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ISSN 0734–242X Waste Management & Research 2009: 27: 641–651

DOI: 10.1177/0734242X09103832

Developing a fuzzy decision support system to determine the location of a landfill site Maria C. M. Alves Federal University of Rio de Janeiro, Polytechnic School, Rio de Janeiro, Brazil

Beatriz S. L. P. Lima Federal University of Rio de Janeiro, Polytechnic School, Rio de Janeiro, Brazil; Federal University of Rio de Janeiro, Coppe-Civil Engineering Program, Rio de Janeiro, Brazil

Alexandre G. Evsukoff, Ian N. Vieira Federal University of Rio de Janeiro, Coppe – Civil Engineering Program, Rio de Janeiro, Brazil

This paper presents two case studies of municipal solid waste site location using a decision-support system based on fuzzy logic. This problem is very complex, as it requires the evaluation of different criteria, which involve environmental, social and economic data. Such data deal with a wide range of information that presents not only quantitative, but also qualitative knowledge. In order to deal with this characteristic, the developed system employs fuzzy rules due to its ability to treat linguistic variables and the human way of thinking. Conventional approaches tend to be less effective in dealing with the imprecise or vague nature of the linguistic assessment. A case study for selecting the location of a new municipal solid waste landfill for the city of Petropolis in Rio de Janeiro is presented. Testing of the proposed method was carried out using data from the municipal solid waste location for another municipality in Rio de Janeiro, Brazil. Keywords: Waste management, fuzzy logic, decision-support system, solid waste landfill, site location

Introduction Population growth followed by industrial and technological development produces one of the greatest urban problems, which is the increasing generation of solid waste resulting from activities of different origins such as industry, home, health services, agriculture, etc. In Brazil, the reduction of high inflation rates since the mid-1980s has caused an increase in consumption patterns, which has led to a consequent increase of solid waste generation by 15 to 20% above the population growth rate. This fact has helped to worsen the solid waste situation in Brazil because of the chaotic final disposal practices in most of the country that contributed to the increase of a series of problems in social, environmental, educational and economic areas. On the other hand, developed countries have already planned their strategy for this area based on a compromise of a progressive reduction of their solid waste generation disposed into landfills up to 35% by 2020 (Ferreira et al. 2002). Imposition of adequate environmental management of

solid waste materials all over the world was chosen as a decision after the ECO-92 Conference. In addition to that, the validation of the Kyoto’s Protocol after the signing by the Russian government in February 2005 turned the trading of carbon credits into a powerful tool for financing environmentally correct practices in solid waste disposal. Final disposal in most developing countries has been just a matter of transporting the collected waste to the nearest available open space and discharging it without any special care, leading to the so-called dumpsites. In Brazil, a great governmental effort is on course in order to minimize inadequate municipal solid waste (MSW) disposal. Nevertheless, the selection of new landfill sites is not a straightforward task. Many aspects, such as environmental features, social impact assessment, and cost considerations must be accounted for in order to point to an adequate management of MSW.

Corresponding author: Professor Beatriz Lima, PO Box 68.506, Rio de Janeiro 21945-970, Brazil. E-mail: [email protected] Received 23 July 2008; accepted in revised form 15 November 2008 Figure 3 appears in color online: http://jrp.sagepub.com

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M. Alves, B. Lima, A. Evsukoff, I. Vieira

