functions, belonging to the polyhedron formed by the problem’s constraints, and compute the global value of the alternative sites. Step 14: Identify the alternatives with the best performance and estimate their frequency according to the produced random value functions. Step 15: Select the most suitable alternative according to the preferences of the DMs. 17

ACCEPTED MANUSCRIPT 4. Case Study 4.1 Siting a landfill in northeastern Greece The present GIS-based suitability analysis aims at ranking candidate sites for the placement of MSW landfills in the Thrace region of northeastern Greece. The Thrace region consists of three prefectures (Xanthi, Rodopi and Evros; Samothraki island is not included in the study), borders Turkey, at the Evros river, to the east, Bulgaria to

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the north and the Kavala prefecture to the west (Figure 4).

The suitability analysis is performed in an area of 8578 km 2, considering a 50x50 m2 cell size for the raster analysis. In the region, there are rivers (Nestos, Evros), wetlands, the Vistonis Lake and important environmental areas protected by the

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Natura 2000 network.

Figure 4: Case study area

4.2 Site selection criteria The MSW landfill siting task necessitates taking account of a large number of spatially related factors to guarantee the efficiency of the landfill facilities. However, a landfill’s efficiency depends not only on the DM’s preferential system but also on

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ACCEPTED MANUSCRIPT the geomorphological characteristics of the candidate area and the existing legislative framework. Because the landfill site selection problem demands the implementation of both criteria and attributes that are related to the examined area’s characteristics, there cannot exist a unanimously accepted objective tree that can be implemented in all possible cases. This is the reason why international and national regulations do not impose buffer zones, in most of the criteria, but provide

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generic guidelines that can be adjusted based on the boundary conditions of the examined area.

In addition, the stakeholders’ opinion should be taken into consideration to improve transparency and public participation in environmental decision making. In that regard, local authorities, environmental management, environmental experts

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(universities, research institutes, etc.), and environmental associations suggest that the criteria should be applied in the analysis according to their points of view (Geneletti, 2010).

The criteria that are applied to locate a landfill were selected according to the high

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frequency of their presence in landfill suitability analyses (Demesouka et al., 2014), and their threshold values were formed according to the national legislation

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framework, when buffer zones are imposed or for safety reasons to avoid the NIMBY phenomenon. The criteria are grouped into three categories: land availability,

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natural resources protection and socioeconomic criteria, and they are subdivided into exclusion (constraints) and non-exclusion (decision criteria) according to their

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use during the site selection process. The primary role of exclusion criteria is the prevention of placing a landfill in areas where doing so is either forbidden by law or

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not in compliance with the DM’s preferential system. As a result, these criteria are applied only during the constraint analysis to define the feasible solutions set. In contrast, the decision criteria are applied in the suitability analysis to rank all the feasible sites resulting from the previous preliminary site screening process (Charnpratheep et al., 1997). In many cases, decision criteria enforce the application of buffer zones around specific areas (e.g., the criterion of distance from residential areas) during the feasible solutions process to conform to national and international

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ACCEPTED MANUSCRIPT regulations related to MSW landfill siting (Greek Governmental Ministry Decision 114218/97 1997, EU directive 99/31/EC).

Natural Resources Protection Criteria This category is composed of criteria concerning the study area’s surface water and

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coastline, in addition to areas of exceptional ecological interest that are protected by national and international conventions. The goal of these criteria is to reduce the water contamination risk due to leachate pollutants of the candidate sites.

g1 - NATURA 2000: Areas of unique recreational value that are protected by the

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European Biotopes Network Natura 2000, as denoted by the Greek legislation (114218/97 (1997)) and the Ramshar Convention of 1971, are considered totally unacceptable for placing a landfill. Although the legislation demands only the exception of those areas, for safety reasons, a buffer zone of 500 m (Kontos et al.,

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2003; Gemitzi et al., 2007) is formed around Thrace’s environmentally protected areas to guarantee the study area’s ecology.

