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 Elhosiny; Haitham El-Ghareeb; Bahaa T. Shabana; Ahmed AbouElfetouh

doi:10.5391/IJFIS.2021.21.1.12

Original Article

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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

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.

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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

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No potential conflict of interest relevant to this article was reported.

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Amany Mohamed Elhoainy is a PhD candidate at Faculty of Computers and Information Sciences, Mansoura University, Egypt. She is a lecturer assistant at Information System Department, Sadat Academy for Management Sciences, Tanta, Egypt. Her research work focuses on geographical information systems, remote sensing, intelligent information system, and neutrosophic set theory.

E-mail: amanyelhosiny2020@gmail.com

Haitham El-Ghareeb is an associate professor of Information Systems at Faculty of Computers and Information Sciences, Mansoura University, Egypt. He is a member of many distinguished computer organizations, reviewer for different highly recognized academic journals, contributor to open-source projects, and the author of different international published books. Haitham is interested in ERP systems, intelligent information systems, and systems integration.

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Bahaa T. Shabana is an associate professor, was born in Mansoura, Egypt, 1963; received the B.Sc. degree in Mathematics from Faculty of Sciences, Mansoura University in 1986. He received higher diplomas in Education, 1988; Pure Mathematics and Scientific Computations, 1999; and Statistics and Computer Sciences, 2001; from Mansoura University, Egypt. He also received a higher diploma of Computer Science and Information Systems, 2003; M.Sc. of Computer and Information System, 2008; from Institute of Statistical Studies and Researches (ISSR). He received Ph.D. of Statistics and Computer Science, 2012, from Faculty of Science, Mansoura University, Egypt. He is interested in geographic information systems (GIS), GIS applications digital photogrammetry, and road networking.

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Ahmed AbouElfetouh he is professor at Information system Department, Mansoura University, Egypt. He received Prof Degree in Information system Department from Faculty of Computers and Information Mansoura University, Egypt, in 2013 He is Vice Dean for Graduate Studies and Research in Egypt from 2013.

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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

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

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

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

Abstract

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

Abstract

Fig 1.

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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

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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 term	Neutrosophic set	Linguistic term	Reciprocal 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 Name	Sub-criteria name	References
Natural Factors (C1)	C11 - Wind direction	The 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 areas	All 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).

C31	C15	C14	C13	Weights
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.

	C11	C12	C13	C14	C15	C21	C22	C23	C31	C32	C33
C11	0.0831	0.0831	0.0831	0.0831	0.0831	0.0831	0.0831	0.0831	0.0831	0.0831	0.0831
C12	0.0866	0.0866	0.0866	0.0866	0.0866	0.0866	0.0866	0.0866	0.0866	0.0866	0.0866
C13	0.0970	0.0970	0.0970	0.0970	0.0970	0.0970	0.0970	0.0970	0.0970	0.0970	0.0970
C14	0.0969	0.0969	0.0969	0.0969	0.0969	0.0969	0.0969	0.0969	0.0969	0.0969	0.0969
C15	0.0930	0.0930	0.0930	0.0930	0.0930	0.0930	0.0930	0.0930	0.0930	0.0930	0.0930
C21	0.0921	0.0921	0.0921	0.0921	0.0921	0.0921	0.0921	0.0921	0.0921	0.0921	0.0921
C22	0.0968	0.0968	0.0968	0.0968	0.0968	0.0968	0.0968	0.0968	0.0968	0.0968	0.0968
C23	0.0963	0.0963	0.0963	0.0963	0.0963	0.0963	0.0963	0.0963	0.0963	0.0963	0.0963
C31	0.0884	0.0884	0.0884	0.0884	0.0884	0.0884	0.0884	0.0884	0.0884	0.0884	0.0884
C32	0.0850	0.0850	0.0850	0.0850	0.0850	0.0850	0.0850	0.0850	0.0850	0.0850	0.0850
C33	0.0850	0.0850	0.0850	0.0850	0.0850	0.0850	0.0850	0.0850	0.0850	0.0850	0.0850