The increasing complexity of MSW management and the uncertainty involved in the problem domain has encouraged the development of many computer modelling tools. Knowledge-based systems have been used to provide guidance in the waste management and planning field. Several optimization methods associated with knowledge-based systems have been largely employed in solid waste management. Some of the original papers providing intelligent support in decisionmaking for waste management are Barlishen & Baetz (1996), Chang & Wang (1996) and Chang et al. (1996). Those papers involve strategies for optimal recycling and incineration programs planning. Barlishen & Baetz (1996) developed a knowledge-based system to assist with the preliminary planning of MSW management systems. A decision support tool is designed where several cost and operational parameters must be provided in order to minimize costs of different recycling schemes. Chang et al. (1996) included environmental constraints in the cost optimization process. MacDonald (1996) employed a multi-criteria analysis model in a spatial decision-making procedure consisting of combining interfaces of different software. In Haastrup et al. (1998), the goal of the developed decision-support system (DSS) was to compare different waste treatment and disposal scenarios (landfill, incinerator and composting) and rank them in terms of a previously defined set of criteria. As uncertainty is an important factor in waste management problems, many researchers applied fuzzy set theory to address uncertainty in decision-making. Optimization analysis for waste management employing fuzzy sets were used in Koo et al. (1991), Chang et al. (1997) and Chang & Wei (2000). Fuzzy sets have also been applied in other environmental applications as a logic controller of waste incinerators (Chang & Chen, 2000 and Chen et al. 2002). Recent studies combined geographic information systems (GIS), fuzzy logic and multi-criteria evaluation techniques in many environmental applications. In Gemitzi et al. (2007), the developed methodology for the problem of MSW landfill siting was applied to Evros prefecture, in the northeast of Greece. In Chang et al. (2009), the developed model evaluated the environmental impacts due to incineration projects in different areas in order to help the allocation of any fair fund distribution among the affected communities. In the present study, a fuzzy decision computational tool (SADLAS) that was able to integrate qualitative and quantitative information as well as incorporate the vagueness and imprecision due to this type of decision-making process was developed. Issues that involve linguistic knowledge and uncertainties inherent to human thinking, such as social economic ones, may play a decisive role in landfill site selection. The main contribution of this work is the treatment of the qualitative information included in the criteria for selection of a new MSW landfill site. This paper is organized as follows: initially a brief review of the MSW disposal scenario in Brazil is presented together with the criteria used for the evaluation of the conditions for siting a new MSW landfill. A general description of the fuzzy model on which the DSS is based follows and the decision criteria employed in the developed system is then defined.

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Finally, the application of the proposed system is presented with reference to two real case studies.

Landfill overview Brazilian scenario In Brazil, a systematic garbage collecting system was officially started in 1880, in the city of Rio de Janeiro, the capital of the country at that time. From then on, the urban collecting system has mostly been the responsibility of each specific municipality and has experienced both good and bad performances. A collecting system must invariably be followed by final disposal of the waste, which is not always adequate in Brazil. Brazil is a large country that is composed of five geographic regions, which are quite different with regard to their size, economics, demographic density and cultural background. It has 5507 municipalities of which more than 70% have less than 20 000 inhabitants, although the urban concentration of the population is around 80% in this country. According to the latest research for National Basic Sanitation (PNSB 2000) carried out by IBGE (Brazilian Institute for Geography and Statistics), the amount of collected urban waste in Brazil is around 230 000 tonnes day–1. The per capita generation of MSW in Brazil has a strong link to the economic development as well as the environmental engagement, the latter being strongly connected to the population education level. The South-east region is the most industrialized one, contributing 58% of the whole internal gross product of the country generating 62% of the total MSW in Brazil (Jucá 2003). The North and Northeast regions are the poorest ones, generating less than half of the Southern region. On the other hand, the lowest per capita generation is performed by the South region, which is the second-most developed in the country. Low per capita MSW generation in this case is due to the environmental policies already in place in this region, rather than due to economic affairs. The MSW disposal scenario in Brazil is not very positive since 63.6% of the municipalities have their MSW disposed in dump sites compared to 32.2% in proper landfills (Jucá 2003). Smaller communities, mostly the poorer ones which do not generate much weight of garbage, are identified as the ones that are responsible for the majority of the localized environmental pollution. The need to adjust its disposal practices as soon as possible must be accompanied by the search for new areas for this purpose.