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g3 - Surface Water: This criterion refers to the adequate distance away from surface water bodies candidate sites must be to minimize the risk of surface water body

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contamination. According to the Greek legislation (114218/97 (1997)), landfill siting is prohibited in areas where water bodies exist, and a 500 m buffer zone is assigned

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around the study area’s lakes and rivers, streams, wetlands and ponds for safety

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reasons.

Socioeconomic Criteria This category includes criteria related to the effects a landfill may have on the study area’s ecology. These criteria aim to prevent aesthetic and economic deterioration of the candidate sites due to the implementation of MSW landfills. For these reasons, an adequate distance is formed around residential or economic exploitation areas to prevent public contrariety and bolster the area’s economic development.

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g2 - Residential Areas: This criterion is one of the most crucial because it is mainly responsible for the scarcity of appropriate sites for landfill siting due to the NIMBY syndrome. According to the Greek legislation (114218/97 (1997)) and European directives (EU 1999/31/EC), a 500 m buffer zone around residential sites is necessary to minimize public annoyance due to bad odors and optical and health intrusion

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phenomena (Charnpratheep et al., 1997; Demesouka et al., 2013a). Moreover, the 500 m zone is deemed necessary to prevent possible land value deterioration due to the addition of such obnoxious facilities (Siddiqui et al., 1996; Sharifi & Retsios, 2004). In addition, because the Greek law 114218/97 prohibits landfill siting in areas where historical or cultural sites exist, these locations are excluded from the landfill

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siting analysis, and to avoid inciting public opposition, a 500 m buffer zone is formed. g4 - Transportation Network: This criterion refers to the necessary distance away from a transportation network that a landfill should be to minimize the visual impact and other disamenities resulting from locating such a construction, thus enhancing

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the study area’s tourist development (Leao et al., 2004; Chang et al., 2008). Thus, a 500 m buffer zone is maintained around Thrace’s primary road network, railway line

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and highways. Although this criterion could be applied also as a decreasing one if minimization transportation distances of created waste (tonne kilometres per year)

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are to be taken into consideration.

Land Availability Criterion

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g5 - Slope: The land incline is a factor that should be taken into account during the siting analysis due to the high risk of groundwater contamination and due to construction cost impacts. For instance, landfill siting in steep areas increases the risk of polluting the nearby groundwater aquifers due to the leakage of derivatives. Moreover, because excavation and ground formation costs in sloped sites are extremely high, in addition to their instability of construction and difficulty of maintenance (Kao & Lin, 1996), flat areas are given the highest grade for suitability.

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ACCEPTED MANUSCRIPT Thus, areas with a slope greater than 10% are excluded from the analysis, and the remaining areas are scored in a decreasing manner.

The first four criteria are distance criteria. All information about the decision criteria used in this case study is summarized in Table 1.

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Table 1: Evaluation criteria (aspiration levels and evaluation scales) Criterion

Preference sense

Aspiration level

Evaluation scale

g1

NATURA 2000

Ascend

500 m

[500 – 20500] m

g2

Residential Areas

Ascend

500 m

[500 – 10500] m

g3

Surface Water

Ascend

500 m

[500 – 12500] m

g4

Transportation Network

Ascend

500 m

[500 – 12500] m

g5

Slope

Descend

10%

[0 – 10] %

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Notation

Figure 5: Feasible solutions set

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ACCEPTED MANUSCRIPT 4.3 Constraint analysis After the selection of the site evaluation criteria and the definition of their target levels (Table 1), the constraint analysis can be conducted to identify all the possible areas that meet the above requirements (legislative framework limitation and safety concerns). In this phase, the criteria with forbidden zones (either exclusion criteria or decision criteria with buffer zones) are applied to reject from the GIS-based

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suitability analysis areas totally inappropriate for placing a landfill, thus limiting the field of evaluated candidate locations that simultaneously satisfy the analysis constraints. The designated threshold/aspiration levels are applied via Boolean operators; thus, the alternative sites where the multicriteria analysis is to be performed are defined. Figure 5 illustrates the set of feasible solutions for landfill

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siting in the examined area, derived after the constraint analysis implementation.

