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International Journal of Fuzzy Logic and Intelligent Systems 2021; 21(1): 12-28

Published online March 25, 2021

https://doi.org/10.5391/IJFIS.2021.21.1.12

© The Korean Institute of Intelligent Systems

A Hybrid Neutrosophic GIS-MCDM Method Using a Weighted Combination Approach for Selecting Wind Energy Power Plant Locations: A Case Study of Sinai Peninsula, Egypt

Amany Mohamed Elhosiny1, Haitham El-Ghareeb2, Bahaa T. Shabana3, and Ahmed AbouElfetouh2

1Information system Department, Sadat Academy for Management Sciences, Tanta, Egypt
2Information system Department, Faculty of Computer & Information Sciences, Mansoura, Egypt
3Computer science Department, Misr Higher Institute of Commerce and Computers (MET), Mansoura, Egypt

Correspondence to :
Amany Mohamed Elhosin (amanyelhosiny2020@gmail.com)

Received: October 30, 2020; Revised: December 29, 2020; Accepted: January 12, 2021

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The production and use of wind energy has eased the problems of energy scarcity and environmental pollution. However, the selection of locations for wind power plants is challenging because the associated decision-making process requires political, socio-economic, and environmental considerations. The selection of suboptimal sites has created several negative impacts. This study aims to resolve this issue by implementing the following factors: integrating a qualitative and quantitative multi-criteria decision-making framework for selecting locations for wind power plants; applying the new framework in Sinai Peninsula in Egypt, and investigating the neutrosophic analytic network process for weight assignment through expert-based and entropy-based criteria; choosing four potential alternative wind power plant sites, and using PROMETHEE-TOPSIS to help decision makers find the best possible alternative; and establishing the supremacy of one option over the other. The results indicate that by applying the proposed approach, an appropriate wind power plant location can be successfully selected among various alternatives.

Keywords: Single value neutrosophic numbers, Analytic network process, PROMETHEE, TOPSIS, Wind farms site selection, Multi criteria decision-making

Article

Original Article

International Journal of Fuzzy Logic and Intelligent Systems 2021; 21(1): 12-28

Published online March 25, 2021 https://doi.org/10.5391/IJFIS.2021.21.1.12

Copyright © The Korean Institute of Intelligent Systems.

A Hybrid Neutrosophic GIS-MCDM Method Using a Weighted Combination Approach for Selecting Wind Energy Power Plant Locations: A Case Study of Sinai Peninsula, Egypt

Amany Mohamed Elhosiny1, Haitham El-Ghareeb2, Bahaa T. Shabana3, and Ahmed AbouElfetouh2

1Information system Department, Sadat Academy for Management Sciences, Tanta, Egypt
2Information system Department, Faculty of Computer & Information Sciences, Mansoura, Egypt
3Computer science Department, Misr Higher Institute of Commerce and Computers (MET), Mansoura, Egypt

Correspondence to:Amany Mohamed Elhosin (amanyelhosiny2020@gmail.com)

Received: October 30, 2020; Revised: December 29, 2020; Accepted: January 12, 2021

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The production and use of wind energy has eased the problems of energy scarcity and environmental pollution. However, the selection of locations for wind power plants is challenging because the associated decision-making process requires political, socio-economic, and environmental considerations. The selection of suboptimal sites has created several negative impacts. This study aims to resolve this issue by implementing the following factors: integrating a qualitative and quantitative multi-criteria decision-making framework for selecting locations for wind power plants; applying the new framework in Sinai Peninsula in Egypt, and investigating the neutrosophic analytic network process for weight assignment through expert-based and entropy-based criteria; choosing four potential alternative wind power plant sites, and using PROMETHEE-TOPSIS to help decision makers find the best possible alternative; and establishing the supremacy of one option over the other. The results indicate that by applying the proposed approach, an appropriate wind power plant location can be successfully selected among various alternatives.

Keywords: Single value neutrosophic numbers, Analytic network process, PROMETHEE, TOPSIS, Wind farms site selection, Multi criteria decision-making

Fig 1.

Figure 1.

A GIS-based neutrosophic ANP method.

The International Journal of Fuzzy Logic and Intelligent Systems 2021; 21: 12-28https://doi.org/10.5391/IJFIS.2021.21.1.12

Fig 2.

