تهیه نقشه پتانسیل نیروگاه های خورشیدی مبتنی برمفهوم ریسک مطالعه موردی: استان خراسان رضوی

نوع مقاله: مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری سنجش از دور و GIS، دانشکده جغرافیا، دانشگاه تهران

2 استادیار گروه سنجش از دور و GIS، دانشکده جغرافیا، دانشگاه تهران

3 دانشجوی دکتری جغرافیا و برنامه ریزی شهری دانشگاه تهران

10.22131/sepehr.2019.37512

چکیده

انرژی خورشیدی از پاکترین، قابل دسترس‌‌ترین و ارزان‌‌ترین انرژی‌‌های جهان است که استفاده از آن اثرات منفی کمتری بر محیط زیست می‌‌گذارد. تعیین مکان مناسب برای احداث و استفاده از تکنولوژی‌‌های خورشیدی از اهمیت بالایی برخوردار است. بنابراین هدف از این تحقیق، انتخاب مناطق بهینه احداث نیروگاه‌‌های خورشیدی با لحاظ کردن مفهوم ریسک در تصمیم‌‌گیری با استفاده از مدل OWA برای استان خراسان رضوی می‌‌باشد. مدل OWA قادر است تا میزان ریسک‌‌پذیری و ریسکگریزی گزینه‌‌های تصمیم‌‌گیران را در انتخاب گزینه نهایی لحاظ کند. در پژوهش حاضر، برای وزندهی به معیارها از مدل وزندهی AHP، جهت استخراج مکانهای مناسب با درجات ریسک مختلف از مدلOWA و برای آنالیز حساسیت وزن معیارها از روش OAT استفاده شده است. نقشههای حاصل از مدل OWA در پنج کلاس خیلی نامناسب، نامناسب، متوسط، مناسب و خیلی مناسب طبقهبندی گردیدند به طوری که در ORness=0 و ORness=1 مساحت طبقه خیلی مناسب (1-8/0) برای استان خراسان رضوی به ترتیب برابر با 6 و 82 درصد از مساحت کل منطقه می‌‌باشد. در استان خراسان رضوی، شهرستان‌‌های فردوس، گنابادو بردسکن دارای بیشترین مساحت از طبقه خیلی مناسب برای احداث نیروگاههای خورشیدی می‌‌باشند. نتابج تجزیه و تحلیل حساسیت معیارها نشان داد که تغییر وزن معیارهای شیب و گسل به ترتیب دارای بیشترین و کمترین تأثیر بر مساحت طبقه خیلی مناسب جهت احداث نیروگاه‌‌های خورشیدی هستند.

کلیدواژه‌ها


عنوان مقاله [English]

Mapping the potential of solar power plants based on the concept of risk Case study: Razavi Khorasan Province

نویسندگان [English]

  • Saman Nadizadeh Shorabeh 1
  • Najmeh Neisany Samany 2
  • Yaghob Abdali 3
1 Ph.D Student of remote sensing & GIS, Faculty geography, University of Tehran
2 Assistant Professor of remote sensing & GIS, University of Tehran
3 Ph.D. Student of Geography and Urban Planning, University of Tehran
چکیده [English]

Extended Abstract
Introduction
There is a huge potential in the usage of renewable energy sources because these natural resources are inexpensive and harmless to the environment. Solar, wind, and geothermal energies are among the renewable energies. Solar photovoltaic (PV) technology is one of the fastest growing renewable energy technologies across the world. Solar energy is a practical and suitable technology, especially in arid areas with high solar energy potential. The first step in using renewable energy in Iran was in 1994, and since then, much attention has been paid to this type of energy in the society and the government. In Iran, 850 million tons of greenhouse gases are produced annually. Consequently, renewable energy sources such as solar energy can have a significant impact on reducing the greenhouse gas emissions.
The integration of GIS and MCDA helps the decision maker to perform decision analysis functions such as ranking the options to select a suitable location so that the GIS is used as a powerful and integrated tool for storing, manipulating and analyzing the solar energy criteria. The use of the MCDA method can facilitate the evaluation and selection of the most appropriate location (s), taking into account the key criteria in the decision-making process.
In this study, the optimal areas for the construction of the solar power plants have been identified in five highly optimistic, optimistic, moderate, pessimistic, and highly pessimistic levels using the spatial criteria and the OWA model. One of the most prominent features of this research in relation to the other articles is the inclusion of the concept of risk into the solar power plant site selection process to determine the optimum areas for the construction of solar power plants using the OWA model.
 