Figure 6: S-UTASTAR Method Implementation

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MC-SDSS

USER

SDSS

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Shared files

GIS

Figure 7: Loose coupling approach (Malczewski, 1999) 5. Implementation and presentation of the results

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The computational part of S-UTASTAR is supported by an add-in implemented in the ArcGIS v.9.2 environment (Figure 6). The add-in was developed with the use of Visual Basic for Application (VBA) programming language, which enables the efficient management of the ArcObjects supported properties and methods in ESRI ArcGIS software. The loose coupling architecture was applied in the developed SDSS

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because the linear program solutions are obtained in collaboration with Microsoft Excel (Figure 7). This approach is popular because it keeps the advantages of both

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systems, i.e., the GIS ability to store large volumes of spatial data and Excel’s ability to rapidly compute complex linear programs (Burrough et al., 1998). A spreadsheet

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operating in the background of the user interface in ArcGIS stores the analysis parameters related to the linear program solutions, which are sent to Microsoft

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Excel; the results are then reverted to ArcGIS. These solutions are utilized for the site suitability index estimation. Thereafter, the SMAA technique is applied for the

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estimation of the frequency of the best ranked alternatives to facilitate the decisionmaking process.

5.1 S - UTASTAR implementation At first, when the DM launches the SDSS, he/she has to select the number of criteria (Figure 6A) and define the criterion maps (i.e., the maps resulting from the feasible solutions implementation) for the suitability index mapping estimation, as illustrated 24

ACCEPTED MANUSCRIPT in Figure 6B. Afterwards, the  parameter, a small, positive number expressing the minimum difference in the values of two consecutive alternatives in the ranking (Jessop, 2004), is defined, which is set to 0.001 in our study (Figure 6C). It should be noted that each criterion scale has already been divided into four equal intervals; see Figure 2 for the case of the first criterion and Figure 8 for the complete set of the twenty variables wij.

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After defining the analysis criterion maps, the SDSS is coded to construct a random set of reference alternatives among the set of feasible solutions. As a simplification, it is considered that the number of random reference alternatives should be at least twice the number of criteria, since the number of unknown variables in the additive model is 5×4=20. Consequently, to obtain a single solution, there is a need for 19

of weights) that should equal to 1.

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linearly independent equations, the 20th equation involving a double sum of wij (sum

At the beginning of the disaggregation phase, the SDSS randomly selects 10 alternatives from the feasible solution set; their performances under the criteria

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complete the decision matrix of the problem (Figure 6D). Thereafter, the DM has to rank the 10 alternatives in a weak order, thus implicitly declaring his/her value

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system. In the present analysis, two experts from the Greek Institute of Geology and Mineral Explorations and the Water Resources Department of the Eastern Macedonia and Thrace Regional Authorities served as DMs.

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Very recently Matsatsinis et al. (2017) proposed an intelligent preference elicitation technique based on pairwise comparisons to aid decision makers in externalizing

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their ranking of reference actions and progressively increase the robustness of the additive value model. On the other hand Kadzinski et al. (2017) state that a practical

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usefulness of this approach is influenced by both expressiveness of the assumed model and robustness of the recommendation computed with its use. Then they experimentally evaluate the above characteristics in view of using an additive value function

in

the

preference

disaggregation

context.

As depicted in Figure 6D, according to the DMs’ preferences, the best alternative is the 9th alternative and the worst alternative is the 6th; thus, all reference sites are arranged in a completely strict order (without any equalities). Finally, the S-UTASTAR algorithm implementation can be performed as described previously to optimally 25

ACCEPTED MANUSCRIPT calculate the variables wij and the corresponding ranking of the alternatives in the AR by solving the UTASTAR linear program, which minimizes the under/overestimation errors. This linear program is shown in Table 2; each linear constraint is represented in a column.