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

	C₁₁	C₁₂	C₁₃	C₁₄	C₁₅	C₂₁	C₂₂	C₂₃	C₃₁	C₃₂	C₃₃	C₃₄	C₃₅
P(A,B)	−0.9	−0.8	0	0.18	1	−0.25	−1	0	0.8	0	−0.89	−0.5	0.71
P(A,C)	−1	0.2	1	0.47	0.71	−1	−0.33	−1	1	−1	0.11	−0.25	0.71
P(A,D)	0	0	0.5	−0.53	0.64	−0.5	−0.17	0	1	−1	−0.56	0.5	1
P(B,A)	0.9	0.8	0	−0.18	−1	0.25	1	0	−0.8	0	0.89	0.5	−0.71
P(B,C)	−0.1	1	1	0.29	−0.29	−0.75	0.67	−1	0.2	−1	1	0.25	0
P(B,D)	0.9	0.8	0.5	−0.71	−0.36	−0.25	0.83	0	0.2	−1	0.33	1	0.29
P(C,A)	1	−0.2	−1	−0.47	−0.71	1	0.33	1	−1	1	−0.11	0.25	−0.71
P(C,B)	0.1	−1	−1	−0.29	0.29	0.75	−0.67	1	−0.2	1	−1	−0.25	0
P(C,D)	1	−0.2	−0.5	−1	−0.07	0.5	0.17	1	0	0	−0.67	0.75	0.29
P(D,A)	0	0	−0.5	0.53	−0.64	0.5	0.17	0	−1	1	0.56	−0.5	−1
P(D,B)	−0.9	−0.8	−0.5	0.71	0.36	0.25	−0.83	0	−0.2	1	−0.33	−1	−0.29
P(D,C)	−1	0.2	0.5	1	0.07	−0.5	−0.17	−1	0	0	0.67	−0.75	−0.29

Table 6 . Preference function.

	C₁₁	C₁₂	C₁₃	C₁₄	C₁₅	C₂₁	C₂₂	C₂₃	C₃₁	C₃₂	C₃₃	C₃₄	C₃₅
P(A,B)	0.00	0.00	0.00	0.18	1.00	0.00	0.00	0.00	0.80	0.00	0.00	0.00	0.71
P(A,C)	0.00	0.20	1.00	0.47	0.71	0.00	0.00	0.00	1.00	0.00	0.11	0.00	0.71
P(A,D)	0.00	0.00	0.50	0.00	0.64	0.00	0.00	0.00	1.00	0.00	0.00	0.50	1.00
P(B,A)	0.90	0.80	0.00	0.00	0.00	0.25	1.00	0.00	0.00	0.00	0.89	0.50	0.00
P(B,C)	0.00	1.00	1.00	0.29	0.00	0.00	0.67	0.00	0.20	0.00	1.00	0.25	0.00
P(B,D)	0.90	0.80	0.50	0.00	0.00	0.00	0.83	0.00	0.20	0.00	0.33	1.00	0.29
P(C,A)	1.00	0.00	0.00	0.00	0.00	1.00	0.33	1.00	0.00	1.00	0.00	0.25	0.00
P(C,B)	0.10	0.00	0.00	0.00	0.29	0.75	0.00	1.00	0.00	1.00	0.00	0.00	0.00
P(C,D)	1.00	0.00	0.00	0.00	0.00	0.50	0.17	1.00	0.00	0.00	0.00	0.75	0.29
P(D,A)	0.00	0.00	0.00	0.53	0.00	0.50	0.17	0.00	0.00	1.00	0.56	0.00	0.00
P(D,B)	0.00	0.00	0.00	0.71	0.36	0.25	0.00	0.00	0.00	1.00	0.00	0.00	0.00
P(D,C)	0.00	0.20	0.50	1.00	0.07	0.00	0.00	0.00	0.00	0.00	0.67	0.00	0.00

Table 7 . Preference index value.