Materials and methods
The primary data used in this study include the Digital Elevation Model (DEM) derived from the Aster satellite data for the extraction of solar radiation and the region slope, the extraction of the mean land surface temperature for 2017 using the Terra Sensor MOD11A1, the preparation of the average map of the vegetation for 2017 using MODRA13A2 Terra sensor, the 1.250000 fault map prepared by the geological organization, the statistics and data of the rainfall prepared by the Meteorological Organization of Chahar mahal-o-Bakhtiari province, shapefile of road network prepared by the Organization of Roads and Urban Development,  the climaticshapefile of
 the country prepared by the Iran Meteorological Organization, the shapefile of urban areas generated by the National Cartographic Center (NCC).The proposed methodology works by employing AHP to obtain the appropriate weights for each criterion, and utilizing OWA to extract suitable locations to varying degrees of risk. Sensitivity analysis for the criteria weights were conducted by virtue of the OAT method.
 
Results and discussion
The northern sectors of Razavi Khorasan province are endowed with cold temperatures and cold mountainous climate, which has had a substantial contribution to the increased cloudy and rainy days as well as the relatively extensive vegetation cover in this area. In this light, with respect to all ‘ORness’s, the target areas fall within the ‘very unsuitable’ and ‘unsuitable’ classes for construction of solar power plants. Moreover, the high slope factor in these areas has contributed to high levels of surface radiation, albeit, as the slope criterion is considered a constraint, the target areas are, in fact, not suitable for the construction of solar power plants. Moving southwards, the suitability of the regions, in terms of construction of solar power plants, tends to shift in the positive direction (very suitable class), which is most likely the result of the low rainfall and vegetation cover in conjunction with high surface temperatures in these areas, as opposed to their counterparts in the north. Areas falling within the very suitable class for construction of solar power plants in Razavi Khorasan can be realized by dint of calculating the percentage of area attributed to each class at ORness = 0.5 per city. The findings show that cities located towards the south and southwest of the province contribute to the highest area in the suitable class, while counties in the northern regions have the lowest share of area in the very suitable class.
The highest sensitivity in locating suitable areas in Razavi Khorasan province were observed among the factors of slope, road, and urban criteria. Alterations in the weights assigned to these criteria would entail a significantly strong impact on the extent of the very suitable class. This highlights the significance of accurately determining the weights for these three criteria in Razavi Khorasan Province. Based on the findings, the rate of change in weight assigned to the of fault criteria ranges from 0 to 0.2, which in turn causes substantial change in the area of regions in the very suitable class extent. However, setting the criteria weight at between 0.2 and 1 appears to have no significant effects in the area of this class.
 
Conclusion
The results of this research indicate that the northern parts of Razavi Khorasan province are highly unsuitable and unsuitable for all of ‘ORness’ values, while a significant extent of ​​the highly suitable class for the construction of solar power plants is comprised of sectors of the southern regions. Areas within the very suitable class corresponding to an ORness=1 comprise 5% of the class, whereas those with an ORness=0 have a 74% share. The three cities of Ferdows, Bardaskan, and Gonabad, had the highest share of the area attributed to the very suitable class (0.8-1), as maintained by a per city analysis of the area for each class. However, the cities of Dergas, Quchan, Mashhad, and Kalat had no share of the areas within ​​the very suitable class. This most probably stems from the high geographic latitudes of said regions, which has engendered unsuitable climatic conditions in these areas. Finally, results from sensitivity analysis of the criteria showed that increases in the weights assigned to the factors of slope, road, and urban criteria, would cause a further increase in the area of the very suitable class. Stated differently, the selection of suitable locations for the establishment of solar power plants is highly sensitive to these criteria. Changes in the weight of the surface temperature criterion had no considerable effect on the area of the very suitable class. Moreover, shifts in the weight allotted to solar radiation and precipitation in the province, ranging from 0 and 0.6, brought about substantial changes in the area of ​​the very suitable class. Whereas, shifts within the 0.6–1 range had no significant effects on the area of the very suitable class.