Table 2: UTASTAR basic linear program (the coefficients -1/+1 of the σ+/σ- over and under – estimation errors are omitted). 0

-0,183

0,183

-1

1

0

W12 W13

0 0,848

-1 -1

0,56 0

-0,56 0

1 1

-0,521 -1

W14 W21

0,039 0

-0,039 -0,892

0 0,892

0 -0,398

0,751 -0,594

-0,751 0,992

W22 W23

0,898 1

-1 -1

1 1

-1 -1

0 0

0,627 0

W24 W31

0,782 0,096

-0,782 0

0,742 0

-0,742 -0,044

0 -0,832

0 0,728

W32 W33

1 0.161

0 0.157

0 0.682

-1 -1

0 0

0 0

W34 W41

0 0.846

0 -0.452

0.008 0.452

-0.008 0

0 0

W42 W43

1 0.161

-1 -0.161

1 1

-0.646 -1

W44 W51

0 0

0 0

0.453 0

-0.453 0

W52 W53

0 -0.6

0 0.08

-0.44 -0.48

0.24 0

W54 σ-σ+

-1 …

0 …

0 …

2nd Member

 0,001

 0,001

 0,001

0

-0,33

0,33

1

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W11

-0,572 0

0,073 0

1 1

0 0

0 0

0 -1

1 1

0,373 1

-0,376 -1

-0,624 0

1 1

0,148 0,144

-0,148 -1

0 1

1 1

1 1

-1 -1

1 1

1 1

0 -1

0.35 0.186

-0.35 0.814

0.117 0

1 1

0.646 1

-1 -1

0 0

1 1

-0.349 -1

1 1

0.967 0

-0.967 -0.88

0 -0.12

0.699 1

-0.699 -0.16

1 1

0.2 0.08

-1 -0.08

0 0

1 1

-1 -1

1 1

0 …

0 …

0 …

0 …

0.4 …

-0.4 …

1

 0,001

 0,001

 0,001

 0,001

 0,001

 0,001

=1

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0,093 0

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The previous linear program gives an optimal solution with [min]z=0, implying multiple solutions that provide the ranking of the reference set stated by the DM. In

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particular, there are infinite number of additive value models that comprise the convex polyhedron of the Table 2 constraints. In the frame of the robustness control

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during the disaggregation phase, 10 extreme optimal models are calculated by successively

minimizing

and

maximizing

the

weight

of

each

criterion

pi=wi1+wi2+wi3+wi4, i=1,2,3,4,5, of the polyhedron of Table 2, after the deletion of all errors. This assumes the execution of 20 linear programs. Table 3 shows the variation of weights (min and max underlined and in bold) and the mean weighting solution (barycenter of the polyhedron), while the variation (min and max of all the interval points of the scale) of the marginal value functions are shown in Figure 8.

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ACCEPTED MANUSCRIPT Table 3: Robustness analysis in disaggregation phase producing 10 characteristic optimal value functions. p2

p3

p4

p5

Min p1

0.024791

0.088255

0.276869

0.086782

0.523302

Max p1

0.184009

0.029322

0.441098

0.055135

0.290435

Min p2

0.074264

0.018523

0.344294

0.114708

0.448211

Max p2

0.030387

0.135919

0.232261

0.064036

0.537398

Min p3

0.036486

0.075757

0.203206

Max p3

0.082667

0.047477

0.503551

Min p4

0.127396

0.059278

0.276692

Max p4

0.074264

0.018523

0.344294

Min p5

0.184009

0.029322

0.441098

Max p5

0.036486

0.075757

Mean weight

0.085476

0.057813

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p1

0.61012

0.031337

0.334968

0.027855

0.508779

0.114708

0.448211

0.055135

0.290435

0.203206

0.074431

0.61012

0.326657

0.069856

0.460198

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0.074431

The application of Equation 7 to Table 3 shows that the ASI takes the value of 0.889,

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which is judged as satisfactory by the analyst. Given also that the variation of the marginal value functions (Figure 8) and the weights (Table 3) are acceptable, the

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analyst decides to proceed to the aggregation phase to rank all the landfill site alternatives. The fact that the inferred decision model is not a single value function

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but a whole polyhedron of such functions (set of constraints in Table 2) drives the

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analyst to activate the SMAA method.