Site location evaluation There is not, in nature, a place which is considered ideal for landfill siting. Nevertheless, the more careful the evaluation of the available areas for siting is, the smaller the risk of turning it into an environmental problem and the lower the siting and operation costs will be. The most adequate area will be the one that best fits all the pertinent criteria, either technical, environmental, social, economic or even legal. Legal aspects

Legislation related to the environmental protection in Brazil pointed to a clear course when the Federal Law No. 6938 in

Fuzzy decision support system to determine the location of a landfill site

1981 created the National Council for the Environment (CONAMA), which deals with the national environmental policy and established a series of regulations. It also qualifies the environmental impact as an instrument of management which obliges the previous licensing of the competent State Environmental Agency for any new landfill. Legal aspects related to preservation of vegetal species were also established by federal legislation (in particular, one can focus on Decree No. 563) in 1992 that started the Pilot Program for the Protection of the Tropical Forests in Brazil. In addition, some resolutions like CONAMA5/1993 and CONAMA12/1994 deal with subjects related to Mata Atlântica, a very important forest located beyond the coast of Brazil. In this way, legislation for the protection of the natural heritage led to the creation of the so-called environmental protected areas (APA) that must be protected from any environmental impact caused by human intervention. State Environmental Agency policies differ from one another, although they have the common responsibility of analysing the impact of any new enterprise in order to ensure that it keeps proper sanitary conditions as well as does not cause excessive harm to the environment. Technical and environmental aspects

The more important environmental points to be aware of are related to the geological, geotechnical and hydro geological conditions, protection of water bodies, and meteorological conditions. Geotechnical conditions of the foundation and borrow areas, depth of water table, distance to the surface water bodies, as well as topographic characteristics of the area must be taken into account. The Brazilian technical norm for design criteria, siting and operation of a MSW landfill (NBR 13896-97) establishes many recommendations, the most important ones deal with limit values for: permeability of the foundation soil, depth of ground water level, distance to the nearest surface water body, and ground slope. A number of other conditions that are not so easily put into numbers must be evaluated, as for example, the conditions of assessment routes, proper use of the soil, occupation of access routes, acceptance of local communities, cost of the land, availability of cover material, political issues and so on. Additionally, some operational data, such as distance to residential areas, distance to the collect centre, and good conditions of accesses for heavy trucks must be taken into account. Decision criteria

The developed tool (SADLAS) deals with a multi-criteria decision-making system, which will be discussed in detail in next section. This system involves some conditions mentioned above and many other decision criteria that must be considered when selecting a landfill site. In this paper, those criteria are divided into three groups. The first group deals with environmental criteria as described below. The Brazilian Norm NBR 13896/1997 requires the minimum values for some of those criteria.

1. Distance to surface water bodies: the landfill sites cannot be sited at a distance less than 200 m from any water body. 2. Soil permeability: it is desirable that the soil presents a low hydraulic conductivity to avoid contamination by landfill leachate. The soil permeability must be less than 5 × 10–5 cm s–1. 3. Depth to the ground water level: the distance between the landfill bottom surface and the highest level of the ground water must be at least 1.5 m. 4. Distance from airports: landfill sites must be placed no closer than 3 km to any airport. 5. Extension of drainage basin: the extension of the catchment’s area must be as small as possible in order to avoid large water volumes in the landfill site. 6. Land use: the site must be out of environmentally protected areas and should be in industrial or agricultural areas. Four social criteria compose the second group, as listed below. The last three criteria are considered as qualitative criteria. 7. Distance from residential areas: it is recommended to be a minimum distance of 1000 m from residential areas. 8. Distance from low-income communities: waste deposit sites can attract low-income and unemployed people in an attempt to take their living out from landfill. This fact can cause a serious social problem for municipalities that need to implement sustainable mechanisms for employment of these people. 9. Occupation of access routes: it is desirable for truck traffic to occur within areas with low demographic density. 10.Problems with local communities: the acceptance level of the surrounding communities must at least be satisfactory. The third and last group is composed of economic criteria that are mainly qualitative criteria (except for the lifetime and ground slope). 11.Availability of cover material: it is recommended that a great amount of cover material is available near the landfill site. 12.Lifetime: the minimum lifetime should be at least 10 years. 13.Land cost: a good negotiation for land use is always desirable. 14.Investment in infrastructure: complete infrastructure is desirable so that it will minimize installation costs. 15.Access to heavy trucks: good pavement roads without hard ramps and curves are the best conditions for heavy trucks traffic. 16.Distance from the collect centre: it is expected that distances should be as small as possible from the collection centre to reduce costs. 17.Ground slope must be between 1 and 30%. The local morphology is supposed to facilitate the leachate collection system for the treatment before effluent discharge into the water bodies.