کلیدواژه‌ها [English]

  • Solar Power Plants
  • GIS-MCDA
  • Risk
  • OWA
  • Razavi Khorasan
1. احمدی، ه.،مرشدی، ج.، عظیمی، ف. 1395.مکانیابی نیروگاه‌های خورشیدی با استفاده از داده‌های اقلیمی و سامانه اطلاعات مکانی (مطالعه موردی: استان ایلام). نشریه سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی،7(1): 57-41.

2. تقوایی، م.،صبوحی، ع. 1396. پهنه‌بندی و مکانیابی نیروگاه‌های خورشیدی در استان اصفهان. نشریه پژوهش و برنامه‌ریزی شهری، 8(28): 61-82.

3. طباطبایی، ط.، امیری، ف. 1394. مکانیابی نیروگاه‌های بادی براساس ارزیابی چندمعیاره مکانی و فرآیند تحلیل سلسله مراتبی (مطالعه موردی: استان بوشهر). نشریه سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی،6(1): 1-16.

4. گرجی، م.، خشنود، س.، عمرانی، ح.، هاشمی، م. 1395. مکانیابی مناطق مستعد نیروگاه خورشیدی تحت تأثیر پارامترهای اقلیمی با استفاده از تحلیل سلسله مراتبی فازی (مطالعه موردی: استان فارس). نشریه سنجش از دور و سامانه اطلاعات جغرافیایی در منابع طبیعی،8(1): 66-85.

5. موقری، ع.،طاوسی، ت. 1392. امکام‌سنجی و پهنه‌بندی مکان‌های مستعد جهت استقرار پنل‌های خورشیدی با تکیه بر فراسنج‌های اقلیمی در استان سیستان و بلوچستان. مجله پژوهش‌های برنامه‌ریزی و سیاستگذاری انرژی،1(1):99-114.

6. نادی‌زاده شورابه، س.، نیسانی سامانی، ن.، جلوخانی نیارکی، م. 1396. تعیین مناطق بهینه دفن پسماند با تأکید بر روند گسترش شهری براساس تلفیق مدل فرآیند سلسله مراتبی و میانگین وزنی مرتب شده، نشریه محیط زیست طبیعی، 70(4): 949-969.

7. Afshari Pour, S., Hamzeh, S., and Neysani Samany, N. 2017. Site Selection of Solar Power Plant using GIS-Fuzzy DEMATEL Model: A Case Study of Bam and Jiroft Cities of Kerman Province in Iran. Journal of Solar Energy Research, 2(4), 323-328.

8. Aguayo, P. 2013. Solar energy potential analysis at building scale using LiDAR and satellite data (Master’s thesis, University of Waterloo). 148 pp.

9. Ahmed, B. 2015. Landslide susceptibility mapping using multi-criteria evaluation techniques in Chittagong Metropolitan Area, Bangladesh. Landslides, 12(6), 1077-1095.

10. Al Garni, H. Z., andAwasthi, A. 2017. Solar PV power plant site selection using a GIS-AHP based approach with application in Saudi Arabia. Applied Energy, 206, 1225-1240.

11. Alamdari, P., Nematollahi, O., and Alemrajabi, A. A. 2013. Solar energy potentials in Iran: A review. Renewable and Sustainable Energy Reviews, 21, 778-788.

12. Allen, R. G., Tasumi, M., Trezza, R., Waters, R., and Bastiaanssen, W. 2002. SEBAL (Surface Energy Balance Algorithms for Land). Advance Training and Users Manual–Idaho Implementation, version, 1, 97.

13. Aly, A., Jensen, S. S., and Pedersen, A. B. 2017. Solar power potential of Tanzania: Identifying CSP and PV hot spots through a GIS multicriteria decision making analysis. Renewable Energy, 113, 159-175.

14. Aydin, N. Y., Kentel, E., andDuzgun, S. 2010. GIS-based environmental assessment of wind energy systems for spatial planning: A case study from Western Turkey. Renewable and Sustainable Energy Reviews, 14(1), 364-373.

15. Boroushaki, S., and Malczewski,J. 2008. Implementing an extension of the analytical hierarchy process using ordered weighted averaging operators with fuzzy quantifiers in ArcGIS. Computers & Geosciences, 34(4), 399-410.

16. Candelise, C., Winskel, M., and Gross. R. J. 2013. The dynamics of solar PV costs and prices as a challenge for technology forecasting. Renewable and Sustainable Energy Reviews, 26, 96-107.