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Figure 8: Barycentral additive value model and variance of the parameters: ug   0.075u1 g1   0.076u 2 g 2   0.306u3 g 3   0.065u 4 g 4   0.478u5 g 5 

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5.2 Application of the SMAA method The implementation of the SMAA method requires that the inequality and equality costraints of the problem are considered during the production of random value functions, belonging to the polyhedron, formed based on the problem constraints in Table 2. The number of randomly produced value functions is set to 100 by the analyst, and the estimated mean value function (barycentral model) is then computed as the most representative of this polyhedron; this function is presented in Figure 8. Of course, the sample of 100 random value functions could be 28

ACCEPTED MANUSCRIPT much larger for the calculation of the SMAA indices. In the context of this study, given the quality of the results obtained in Tables 4 and 5, the decision analyst did not consider it necessary to increase the sample size and repeat the same computational process. Afterwards, with the use of the barycentral function, the global values of all 412.430 alternatives are calculated to estimate the site suitability index of the problem, as presented in Table 4 (head of the ranking)

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and Figure 8. As every cell of the examined area is considered to be a discrete alternative, the additive overlay operator is used to enable continuous suitability mapping. This is achieved by converting each criterion map into a common scale, with the use of the marginal value function resulting from UTASTAR, and multiplying

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it by the corresponding attribute weight in Figure 8.

Figure 8: Site suitability index estimation.

In Table 4, the ten alternatives with the highest performance are identified, and the value of each one is computed for all the produced random value functions. Next,

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ACCEPTED MANUSCRIPT their rank frequencies are calculated based on the 100 randomly produced value functions (Table 5). Table 4: The ten alternatives with the highest value Natura 2000 4,610 4,137 9,350 9,399 9,447 5,071 9,302 9,460 9,224 4,504

Residential Areas 9,850 9,409 5,808 5,811 5,814 9,956 5,805 5,764 5,551 7,950

Surface Water

Transportation

Slope

Value

Rank

9,091 8,489 10,292 10,262 10,231 9,459 10,323 10,191 10,224 7,723

11,189 11,025 7,558 7,539 7,521 11,562 7,577 7,474 7,407 12,095

0.350 0.196 0.000 0.000 0.000 1.262 0.146 0.075 0.000 0.000

0.8650 0.8572 0.8557 0.8553 0.8548 0.8535 0.8532 0.8520 0.8518 0.8505

1 2 3 4 5 6 7 8 9 10

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Alternative (ID) 169172 171103 189021 189020 189019 168748 189022 189104 189551 178180

Table 5: Frequency of the ranking positions of the ten alternatives Alternati ve (ID)

1st

2nd

3rd

4th

5th

6th

7th

169172

31

34

0

3

0

0

171103

0

11

34

0

11

189021

17

19

10

20

189020

0

20

23

189019

3

4

168748

30

189022

9th

10th

15

13

4

0

4

0

11

25

4

27

7

0

0

0

0

12

23

15

7

0

0

0

21

23

7

19

19

4

0

0

12

4

0

0

0

4

4

4

42

8

0

4

11

7

14

16

29

11

0

189104

0

0

189551

0

0

178180

11

0

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8th

3

11

21

16

19

22

8

0

6

14

20

10

16

12

22

4

22

0

0

13

4

22

24

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0

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According to the results, presented in Table 5, it is most likely that the DMs will select the 1st site because it has a 65% probability of appearing in either of the first

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two places of the ranking. The same probability is presented for the 7 th alternative, but it has the same probability of appearing in the 10th position, so it is not considered a preferable one. The next best alternative is the 3rd because there is a 100% probability of it being placed in the first six places; the same probability exists for the 4th site with respect to it being placed in positions 2 – 7. This robust argumentation convinces the DMs to stop the decision support process here and to further consider the top-ranked alternatives derived from the SMAA method based on economic, environmental, social, planning, and political aspects.