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Those three groups were implemented separately in the system. In this way, the decision-maker can access not only the global grade but also the grades for each particular group. The global grade is the weighted average of the three groups, so different weights can be furnished for each group according to the decision-maker’s judgment. The criteria described above represents quantitative and qualitative information and some of the measures may be corrupted by uncertainty, such that the decision problem is difficult to be modelled using classical multi-criteria decision methods (Cho 2003). In this work, a fuzzy decision approach is used in order to integrate qualitative and quantitative information, to incorporate the uncertainty and to sweep off the discontinuity from decision process as much as possible, as described in next section.

The fuzzy decision approach In the fuzzy system’s approach for multi-criteria decisionmaking (MCDM), each decision is considered as a fuzzy set defined in the domain defined by the criteria. This section describes the representation of a fuzzy rule-based MCDM approach, which is based on the fuzzy pattern matching approach (Dubois et al. 1988). Consider the criteria as input variables and the possible decisions represented by the set D = {D1 … Dm}. The solution to the decision problem is to assign a decision Dk ∈ D corresponding to an observation set of the criteria.

Fig. 1: A triangular fuzzy partition.

ui(t) =  µ Ai1 ( x i ( t ) )…µ Ain ( x i ( t ) )



(2)



i

where µ Aij ( x i ( t ) ) is the fuzzy membership function of the variable to the fuzzy set Aij. If the variable is nominal then the fuzzification vector is a binary vector, with a unitary membership corresponding to the observed nominal value and zero membership to all other values.

Fuzzy rules A fuzzy rule relates input linguistic terms Aij ∈ Ai to the decisions Dk ∈ D in rules written as: if xi(t) is Aij then the decision is Dk

(3)

Fuzzy rules can be written with two or more variables in the rules premises as:

Fuzzification In a general application, the input variables’ values may be numeric (discrete or continuous) or nominal. Fuzzy sets allow a unified representation for nominal and numeric variables as fuzzy sets. Each input variable xi can be described using ordered linguistic terms in a descriptor set Ai = { A i1, …, A ini }. When the variable is nominal, the descriptor set is the set of possible values for the variable (or a combination of them). When the variable is numeric, the meaning of each term Aij ∈ Ai is given by a fuzzy set defined on the variable domain. The process of computing such fuzzy sets is known as the fuzzification of the variable, that is an important issue since it provides the linguistic-to-numeric interface that allows dealing with variable values as linguistic terms. In order to simplify computations, fuzzy sets are computed by strong fuzzy partitions of the input variables domain, such that:

∀x i ( t ),

∑

µ Aij ( x i ( t ) ) = 1

(1)

if zi(t) is Bij then the decision is Dk

(4)

where zi(t) ∈ Xq, q < p, represents a subset of input variables that are considered in the multidimensional rule and Bij is a fuzzy set in the multi-variable fuzzy partition defined over Xq. For applications with a large number of variables, reasonable results can be achieved using partial output aggregation of mono-variables sub-models (Evsukoff et al. 1997). The fuzzy rule describes a flexible constraint on the values of the variable xi that can be related to the decision Dk. For instance, if the variable xi represents the ‘soil permeability’, then an example of the rule (3) could be: ‘if the soil permeability is high then the location is BAD’. i In this work, the confidence factor is ϕ jk . A set of rules (or a rule base) for each input variable defines a sub-model that can be represented by the matrix Φi as shown in Table 1. In such a matrix, the lines j = 1…n i are related to the terms in the input variable descriptor set Ai and the columns

j = 1…n i

D1

…

Dm

Ai1

Φi (Ai1, D1)

…

Φi (Ai1, Dm)

…

Φi

…

…

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Table 1: Rule base weights’ matrix.