17. Carrión, J. A., Estrella, A. E., Dols, F. A., Toro, M. Z., Rodríguez, M., andRidao, A. R. 2008. Environmental decision-support systems for evaluating the carrying capacity of land areas: Optimal site selection for grid-connected photovoltaic power plants. Renewable and Sustainable Energy Reviews, 12(9), 2358-2380.

18. Cevallos-Sierra, J., & Ramos-Martin, J. 2018. Spatial assessment of the potential of renewable energy: The case of Ecuador. Renewable and Sustainable Energy Reviews, 81, 1154-1165.

19. Charabi, Y., and Gastli,A. 2011. PV site suitability analysis using GIS-based spatial fuzzy multi-criteria evaluation. Renewable Energy, 36(9), 2554-2561.

20. Chen, H., Wood, M. D., Linstead. C., and Maltby, E. 2011. Uncertainty analysis in a GIS-based multi-criteria analysis tool for river catchment management. Environmental modelling & software, 26(4), 395-405.

21. Delgado, M. G., and Sendra, J. B. 2004. Sensitivity analysis in multicriteria spatial decision-making: a review. Human and Ecological Risk Assessment, 10(6), 1173-1187.

22. Doljak, D., andStanojeviæ, G. 2017. Evaluation of natural conditions for site selection of ground-mounted photovoltaic power plants in Serbia. Energy, 127, 291-300.

23. Doorga, J. R., Rughooputh, S. D., & Boojhawon, R. 2019. Multi-criteria GIS-based modelling technique for identifying potential solar farm sites: A case study in Mauritius. Renewable energy, 133, 1201-1219.

24. Effat, H. A. 2013. Selection of potential sites for solar energy farms in Ismailia Governorate, Egypt using SRTM and multicriteria analysis. International Journal of Advanced Remote Sensing and GIS, 2(1), 205-220.

25. ESRI.2016. Modeling solar radiation. http://pro.arcgis.com/en/pro-app/toolreference/spatial-analyst/modeling-solar-radiation.htm.

26. Feick, R., and Hall. B. 2004. A method for examining the spatial dimension of multi-criteria weight sensitivity. International Journal of Geographical Information Science, 18(8), 815-840.

27. Feizizadeh, B., andBlaschke, T. 2012. Comparing GIS-Multicriteria Decision Analysis for landslide susceptibility mapping for the lake basin, Iran. In Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International.5390-5393 pp.

28. Gastli, A., and Charabi, Y. 2010. Solar electricity prospects in Oman using GIS-based solar radiation maps. Renewable and Sustainable Energy Reviews, 14(2), 790-797.

29. Gorsevski, PV., Cathcart, S. C., Mirzaei, G., Jamali, M. M., Ye, X., and Gomezdelcampo, E. 2013. A group-based spatial decision support system for wind farm site selection in Northwest Ohio. Energy Policy, 55, 374-385.

30. Gorsevski, P. V., Donevska, K. R., Mitrovski, C. D., and Frizado, J. P. 2012. Integrating multi-criteria evaluation techniques with geographic information systems for landfill site selection: a case study using ordered weighted average. Waste management, 32(2), 287-296.

31. Höfer, T., Sunak, Y., Siddique, H., and Madlener, R.2016. Wind farm siting using a spatial Analytic Hierarchy Process approach: A case study of the Städteregion Aachen. Applied energy, 163, 222-243.

32. Hosenuzzaman, M., Rahim, N. A., Selvaraj, J., Hasanuzzaman, M., Malek, A.B.M.A., and Nahar, A. 2015. Global prospects, progress, policies, and environmental impact of solar photovoltaic power generation. Renewable and Sustainable Energy Reviews, 41, 284-297.

33. Janke, J. R. 2010. Multicriteria GIS modeling of wind and solar farms in Colorado. Renewable Energy, 35(10), 2228-2234.

34. Jelokhani-Niaraki, M., and Malczewski, J. 2015a. A group multicriteria spatial decision support system for parking site selection problem: A case study. Land Use Policy, 42, 492-508.

35. Jelokhani-Niaraki, M., and Malczewski, J. 2015b. Decision complexity and consensus in Web-based spatial decision making: A case study of site selection problem using GIS and multicriteria analysis. Cities, 45, 60-70.

