Araştırma Makalesi
BibTex RIS Kaynak Göster

Hybrid Multi-Criteria GIS-based Site Selection for Offshore Wind Energy in the Continental Shelf of Rize

Yıl 2025, Cilt: 6 Sayı: 2, 315 - 342, 27.09.2025
https://doi.org/10.48123/rsgis.1754468

Öz

This study develops an integrated, multi-criteria decision support framework for offshore wind farm site selection within the continental shelf of Rize Province, utilizing Geographic Information Systems (GIS). By combining the Fuzzy Analytic Hierarchy Process (FAHP), Analytic Hierarchy Process (AHP), Entropy Weighting Method (EWM), and Half-Quadratic Programming (HQP), the framework balances subjective expert judgments with objective data-driven metrics, enhancing the reliability and consistency of the decision-making process. GIS-based spatial analysis enables high-accuracy visualization of the spatial distribution of suitability criteria, producing actionable and transparent outcomes. Approximately 13.5% of the study area was identified as having high or very high suitability, with optimal zones concentrated off the coasts of Rize Central, Pazar, and Çayeli—regions that offer strategic advantages such as proximity to port and transmission infrastructure, suitable water depths, and low ecological sensitivity. Nevertheless, the region’s generally weak wind regime necessitates careful turbine selection and energy modeling. The study contributes to the literature by offering a multilayered evaluation that goes beyond technical suitability to include environmental, geological, and logistical dimensions, while introducing a hybrid weighting strategy that models the site selection process innovatively. The proposed framework is flexible and replicable under diverse regional and climatic conditions, serving as a reference for future applications.