Figure 2.

Highly suitable sites for wind farm construction: (a) extracted suitable sites and (b) filtered suitable sites.

The International Journal of Fuzzy Logic and Intelligent Systems 2021; 21: 12-28https://doi.org/10.5391/IJFIS.2021.21.1.12

Table 1 . The single value neutrosophic scale for the comparison matrix [36].

Linguistic termNeutrosophic setLinguistic termReciprocal neutrosophic set
Extremely highly preferred(0.90, 0.10, 0.10)Mildly lowly preferred(0.10, 0.90, 0.90)
Extremely preferred(0.85,0.20, 0.15)Mildly preferred(0.15,0.80, 0.85)
Very strongly to extremely preferred(0.80, 0.25, 0.20)Mildly preferred to very lowly preferred(0.20, 0.75, 0.80)
Very strongly preferred(0.75,0.25, 0.25)Very lowly preferred(0.25,0.75, 0.75)
Strongly preferred(0.70, 0.30, 0.30)Lowly preferred(0.30, 0.70, 0.70)
Moderately highly to strongly preferred(0.65, 0.30, 0.35)Moderately lowly preferred to lowly preferred(0.35, 0.70, 0.65)
Moderately highly preferred(0.60, 0.35, 0.40)Moderately lowly preferred(0.40, 0.65, 0.60)
Equally to moderately preferred(0.55, 0.40, 0.45)Moderately to equally preferred(0.45, 0.60, 0.55)
Equally preferred(0.50, 0.50, 0.50)Equally preferred(0.50, 0.50, 0.50)

Table 2 . Determination and explanation of the main criteria and factors.

Cluster NameSub-criteria nameReferences
Natural Factors (C1)C11 - Wind directionThe location of wind turbines is determined by the prevailing wind direction in order to be effective.
C12 - Aspect[48]
C13 - Elevation[21], [49], [50]
C14 - Slope[51], [52]
C15 - Wind speed[28], [52]

Socio-Economic Factors (C2)C21 - Dist. from power lines[48]
C22 - Dist. from cities/villages[48]
C23 - Dist. from main roads[48]

Environmental Factors (C3)C31 - Land Cover/Land Use[48]
C32 - Dist. from protected areas[53], [54]
C33 - Dist. from risks areasAll the mechanical parts of wind power turbines should be kept away from the water To prevent damage to the turbine components, wind turbine fans are lowered and disconnected.

Table 3 . Pairwise comparison matrix for natural criteria for land-use and priority vector (CR=0.009).

C31C15C14C13Weights
C15(0.50,0.50,0.50)(0.55,0.40,0.45)(0.60,0.35,0.40)(0.629,0.331,0.371)
C14(0.45,0.60,0.55)(0.50,0.50,0.50)(0.55,0.40,0.45)(0.572,0.4256,0.428)
C13(0.40,0.65,0.60)(0.45,0.60,0.55)(0.50,0.50,0.50)(0.515,0.522,0.485)

Table 4 . The limitation super-matrix.

C11C12C13C14C15C21C22C23C31C32C33
C110.08310.08310.08310.08310.08310.08310.08310.08310.08310.08310.0831
C120.08660.08660.08660.08660.08660.08660.08660.08660.08660.08660.0866
C130.09700.09700.09700.09700.09700.09700.09700.09700.09700.09700.0970
C140.09690.09690.09690.09690.09690.09690.09690.09690.09690.09690.0969
C150.09300.09300.09300.09300.09300.09300.09300.09300.09300.09300.0930
C210.09210.09210.09210.09210.09210.09210.09210.09210.09210.09210.0921
C220.09680.09680.09680.09680.09680.09680.09680.09680.09680.09680.0968
C230.09630.09630.09630.09630.09630.09630.09630.09630.09630.09630.0963
C310.08840.08840.08840.08840.08840.08840.08840.08840.08840.08840.0884
C320.08500.08500.08500.08500.08500.08500.08500.08500.08500.08500.0850
C330.08500.08500.08500.08500.08500.08500.08500.08500.08500.08500.0850

Table 5 . Deviations between any two potential alternatives with respect to criteria Cj.