…

An easy way to parameterize such fuzzy partitions as Figure 1 is to use triangular membership functions that are completely determined by the centres of triangles, which may be considered as prototype values for the corresponding fuzzy sets. For each input variable (criterion), the result of the fuzzification is the fuzzification vector that generalizes the information contained in the input variable. It is computed in the same way either the variable is numeric or nominal as:

Aini

Φi (Aini, D1)

…

Φi (Aini , Dm)


Fig. 2: The weighted fuzzy classifier.

k = 1…m are related to decisions in the set D. The values i ϕ jk ∈ { 0, 1 } represent the rules linking the term Aij ∈ Ai to i the decision Dk ∈ D, such that a value ϕ jk = 1 means that the observation of the term Aij is related with the decision Dk and corresponding rule is present in the model. The rule base is the kernel of the fuzzy decision model. The fuzzy inference model is consistent if the confidence faci tor represented by values ϕ jk ∈ [ 0, 1 ] are used in the rule base, as is often the case in classification problems (Evsukoff et al. 1997). Nevertheless, factionary rule weights are difficult to assign by human experts and only binary rule weights were used in this work.

Fuzzy inference The fuzzy inference computes partial outputs for each submodel from the input variables values. There is one fuzzification vector for each sub-model, but a sub-model may be related to more than one input variable. The choice of which variables must be considered in a multi-variable sub model is application dependent. The output of the fuzzy system is the decision membership vector (or the fuzzy model output vector) y(t) =  µ ( x ( t ) ), …, µ ( x ( t ) ) , where each component µ ( x ( t ) ) Dm Dk  D1  is computed from an x(t) in two steps (Dubois et al. 1988): 1. compute a partial output yi(t) for each sub model, and 2. compute the final output y(t) by aggregation of the partial outputs. The partial output is the vector yi(t) =  µ D1 ( x i ( t ) ), K,  µ Dm ( x i ( t ) )  , of which the components represent the mem bership value of the possible decisions considering only the information in the sub model i. Adopting strong normalized fuzzy partitions and using the sum-product operator for the fuzzy inference, the partial output is computed from the membership vector ui(t) and the rule base weights’ matrix Φi by the max-min fuzzy composition operation as:

  µ Dk ( x i ( t ) ) = max  min  µ Aij ( x i ( t ) ), Φ i ( A ij, D k )    j = 1…n  

(5)

The final output is computed by the aggregation of all partial conclusions yi(t) by an aggregation operator H : [0, 1]p → [0, 1] as:

µ Dk ( x ( t ) ) = H  µ Dk ( x 1 ( t ) ), …, µ Dk ( x p ( t ) )  

(6)

The best aggregation operator must be chosen according to the semantics of the decision. A conjunctive operator, such as the ‘minimum’ or the ‘product’, gives good results for expressing that all criteria must agree. A weighted operator like OWA (Yager 1988) may be used to express some compromise between partial conclusions. The final decision is computed by a decision rule. The most usual decision rule is the ‘maximum rule’, where the decision is chosen according to the greatest membership value. The current approach is flexible enough so that some partial conclusions can be computed from the combination of two or three variables in multi-variable rules. An aggregation operator computes a final conclusion from partial conclusions obtained from all sub-models as shown in Figure 2.

Defuzzification In some applications, such as the one described in this work, it is desirable to get a final score associated to the decision such that intermediate decisions could be analysed. In those cases a defuzzification step is used to compute the final score, by associating a score vector w = [ w 1, …, w m ], where the score wk is associated to the decision Dk ∈ D. The final score is computed as: score (t) =

∑

µ Dk ( x ( t ) ).w k

(7)

k = 1…m

where the decision membership value µ D k ( x ( t ) ) is computed by equation (6).

The fuzzy MCDM approach for location selection The objective of the SADLAS system is to help the decisionmaker when choosing a landfill site following the three groups of criteria mentioned above: environmental, social and economic. The output of the computational system can furnish partial quality grades for each category, so the characteristics of the sites are monitored following those groups. Figure 3 illustrates one of the screens of SADLAS, related respectively to the input of environmental and economic criteria, and to the output of the results. The uncertainties in the data are modelled as quantitative and qualitative criteria. The quantitative criteria represent values mainly defined in Brazilian Norms as NBR 13896/

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Fig. 3: Input of economic criteria.