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6. Discussion This paper is a first attempt to integrate UTASTAR, a well-known representative of the multicriteria aggregation-disaggregation approach, into a raster based suitability analysis for placing MSW landfills and discusses in detail the methodology’s implementation. Moreover, the implementation of SMAA technique, to indicate the

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frequency at which a site achieves the top ranking positions within a large set of alternative landfill sites, enhances the proposed model’s robustness.

The proposed methodology is selected due to its easiness in understanding and implementation, as it only demands by DMs to define a small rank of reference

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alternatives in descending order. Therefore, DMs are capable of expressing their preferential system, based on the alternatives performances on the analysis criteria, to transform the analysis criteria attributes into a common scale using utility functions and estimate the analysis additive value model. In addition, the problem of handling a large number of spatial alternatives is solved with the use of a random

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reference set, produced by the SDSS and coded to be twice the number of the

solutions set.

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criteria. The method’s final solution can then be extrapolated to the whole feasible

The analysis criteria (decision and exclusion) were selected, according to the high

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frequency of their presence in landfill suitability analysis and study area’s specific characteristics, enabling alternatives ranking. The fact that criteria are strongly

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related to the study area’s characteristics is the reason why there are no buffer zones imposed by national or international legislation in force, but rather generic

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guidelines that can be adjusted according to the examined area’s boundary condition. However, the developed MC-SDSS is eligible to insert many other criteria in different case studies, according to DMs and stakeholders preferences or the available data of the area under investigation. 7. Conclusions The study at hand presents S-UTASTAR, an extension of the UTASTAR ordinal regression method to spatial decision-making analyses. The application of S-

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ACCEPTED MANUSCRIPT UTASTAR is illustrated by a raster-based case study aiming to rank the candidate locations for placing an MSW landfill in a region, according to their appropriateness and the DMs’ preferential system. The computational part of the method is supported by an add-in developed as a VBA macro in a commercial GIS package. In addition, the SMAA technique is applied to analyze the robustness of the decision system and to help the DMs during the final selection of the MSW landfill site. The

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SMAA technique, with the use of simulation techniques, provides the DMs with indices for statistically measuring the ranking position of the top-ranked alternatives. The proposed MC-SDSS is rather easy to use and handle because the user only has to define a small ranking of reference alternatives in descending order of attractiveness based on their performances. In that regard, a large number of alternatives can be

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handled because, with the use of a random set of alternatives, DMs are able to express their preferential system, and the mean value (barycenter) model, produced based on the random value functions, can then be extrapolated to the whole set of alternatives. Furthermore, the final additive utility function, depicting the effect of

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criteria on the site suitability index estimation, makes the results comprehensive. Potential feedback can also be exploited to resolve any problems that may arise

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from the assessed order and thus increase the efficiency of the developed MC-SDSS. Moreover, the model is easy to apply due to the fact that after a DM determines the

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weak order, the criteria standardization functions are formed solely according to the presented methodology. As a result, DMs who are not familiar with definitions such

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as criteria standardization methods and criteria weights elicitation methods are able to express their preferential system only by ranking the alternatives of the reference

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set.

Bearing in mind the flexibility of multicriteria decision analysis methods to handle conflicting objectives, the proposed MC-SDSS can be proven to be extremely useful in a variety of facility siting and land-use planning approaches. Even though there is no way to establish superiority between MCDA methods, the use of other well-established multicriteria methods would enhance the quality of spatial decisions. Moreover, as every method demands different degrees of information from DMs (standardization methods, weights elicitation methods, 32

ACCEPTED MANUSCRIPT pairwise comparison, etc.), users can choose to follow the approach that they are most familiar with. As a result, an integrated system composed of different and interrelated multicriteria approaches should become a useful support tool in realworld planning problems.

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Acknowledgements Part of the current research has been funded by the "IKY FELLOWSHIPS OF EXCELLENCE FOR POSTGRADUATE STUDIES IN GREECE - SIEMENS PROGRAM".

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