36. Jelokhani-Niaraki, M., and Malczewski, J.2015c. The decision task complexity and information acquisition strategies in GIS-MCDA. International Journal of Geographical Information Science, 29(2), 327-344.

37. Jiang, H., andEastman, J. R. 2000. Application of fuzzy measures in multi-criteria evaluation in GIS. International Journal of Geographical Information Science, 14(2), 173-184.

38. Kiavarz, M., andJelokhani-Niaraki,M. 2017. Geothermal prospectivity mapping using GIS-based Ordered Weighted Averaging approach: A case study in Japan’s Akita and Iwate provinces. Geothermics, 70, 295-304.

39. Iaaly, A., Jadayel, O., Karame, N., & Khayat, N. 2019. Solar Power Plant Site Location Suitability Analysis Using GIS Weighted Average Raster Overlay [Lebanon]. In Advances in Remote Sensing and Geo Informatics Applications (pp. 37-40). Springer, Cham.

40. Lee, A., Kang, H. Y., & Liou, Y. J. 2017. A hybrid multiple-criteria decision-making approach for photovoltaic solar plant location selection. Sustainability, 9(2), 184.

41. Lenin, D., andKumar, S. J. 2015. GIS based multicriterion site suitability analysis for solar power generation plants in India. The International Journal of Science and Technoledge, 3(3), 197.

42. Liu, J., Xu, F., and Lin, S. 2017. Site selection of photovoltaic power plants in a value chain based on grey cumulative prospect theory for sustainability: A case study in Northwest China. Journal of cleaner production, 148, 386-397.

43. Malczewski, J. 1999. GIS and multicriteria decision analysis. John Wiley & Sons.

44. Malczewski, J. 2006. Ordered weighted averaging with fuzzy quantifiers: GIS-based multicriteria evaluation for land-use suitability analysis. International Journal of Applied Earth Observation and Geoinformation, 8(4), 270-277.

45. Malczewski, J., andRinner, C. 2016. Multicriteria decision analysis in geographic information science. Springer.

46. Mekonnen, A. D., and Gorsevski, P. V. 2015. A web-based participatory GIS (PGIS) for offshore wind farm suitability within Lake Erie, Ohio. Renewable and Sustainable Energy Reviews, 41, 162-177.

47. Moriarty, P., andHonnery, D. 2012. What is the global potential for renewable energy? Renewable and Sustainable Energy Reviews, 16(1), 244-252.

48.Noorollahi, E., Fadai, D., Akbarpour Shirazi, M., andGhodsipour, SH. 2016. Land suitability analysis for solar farms exploitation using GIS and fuzzy analytic hierarchy process (FAHP)—a case study of Iran. Energies, 9(8), 643.

49. Noorollahi, Y., Itoi, R., Fujii, H., andTanaka, T. 2007. GIS model for geothermal resource exploration in Akita and Iwate prefectures, northern Japan. Computers & geosciences, 33(8), 1008-1021.

50. Protocol, K. 1997. United Nations framework convention on climate change. Kyoto Protocol, Kyoto, 19.

51. Rahnama, M. R., Aghajani, H., and Fattahi, M. 2012. Integrating Ordered Weighted Average (OWA) techniques with geographic information Systems for landfill site selection (Case study: Metropolis of Mashhad). Geography and Environmental Hazards 3. 13-15.

52. Rinner, C., andMalczewski,J. 2002. Web-enabled spatial decision analysis using Ordered Weighted Averaging (OWA). Journal of Geographical Systems, 4(4), 385-403.

53. Saaty, T. L. 1980. The Analytical Hierarchy Process, Planning, Priority. Resource Allocation. RWS Publications, USA.

54. Saaty, T. L.1986. Axiomatic foundation of the analytic hierarchy process. Management science, 32(7), 841-855.

55. Saaty, T. L., andVargas GL. 1991. Prediction, projection and forecasting Kluwer.

56. Sabzevari, A. R., andDelavar, M. R. 2017. GIS-BASED SITE SELECTION FOR UNDERGROUND NATURAL RESOURCES USING FUZZY AHP-OWA. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, 42, 463-468.

57. Sadeghi, M., and Karimi, M. 2017. GIS-BASED SOLAR AND WIND TURBINE SITE SELECTION USING MULTI-CRITERIA ANALYSIS: CASE STUDY TEHRAN, IRAN. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 469-476.

