*7.1.3.1. Land use*

Land use is important for resolving public conflicts over the acceptance of unwanted facility siting [4]. **Table 4** shows the membership values assigned to all categories used in the analysis based on results of investigations with experts (agronomist, environmentalists…).


**Table 3.** Buffer zones for the generation of constraint map.

*7.1.3.2. Olfactory and sonorous impacts*

*7.1.4. Socio-economic factors*

d = 1000 hab/km2

*7.1.5. Proximity to dense population*

).

the best fitted distance for siting a landfill (**Figure 3**).

tion, distance from road and proximity to building materials.

**Factors Sub-factors Standardization of factors**

Olfactory and sonorous

Proximity to buildings

impacts

population

materials

Socio-economic Proximity to dense

 Urban areas 0 Protected area 0 Wetlands 0 Water 0 Vine 2 Mariachi culture 2 Cereals 3 Olive trees 3 Forager culture 4 Course 6 Naked soil 10

Environmental Land use (no units)

Concerning the olfactory and sonorous impacts factors, a simple linear distance decay function is appropriate for these criteria, in which a cost distance from the main roads increases, its suitability increases. To rescale the cost distance factor, a monotonically increasing linear fuzzy membership function was used. The first control point (a = 200 m) indicates the least suitable distance for siting a landfill while the second control point (b = 3000 m) and indicates

**Table 4.** Fuzzy set memberships and membership functions with control points used for MSW landfill site selection.

Proximity to roads 200 and 3000 m J-shaped, decreasing

**Control point Fuzzy function/membership**

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79

An Integrated Multicriteria and Fuzzy Logic Approach for Municipal Solid Waste Landfill Siting

200 and 3000 m Linear, increasing

200 and 1000 inhabitants/km2 Sigmoidal, decreasing

5000 and 15,000 m J-shaped, decreasing

The socio-economic factors comprises three sub-factors namely proximity to dense popula-

Proximity to the waste generation centers generate most of the waste quantity is a very significant factor because it defines the working costs for the landfill. The closer to the dense population settlements, the lower the operation cost will be. The population density map was standardized by sigmoid decreasing fuzzy function controlled by two points (c = 200 hab/ km2

,

**Figure 2.** Boolean images of constraints maps a) Soil permeability, b) Elevation, c) Slope, d) Distance from coastal Zone, g) Depth to ground water Table, h) Distance from water supply, i) Distance from wetlands, j) Distance from rivers, k) Distance from irrigation canals, l) Land use, m) Distance from roads, n) Distance from protected areas, o) Distance from residential areas.

An Integrated Multicriteria and Fuzzy Logic Approach for Municipal Solid Waste Landfill Siting http://dx.doi.org/10.5772/intechopen.75161 79


**Table 4.** Fuzzy set memberships and membership functions with control points used for MSW landfill site selection.

#### *7.1.3.2. Olfactory and sonorous impacts*

Concerning the olfactory and sonorous impacts factors, a simple linear distance decay function is appropriate for these criteria, in which a cost distance from the main roads increases, its suitability increases. To rescale the cost distance factor, a monotonically increasing linear fuzzy membership function was used. The first control point (a = 200 m) indicates the least suitable distance for siting a landfill while the second control point (b = 3000 m) and indicates the best fitted distance for siting a landfill (**Figure 3**).

#### *7.1.4. Socio-economic factors*

**Figure 2.** Boolean images of constraints maps a) Soil permeability, b) Elevation, c) Slope, d) Distance from coastal Zone, g) Depth to ground water Table, h) Distance from water supply, i) Distance from wetlands, j) Distance from rivers, k) Distance from irrigation canals, l) Land use, m) Distance from roads, n) Distance from protected areas, o) Distance from

**Constraints Buffering**

78 Fuzzy Logic Based in Optimization Methods and Control Systems and Its Applications

Distance from water supply (reservoirs, wells, boreholes,

**Table 3.** Buffer zones for the generation of constraint map.

springs)