1997 and other technical recommendations. The qualitative criteria are linguistic values and encode the knowledge of experts. Those criteria involve human subjective judgments that constitute a type of imprecise data that are not easily represented with traditional computing. Thus, fuzzy logic is a more natural approach to deal with those types of problems.

Fuzzification of the criteria Quantitative criteria

These are defined within a certain interval with known lower and upper bounds. The numeric values are subjected to fuzzification so they can be described by linguistic terms. The following trapezoidal and triangular membership functions (Figure 4) are employed here and the respective values are shown in Table 2. Qualitative criteria

These are already expressed in linguistic terms so they are not subjected to the fuzzification process (Table 3).

Fig. 4: Trapezoidal and triangular membership functions.

Fuzzy inference The fuzzy inference receives the fuzzy, linguistic criteria that are used to activate the rules from the rule base. They are in terms of linguistic variables and have fuzzy sets associated with them. The inference engine thus maps the input fuzzy sets into output fuzzy sets, handling the knowledge in a procedure similar to human reasoning and decision-making.

646

The outputs of the decision-support system mean the qualification degree of the sites for the installation of a new MSW landfill. The decision set D = {D1 … Dm} represent the linguistic terms {‘Bad’, ‘Acceptable’, ‘Adequate’}. The rule base consists of 60 rules that describe, separately by groups, the involvement of the criteria on the location


Table 2: Fuzzy sets parameters. Criteria/bounds

a1

a2

a3

a4

200

1000

1500

2000

1.0E–6

1.0E–5

1.0E–4

1.5

2.0

2.5

3

5

7

x1

Distance to surface water bodies (m)

x2

Soil Permeability (cm/s)

x3

Depth to the ground water level (m)

x4

Distance from airports (km)

x7

Distance from residential areas (m)

1000

1750

2250

x12

Lifetime (years)

5

12.5

20

x17

Ground slope

1

10

25

3000

30

Table 3: Criteria and their types of rules. Group Environmental

Social

Economic

Criteria

# Fuzzy sets

Type of rule

x1

Distance to surface water bodies+

4

1

x2

+

3

2 1

Soil Permeability

x3

Depth to the ground water level

3

x4

Distance from airports+

3

1

x5

Extension of drainage basin*

3

2

x6

Land use+

2

4

x7

Distance from residential areas+

4

1

x8

Distance from low-income communities*

3

1

x9

Occupation of access routes*

3

2

x10

Problems with local communities*

3

2

x11

Availability of cover material*

2

3

+

x12

Life time

3

1

x13

Land cost*

3

2

x14

Investment in infrastructure*

3

2

x15

Access to heavy trucks*

3

1

x16

Distance from the collect center*

3

2

4

2

x17

+

Ground slope

+

* Qualitative criteria + Quantitative criteria

qualification. The fuzzy inference can be seen as a ‘linguistic translator’ since it transforms the characteristic of each criterion into a location qualification, according to existing norms. For instance, rules are written as ‘if the land cost is LOW then the location is ADEQUATE’, where the fuzzy inference is relating the adjective ‘LOW’ for the land cost with the location qualification. The fuzzy rules were written directly as an interpretation of the Brazilian norms for landfill site location, such that i ϕ jk ∈ { 0, 1 }. All criteria were related to the decision using one of the options of rule types described as following. Type 1:  If input is ‘small’ then output is ‘bad’ 1 00  0 1 0 ⇒  If input is ‘medium’ then output is ‘acceptable’  0 01  If input is ‘large’ then output is ‘adequate’

    

Type 2:  If input is ‘large’ then otput is ‘bad’ 0 01  0 1 0 ⇒  If input is ‘medium’ then otput is ‘acceptable’  1 00  If input is ‘small’ then otput is ‘adequate’

    

Type 3: 1 0 1 0     

0 0 0 1 If If If If

0 1 ⇒ 0 0

distance is ‘near’ and quantity is ‘small’ then output is ‘bad’ distance is ‘near’ and quantity is ‘large’ then output is ‘adequate’ distance is ‘far’ and quantity is ‘small’ then output is ‘bad’ distance is ‘far’ and quantity is ‘large’ then output is ‘acceptable’

    

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Type 4:

 1 0 0 ⇒  If land use is ‘PA’ then output is ‘bad’   If land use is ‘not PA’ then output is ‘adequate’ 001   Table 3 shows all the criteria and the respective types of rules used to apply the fuzzy inference stage. After fuzzy inference, the partial conclusions for each criterion are aggregated into a conclusion, representing the location qualification degree, separately by each group, which are processed in the defuzzification step.