58. Sánchez-Lozano, J. M., Antunes, C. H., García-Cascales, M. S., andDias, L. C. 2014. GIS-based photovoltaic solar farms site selection using ELECTRE-TRI: Evaluating the case for Torre Pacheco, Murcia, Southeast of Spain. Renewable Energy, 66, 478-494.

59. Sindhu, S., Nehra, V., & Luthra, S. 2017. Investigation of feasibility study of solar farms deployment using hybrid AHP-TOPSIS analysis: Case study of India. Renewable and Sustainable Energy Reviews, 73, 496-511.

60. Store, R., and Kangas, J. 2001. Integrating spatial multi-criteria evaluation and expert knowledge for GIS-based habitat suitability modelling. Landscape and urban planning, 55(2), 79-93.

61. Suh, J., & Brownson, J. 2016. Solar farm suitability using geographic information system fuzzy sets and analytic hierarchy processes: Case study of ulleung island, Korea. Energies, 9(8), 648.

62. Suuronen, A., Lensu, A., Kuitunen, M., Andrade-Alvear, R., Celis, N. G., Miranda, M., Perez, M., and Kukkonen, J. V.2017. Optimization of photovoltaic solar power plant locations in northern Chile. Environmental Earth Sciences, 76(24), 824.

63. Tahri, M., Hakdaoui, M., andMaanan,M. 2015. The evaluation of solar farm locations applying Geographic Information System and Multi-Criteria Decision-Making methods: Case study in southern Morocco. Renewable and Sustainable Energy Reviews, 51, 1354-1362.

64. United Nations. 2015. In: 21st Conference of the parties of the UNFCCC in Paris. Retrieved in 2017 from https://treaties.un.org/ doc/Publication/UNTS/No%20Volume/54113/Part/I-54113- 0800000280458f37.pdf

65. Uyan, M. 2013. GIS-based solar farms site selection using analytic hierarchy process (AHP) in Karapinar region, Konya/Turkey. Renewable and Sustainable Energy Reviews, 28, 11-17.

66. Von Hoyningen-Huene, W., Schmidt, T., Schienbein, S., Kee, C. A., and Tick, L. J. 1999. Climate-relevant aerosol parameters of South-East-Asian forest fire haze. Atmospheric Environment, 33(19), 3183-3190.

67. Xu, J., Song, X., Wu, Y., andZeng, Z. 2015. GIS-modelling based coal-fired power plant site identification and selection. Applied energy, 159, 520-539.

68. Yager, R. R. 1988. On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Transactions on systems, Man, and Cybernetics, vol. 18, 183-190.

69. Yager, R. R, Kelman A. 1999. An extension of the analytical hierarchy process using OWA operators. Journal of Intelligent & Fuzzy Systems, 7(4), 401-417.

70. Yalcin, M., andGul, F. K. 2017. A GIS-based multi criteria decision analysis approach for exploring geothermal resources: Akarcay basin (Afyonkarahisar). Geothermics, 67, 18-28.

71. Yousefi, H., Hafeznia, H., & Yousefi-Sahzabi, A. 2018. Spatial Site Selection for Solar Power Plants Using a GIS-Based Boolean-Fuzzy Logic Model: A Case Study of Markazi Province, Iran. Energies, 11(7), 1648.

72. Yushchenko, A., De Bono, A., Chatenoux, B., Patel, M. K., & Ray, N. 2018. GIS-based assessment of photovoltaic (PV) and concentrated solar power (CSP) generation potential in West Africa. Renewable and Sustainable Energy Reviews, 81, 2088-2103.

73. Zabihi, H., Alizadeh, M., Kibet Langat, P., Karami, M., Shahabi, H., Ahmad, A., ... & Lee, S. 2019. GIS Multi-Criteria Analysis by Ordered Weighted Averaging (OWA): Toward an Integrated Citrus Management Strategy. Sustainability, 11(4), 1009.

74. Zoghi, M., Ehsani, A. H., Sadat, M., javad Amiri, M., and Karimi, S. 2017. Optimization solar site selection by fuzzy logic model and weighted linear combination method in arid and semi-arid region: A case study Isfahan-IRAN. Renewable and Sustainable Energy Reviews, 68, 986-996.