	C₁₁	C₁₂	C₁₃	C₁₄	C₁₅	C₂₁	C₂₂	C₂₃	C₃₁	C₃₂	C₃₃	C₃₄	C₃₅	Aggregated preference index
w_C	1.82	0.47	0.88	1.35	0.54	0.70	0.24	0.68	0.66	0.51	0.35	0.23	1.57	-
P (A,B)	0.00	0.00	0.00	0.24	0.54	0.00	0.00	0.00	0.53	0.00	0.00	0.00	1.12	2.43
P (A,C)	0.00	0.09	0.88	0.63	0.38	0.00	0.00	0.00	0.66	0.00	0.04	0.00	1.12	3.81
P (A,D)	0.00	0.00	0.44	0.00	0.34	0.00	0.00	0.00	0.66	0.00	0.00	0.12	1.57	3.14
P (B,A)	1.64	0.37	0.00	0.00	0.00	0.17	0.24	0.00	0.00	0.00	0.31	0.12	0.00	2.86
P (B,C)	0.00	0.47	0.88	0.40	0.00	0.00	0.16	0.00	0.13	0.00	0.35	0.06	0.00	2.45
P (B,D)	1.64	0.37	0.44	0.00	0.00	0.00	0.20	0.00	0.13	0.00	0.12	0.23	0.45	3.59
P (C,A)	1.82	0.00	0.00	0.00	0.00	0.70	0.08	0.68	0.00	0.51	0.00	0.06	0.00	3.85
P (C,B)	0.18	0.00	0.00	0.00	0.15	0.52	0.00	0.68	0.00	0.51	0.00	0.00	0.00	2.05
P (C,D)	1.82	0.00	0.00	0.00	0.00	0.35	0.04	0.68	0.00	0.00	0.00	0.18	0.45	3.52
P (D,A)	0.00	0.00	0.00	0.71	0.00	0.35	0.04	0.00	0.00	0.51	0.20	0.00	0.00	1.81
P (D,B)	0.00	0.00	0.00	0.95	0.19	0.17	0.00	0.00	0.00	0.51	0.00	0.00	0.00	1.82
P (D,C)	0.00	0.09	0.44	1.35	0.04	0.00	0.00	0.00	0.00	0.00	0.24	0.00	0.00	2.15

Table 8 . PROMETHEE II flow.

	A	B	C	D	φ⁺ Leaving flow
A	-	1.86	4.26	2.35	2.83
B	2.86	-	3.46	3.36	3.23
C	3.85	2.05	-	3.07	2.99
D	1.81	1.82	2.94	-	2.19
φ⁻ Entering flow	2.84	1.91	3.55	2.93	-

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

	A	B	C	D	Si+	Si-
A	-	1.86	4.26	2.35	3.39	2.98
B	2.86	-	3.46	3.36	3.47	1.96
C	3.85	2.05	-	3.07	3.48	3.70
D	1.81	1.82	2.94	-	2.79	3.17
Vj+ positive ideal	2.83	3.23	2.99	2.19	-	-
Vj- negative ideal	2.84	1.91	3.55	2.93	-	-

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

Alternatives	Si+	Si-	P_i	Alternatives ranking
A	3.39	2.98	0.47	3
B	3.47	1.96	0.36	4
C	3.48	3.70	0.51	2
D	2.79	3.17	0.53	1

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Original Article

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

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Conflict of Interest

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Article

Original Article

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

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Evaluating and Selecting a Supplier in a Healthcare Supply Chain Using the Technique for Order Performance by Similarity to Ideal Solution Under Intuitionistic Fuzzy Environment

International Journalof Fuzzy Logic andIntelligent Systems

Original Article

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

Abstract

Conflict of Interest

Biographies

Article

Original Article

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

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Supply Chain Risk Analysis through the Computational Method

Evaluating and Selecting a Supplier in a Healthcare Supply Chain Using the Technique for Order Performance by Similarity to Ideal Solution Under Intuitionistic Fuzzy Environment

International Journal
of Fuzzy Logic and
Intelligent Systems