Defuzzification Experts prefer scores to represent decisions as they allow a better understanding of the results. A numerical final score is thus computed by defuzzification in order to represent the location quality in terms of a quantitative grade. A score vector w = [0,5,10] is used to compute a final score for the landfill by defuzzification (equation (7)), such that the better the landfill location, the greater is the final score. The final global rate is computed as a weighted average of the three group rates previously calculated, thus the highest rate points toward the best site. The user can input these weights to each group based on his priorities. This methodology was applied in two case studies to investigate the performance of the fuzzy system and the results are presented next. The paper of Chang et al. (2008) presents a preliminary investigatory stage for rejecting sites that do not meet statutory requirements. In this work, the authors decided to take a different approach because in many real cases, only a small

number of candidate sites is available and some locations that do not meet the statutory requirements can be submitted to engineering interventions in order to satisfy those requirements. So, the sites that can be improved by such interventions can be evaluated again.

Case studies In the following case studies, the weights of the three groups were equally distributed and the final score is the average of the scores obtained by each group. Decision-makers may find it necessary to furnish different values for these weights, if there is a major concern about a particular group of criteria.

Case 1 The first case studied is a landfill site already operating in a municipality in Rio de Janeiro State called Nova Iguaçu. This case was important to validate the system showing that it worked very well and the results agreed with the decision already taken by the experts. This venture had four available sites that were analysed by SADLAS. The input data of the four sites are shown in Table 4. The output of SADLAS is shown in Table 5. It can be observed that site 4 was the best place indicated by the system to construct the new landfill. This result is in agreement with the final decision that had already been taken by the design consultants so that the new landfill is already in operation on this site, which validates the fuzzy decision-making system. A brief analysis can be made observing the performance of the three different criteria for each site. Site 3 presents the worst performance in the social group. Specifically, it is very

Table 4: Input data – case study 1. Criteria Distance to surface water bodies (m) –1

Site 1

Site 2

Site 3

Site 4

300

1000

2000

2000

–6

–4

–6

10–6

Soil permeability (cm s )

10


1.0

1.0

3.0

3.0


12

15

0

8

Extension of drainage basin

10

10

Medium

Large

Large

Small

Land use

Not PA

Not PA

Not PA

Not PA

Distance from residential areas

2000 m

2000 m

100 m

2500 m

Distance from low-income communities

Medium

Medium

Small

Large

Occupation of access routes

Medium

Small

Large

Medium

Problems with local communities

Medium

Medium

Medium

Medium

distance

Near

Far

Far

Near

quantity

Small

Small

Small

Large

Life time

2 years

15 years

15 years

25 years

Land cost

Low

Low

Low

Low

Investment in infrastructure

Small

Medium

Small

Medium

Medium

Difficult

Easy

Easy

Small

Small

Small

Small

8%

3%

3%

20%

Availability of cover material

Access to heavy trucks Distance from the collect centre Ground slope

648


Table 5: Results of case study 1. Partial grades Group Site 1

Site 2

Site 3

Site 4

Environmental

1731

2253

5000

6154

Social

5000

6000

1167

6722

Economic

4365

5802

7654

8889

Final grade

3699

4685

4607

7255

close to residential areas and to low-income communities. Site 1 presents the poorest performance in the environmental group, mainly due to its proximity to surface water bodies. In this case, the final decision for the best site was very straightforward as far as site 4 shows not only the best final grade but also the best partial grades for all three groups. Consequently, the decision-maker has a clear view without any extra effort toward a more detailed analysis of the partial grades. In some situations, such as the one that is presented in case study 2, it does not turn out this way, and this obliges decision-makers to continue on a more detailed analysis of output results.