Elevation Exclude areas over 200 m Slope Exclude areas over 5% Distance from coastal zone 3 km buffer zone

Depth to ground water table Exclude depth less than 14 m

Distance from wetlands 1 km buffer zone Distance from rivers 200 m buffer zone Distance from irrigation canals 200 m buffer zone

Distance from protected areas 300 m buffer zone Distance from residential areas 2 km buffer zone Proximity to roads 200 m buffer zone

Soil permeability Exclude soils having high rate of permeability

Land use Exclude arable lands and area with high economic

3 km buffer zone

advantages

residential areas.

The socio-economic factors comprises three sub-factors namely proximity to dense population, distance from road and proximity to building materials.

#### *7.1.5. Proximity to dense population*

Proximity to the waste generation centers generate most of the waste quantity is a very significant factor because it defines the working costs for the landfill. The closer to the dense population settlements, the lower the operation cost will be. The population density map was standardized by sigmoid decreasing fuzzy function controlled by two points (c = 200 hab/ km2 , d = 1000 hab/km2 ).

*7.1.7. Proximity to building materials*

and characteristics of the region (**Tables 5** and **6**).

**Land use**

**Proximity to buildings materials**

λ max=3.038, CI = 0.019, RI = 0.58, CR = 0.03 < <0.1 (consistency is acceptable).

**Table 6.** Pair-wise comparison matrix for assessing the weights of socio-economic factors.

λ max=2, CI = 0.00, CR = 0.00 (consistency is acceptable).

**Olfactory and sonorous impacts**

**Table 5.** Pair-wise comparison matrix for assessing the weights of environmental factors.

Land use 1 5 2.23 2.23/(2.23 + 0.45) = 0.83

construction of a trustworthy matrix.

(c = 5000 m, d = 15,000).

*7.1.8. Scenario creation*

Olfactory and sonorous

Proximity to buildings

Proximity to dense population

impacts

materials

Proximity to building materials is used for comparing the costs for building materials during landfill construction (impermeable soil for the bottom liner) and landfill operation (daily and final cover material). For sanitary landfills, such materials also minimize the propagation of various vectors (i.e., insects, rodents, birds and air contaminants) that may affect public health and well-being [4]. Again, a j-shaped decreasing function was used with two control points

An Integrated Multicriteria and Fuzzy Logic Approach for Municipal Solid Waste Landfill Siting

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81

The next step was implementation of the AHP to calculate the relative weights of the criteria. This step involved construction of comparison matrix where weights are determined through the pairwise comparison method. Pairwise comparison method was used only to assign weights and establish importance of environmental criteria using experience of experts

The final stage is to calculate a CR to measure how consistent the judgments have been relative to large samples of purely random judgments. For the case study the CR was 0, indicating

WLC is displayed to compute the possible landfill areas for both of the environmental and socio-economic set of criteria, using the assigning weight to each of the criteria. Intermediate fitness maps were created for the environmental and socio-economic group of criteria, respectively. Final aggregation of the two intermediate suitability maps was implemented for three scenarios to demonstrate the importance of the weights associated with the environmental and

> **Proximity to dense population**

Proximity to roads 3 1/3 1 1 0.26

**Eigenvector Weight**

**Proximity to roads**

**Eigenvector Weight**

1/5 1 0.45 0.45/(2.23 + 0.45) = 0.17

1 1/5 1/3 0.44 0.11

5 1 3 2.44 0.63

**Figure 3.** Examples of spatial evaluation maps of factors a) Land use, b) Olfactory and sonorous impacts, c) proximity to buildings materials, d) Distance from roads.

#### *7.1.6. Proximity to roads*

Proximity to roads considers the construction costs for building new road infrastructure between the settlements and potential landfill. A j-shaped decreasing fuzzy membership function was used for standardization controlled by two points (c = 200 m, d = 3000).