Kaynakça

  • Aldersey-Williams, J., Broadbent, I. D., & Strachan, P. A. (2019). Better estimates of LCOE from audited accounts – A new methodology with examples from United Kingdom offshore wind and CCGT. Energy Policy, 128, 25–35. https://doi.org/10.1016/j.enpol.2018.12.044
  • Ali, S., Lee, S. M., & Jang, C. M. (2017). Determination of the most optimal on-shore wind farm site location using a GIS–MCDM methodology: Evaluating the case of South Korea. Energies, 10(12), Article 2072. https://doi.org/10.3390/en10122072
  • Ali, F., Etemad-Shahidi, A., Stewart, R. A., Sanjari, M. J., Hayward, J. A., & Nicholson, R. C. (2024). Co-located offshore wind and floating solar farms: A systematic quantitative literature review of site selection criteria. Renewable Energy Focus, 50, Article 100611. https://doi.org/10.1016/j.ref.2024.100611
  • Argin, M., Yerci, V., Erdogan, N., Kucuksari, S., & Cali, U. (2019). Exploring the offshore wind energy potential of Turkey based on multi-criteria site selection. Energy Strategy Reviews, 23, 33–46. https://doi.org/10.1016/j.esr.2018.12.005
  • Başeğmez, M. (2025a). Strategic multi-criteria framework for nuclear plant siting: Integrating AHP, EWM, and game theory with GIS. Progress in Nuclear Energy, 188, Article 105897. https://doi.org/10.1016/j.pnucene.2025.105897
  • Başeğmez, M. (2025b). A comprehensive GIS-driven hybrid approach for safe and sustainable radioactive waste disposal facility planning. Annals of Nuclear Energy, 223, Article 111629. https://doi.org/10.1016/j.anucene.2025.111629
  • Basegmez, M., & Aydin, C. C. (2025). Climate change impact on green spaces planning in an urban area using a hybrid approach. Environmental Science and Pollution Research, 32(7), 4288–4312. https://doi.org/10.1007/s11356-025-35927-1
  • Bayram, P., Celep, E., Kostak, S., & Öztürk, Z. K. (2021). Heterojen rüzgâr çiftliği saha seçimi. Sürdürülebilir Mühendislik Uygulamaları ve Teknolojik Gelişmeler Dergisi, 4(2), 64–78. https://doi.org/10.51764/SMUTGD.962923
  • Bidwell, D. (2017). Ocean beliefs and support for an offshore wind energy project. Ocean & Coastal Management, 146, 99–108. https://doi.org/10.1016/j.ocecoaman.2017.06.012
  • Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets and Systems, 17(3), 233–247. https://doi.org/10.1016/0165-0114(85)90090-9
  • Büyüközkan, G., Karabulut, Y., & Mukul, E. (2018). A novel renewable energy selection model for United Nations’ Sustainable Development Goals. Energy, 165, 290–302. https://doi.org/10.1016/j.energy.2018.08.215
  • Caceoğlu, E., Yildiz, H. K., Oğuz, E., Huvaj, N., & Guerrero, J. M. (2022). Offshore wind power plant site selection using analytical hierarchy process for Northwest Turkey. Ocean Engineering, 252, Article 111178. https://doi.org/10.1016/j.oceaneng.2022.111178
  • Cali, U., Erdogan, N., Kucuksari, S., & Argin, M. (2018). Techno-economic analysis of high-potential offshore wind farm locations in Turkey. Energy Strategy Reviews, 22, 325–336. https://doi.org/10.1016/j.esr.2018.10.007
  • Canat, A. N., & Özkan, C. (2024). Appropriate port selection for floating based offshore wind farms with MCDM methods: The case of Türkiye. Journal of the Faculty of Engineering and Architecture of Gazi University, 40(1), 639–652. https://doi.org/10.17341/GAZIMMFD.1398232
  • Caner, H. I., & Aydin, C. C. (2021). Shipyard site selection by raster calculation method and AHP in GIS environment, İskenderun, Turkey. Marine Policy, 127, Article 104439. https://doi.org/10.1016/j.marpol.2021.104439
  • Castro-Santos, L., Lamas-Galdo, M. I., & Filgueira-Vizoso, A. (2020). Managing the oceans: Site selection of a floating offshore wind farm based on GIS spatial analysis. Marine Policy, 113, Article 103803. https://doi.org/10.1016/j.marpol.2019.103803
  • Chamanehpour, E. (2017). Site selection of wind power plant using multi-criteria decision-making methods in GIS: A case study. Computational Ecology and Software, 7(2), 49–64.
  • Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), 649–655. https://doi.org/10.1016/0377-2217(95)00300-2
  • Chen, L., & Macdonald, E. (2014). A system-level cost-of-energy wind farm layout optimization with landowner modeling. Energy Conversion and Management, 77, 484–494. https://doi.org/10.1016/j.enconman.2013.10.003
  • Deveci, M., Özcan, E., & John, R. (2020, February 6–7). Offshore wind farms: A fuzzy approach to site selection in a Black Sea Region [Conference presentation]. 2020 IEEE Texas Power and Energy Conference (TPEC), College Station, TX, USA.
  • Díaz-Cuevas, P., Biberacher, M., Domínguez-Bravo, J., & Schardinger, I. (2018). Developing a wind energy potential map on a regional scale using GIS and multi-criteria decision methods: The case of Cadiz (south of Spain). Clean Technologies and Environmental Policy, 20(6), 1167–1183. https://doi.org/10.1007/s10098-018-1539-x