Case 2 The second case studied was another municipality in Rio de Janeiro State, the city of Petropolis, where the local government was looking for a new place for MSW disposal. This town has around 300 000 people and is located in a mountain region enclosed by many environmentally protected areas. The city government had three sites as potential areas to

locate the landfill. Figure 5 shows the study area with the three available sites. The input data of those candidate sites are presented in Table 6. The results are presented in Table 7. It can be observed that the final grade is quite close in all three cases, although it points to site 2 as the best one for the new landfill. On the other hand, there is not such an equal distribution of the partial grades among the different groups in comparison with case study 1 presented previously. At this point, decisionmakers must then look carefully at each partial grade to draw an overall picture of the available sites. The final choice is specific to each particular situation. A more detailed observation of site 2 (Table 7) shows that the partial grade for economic group is very low for this site due mainly to its short lifetime. In addition, there is no place for extensions, which does not offer a chance for any improvement to this criterion. On the other hand, as it is already operating as a dumpsite, there are no problems with local communities, leading to a high grade for the social group criteria. In second place comes site 1, which presents quite a low grade for the environmental group, provided it is very close to a water surface body and additionally has a very shallow underground water table. Finally, the third rank is site 3 for two main reasons: the low grade for the social group is due to problems with local communities and the low grade for the environmental group is due to high soil permeability and the proximity of surface water bodies. Among the listed criteria, just a few can be adapted in order to suit environmental issues such as waterproofing of the landfill base and deviation of surface water bodies. The

Fig. 5: Case 2: study area with available sites.

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Table 6: Input data – case study 2. Criteria Distance to surface water bodies (m)

Site 1

Site 2

Site 3

1

600

50

1 × 10

–1

Soil permeability (cm s )

–6

1 × 10

–5

2.56 × 10–5


0.7

2.0

8.4


60

100

110

Extension of drainage basin

–

–

–

PA

Not PA

Not PA

Distance from residential areas

6000 m

4000 m

2000 m

Distance from low-income communities

Medium

Large

Medium

Small

Small

Small

Medium

Small

Large

Near

Near

Near

Land use

Occupation of access routes Problems with local communities distance

Availability of cover material

Small

Large

Large

Life time

quantity

15 years

1 year

25 years

Land cost

Low (*)

High

Low

Investment in infrastructure

Small

Small

Small

Access to heavy trucks

Easy

Easy

Easy

Distance from the collect center

Small

Medium

Medium

–

–

–

Ground slope

Table 7: Results of case study 2. Grades

Grades (with engineering interventions)

Group Site 1

Site 2

Site 3

Site 2

Site 3

Environmental

5000

7115

6154

8077

10 000

Social

7667

10 000

4833

10 000

4833

Economic

8765

4630

9815

4630

9815

Final grade

7144

7248

6934

7569

8216

computation tool allows these two criteria to be modified by engineering interventions in order to enlarge environmental grade. A new analysis of case 2 was run considering engineering interventions in site 2 and site 3. This evaluation indicated site 3 as the best place to install a new landfill, although this site held social problems, as the neighbouring communities were opposed to its implementation (Table 7).

Final remarks The computational system developed in this work is shown to be a friendly tool designed to help the decision-maker in choosing a landfill site among potential available areas based on some criteria. Those criteria were derived from Brazilian norms and the manuals used in MSW landfill design and were treated separately in three groups: environmental, economic and social. The system employed a fuzzy inference engine that mapped the input fuzzy sets into output fuzzy sets, handling the knowledge in a procedure similar

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to human reasoning and decision-making. The rule base on the fuzzy inference consisted of 60 rules that described the involvement of the input criteria on the qualification degree of the sites, meaning the output of the decision-support system. Two cases were run using SADLAS. The first case studied was a landfill site already operating in a municipality in Rio de Janeiro State, and it was able to validate the system showing that it worked very well and the results agreed with the final decision taken previously by the design consultants. The second case studied was another municipality in Rio de Janeiro State where the local government was still in the decision process to choose a site for the new MSW landfill operation. This case was shown to be a more complex problem due to the particular characteristics of the three available sites. Finally, the developed system was shown to be an efficient tool that not merely helps decision-makers in choosing a landfill site but can also give an overall picture of the available sites.


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