C11C12C13C14C15C21C22C23C31C32C33C34C35
P(A,B)−0.9−0.800.181−0.25−100.80−0.89−0.50.71
P(A,C)−10.210.470.71−1−0.33−11−10.11−0.250.71
P(A,D)000.5−0.530.64−0.5−0.1701−1−0.560.51
P(B,A)0.90.80−0.18−10.2510−0.800.890.5−0.71
P(B,C)−0.1110.29−0.29−0.750.67−10.2−110.250
P(B,D)0.90.80.5−0.71−0.36−0.250.8300.2−10.3310.29
P(C,A)1−0.2−1−0.47−0.7110.331−11−0.110.25−0.71
P(C,B)0.1−1−1−0.290.290.75−0.671−0.21−1−0.250
P(C,D)1−0.2−0.5−1−0.070.50.17100−0.670.750.29
P(D,A)00−0.50.53−0.640.50.170−110.56−0.5−1
P(D,B)−0.9−0.8−0.50.710.360.25−0.830−0.21−0.33−1−0.29
P(D,C)−10.20.510.07−0.5−0.17−1000.67−0.75−0.29

Table 6 . Preference function.

C11C12C13C14C15C21C22C23C31C32C33C34C35
P(A,B)0.000.000.000.181.000.000.000.000.800.000.000.000.71
P(A,C)0.000.201.000.470.710.000.000.001.000.000.110.000.71
P(A,D)0.000.000.500.000.640.000.000.001.000.000.000.501.00
P(B,A)0.900.800.000.000.000.251.000.000.000.000.890.500.00
P(B,C)0.001.001.000.290.000.000.670.000.200.001.000.250.00
P(B,D)0.900.800.500.000.000.000.830.000.200.000.331.000.29
P(C,A)1.000.000.000.000.001.000.331.000.001.000.000.250.00
P(C,B)0.100.000.000.000.290.750.001.000.001.000.000.000.00
P(C,D)1.000.000.000.000.000.500.171.000.000.000.000.750.29
P(D,A)0.000.000.000.530.000.500.170.000.001.000.560.000.00
P(D,B)0.000.000.000.710.360.250.000.000.001.000.000.000.00
P(D,C)0.000.200.501.000.070.000.000.000.000.000.670.000.00

Table 7 . Preference index value.

C11C12C13C14C15C21C22C23C31C32C33C34C35Aggregated preference index
wC1.820.470.881.350.540.700.240.680.660.510.350.231.57-
P (A,B)0.000.000.000.240.540.000.000.000.530.000.000.001.122.43
P (A,C)0.000.090.880.630.380.000.000.000.660.000.040.001.123.81
P (A,D)0.000.000.440.000.340.000.000.000.660.000.000.121.573.14
P (B,A)1.640.370.000.000.000.170.240.000.000.000.310.120.002.86
P (B,C)0.000.470.880.400.000.000.160.000.130.000.350.060.002.45
P (B,D)1.640.370.440.000.000.000.200.000.130.000.120.230.453.59
P (C,A)1.820.000.000.000.000.700.080.680.000.510.000.060.003.85
P (C,B)0.180.000.000.000.150.520.000.680.000.510.000.000.002.05
P (C,D)1.820.000.000.000.000.350.040.680.000.000.000.180.453.52
P (D,A)0.000.000.000.710.000.350.040.000.000.510.200.000.001.81
P (D,B)0.000.000.000.950.190.170.000.000.000.510.000.000.001.82
P (D,C)0.000.090.441.350.040.000.000.000.000.000.240.000.002.15

Table 8 . PROMETHEE II flow.

ABCDφ+ Leaving flow
A-1.864.262.352.83
B2.86-3.463.363.23
C3.852.05-3.072.99
D1.811.822.94-2.19
φ Entering flow2.841.913.552.93-

Table 9 . Positive and negative ideal solutions, Si+,Si-.

ABCDSi+Si-
A-1.864.262.353.392.98
B2.86-3.463.363.471.96
C3.852.05-3.073.483.70
D1.811.822.94-2.793.17
Vj+ positive ideal2.833.232.992.19--
Vj- negative ideal2.841.913.552.93--

Table 10 . Rank of the alternatives based on the closeness coefficient.

AlternativesSi+Si-PiAlternatives ranking
A3.392.980.473
B3.471.960.364
C3.483.700.512
D2.793.170.531

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