  • DTU Wind and Energy Systems. (2025). Global Wind Atlas. Retrieved April 1, 2025, from https://globalwindatlas.info/en/
  • Emeksiz, C., & Demirci, B. (2019). The determination of offshore wind energy potential of Turkey by using novelty hybrid site selection method. Sustainable Energy Technologies and Assessments, 36, Article 100562. https://doi.org/10.1016/j.seta.2019.100562
  • European Commission. (2025). EMODnet map viewer. Retrieved January 24, 2025, from https://emodnet.ec.europa.eu/ geoviewer/#
  • European Space Agency. (2025). Copernicus Marine Data Store | Copernicus Marine Service. Retrieved January 4, 2025, from https://data.marine.copernicus.eu/products?facets=areas%7EBlack+Sea
  • Feng, Y., Fanghui, Y., & Li, C. (2019). Improved entropy weighting model in water quality evaluation. Water Resources Management, 33(6), 2049–2056. https://doi.org/10.1007/s11269-019-02227-6
  • Feng, Z., Li, G., Wang, W., Zhang, L., Xiang, W., He, X., Zhang, M., & Wei, N. (2023). Emergency logistics centers site selection by multi-criteria decision-making and GIS. International Journal of Disaster Risk Reduction, 96, Article 103921. https://doi.org/10.1016/j.ijdrr.2023.103921
  • Fetanat, A., & Khorasaninejad, E. (2015). A novel hybrid MCDM approach for offshore wind farm site selection: A case study of Iran. Ocean & Coastal Management, 109, 17–28. https://doi.org/10.1016/j.ocecoaman.2015.02.005
  • Genç, M. S., Karipoğlu, F., Koca, K., & Azgın, Ş. T. (2021). Suitable site selection for offshore wind farms in Turkey’s seas: GIS-MCDM based approach. Earth Science Informatics, 14(3), 1213–1225. https://doi.org/10.1007/s12145-021-00632-3
  • Gil-García, I. C., Ramos-Escudero, A., Molina-García, Á., & Fernández-Guillamón, A. (2023). GIS-based MCDM dual optimization approach for territorial-scale offshore wind power plants. Journal of Cleaner Production, 428, Article 139484. https://doi.org/10.1016/j.jclepro.2023.139484
  • Hasanzadeh, M., Kamran, K. V., Feizizadeh, B., & Mollabashi, S. H. (2024). GIS based spatial decision-making approach for solar energy site selection, Ardabil, Iran. International Journal of Engineering and Geosciences, 9(1), 115–130. https://doi.org/10.26833/IJEG.1341451
  • Hosseinzadeh, S., Etemad-Shahidi, A., & Stewart, R. A. (2023). Site selection of combined offshore wind and wave energy farms: A systematic review. Energies, 16(4), Article 2074. https://doi.org/10.3390/en16042074
  • Hou, P., Enevoldsen, P., Hu, W., Chen, C., & Chen, Z. (2017). Offshore wind farm repowering optimization. Applied Energy, 208, 834–844. https://doi.org/10.1016/j.apenergy.2017.09.064
  • İnci, M., & Türksoy, Ö. (2019). Review of fuel cells to grid interface: Configurations, technical challenges and trends. Journal of Cleaner Production, 213, 1353–1370. https://doi.org/10.1016/j.jclepro.2018.12.281
  • Ioannou, A., Angus, A., & Brennan, F. (2018). A lifecycle techno-economic model of offshore wind energy for different entry and exit instances. Applied Energy, 221, 406–424. https://doi.org/10.1016/j.apenergy.2018.03.143
  • International Renewable Energy Agency. (2020). Renewable capacity statistics 2020. https://www.eaere.org/policy/ energy/irenas-renewable-capacity-statistics-2020/.
  • Isiacik Colak, T. A., Senel, G., & Goksel, C. (2021). GIS-based maritime spatial planning for site selection of offshore wind farms with exergy efficiency analysis: A case study. International Journal of Exergy, 34(2), 255–268. https://doi.org/10.1504/IJEX.2021.113008
  • Iyappan, L., & Pandian, P. K. (2016). Geoprocessing model for identifying potential wind farm locations. IET Renewable Power Generation, 10(9), 1287–1297. https://doi.org/10.1049/iet-rpg.2015.0187
  • Jadoon, T. R., Ali, M. K., Hussain, S., Wasim, A., & Jahanzaib, M. (2020). Sustaining power production in hydropower stations of developing countries. Sustainable Energy Technologies and Assessments, 37, Article 100637. https://doi.org/10.1016/j.seta.2020.100637
  • Jeon, S. H., Cho, Y. U., Seo, M. W., Cho, J. R., & Jeong, W. B. (2013). Dynamic response of floating substructure of spar-type offshore wind turbine with catenary mooring cables. Ocean Engineering, 72, 356–364. https://doi.org/10.1016/j.oceaneng.2013.07.017
  • Kabak, M., & Akalın, S. (2022). A model proposal for selecting the installation location of offshore wind energy turbines. International Journal of Energy and Environmental Engineering, 13(1), 121–134. https://doi.org/10.1007/s40095-021-00421-0
  • Kim, C. K., Jang, S., & Kim, T. Y. (2018). Site selection for offshore wind farms in the southwest coast of South Korea. Renewable Energy, 120, 151–162. https://doi.org/10.1016/j.renene.2017.12.081
  • Lee, J., Qin, S., Coughlan, K., & Aubeny, C. P. (2025, May 5–8). Decision-making framework for marine anchor selection in floating offshore wind farms [Conference presentation]. Annual Offshore Technology Conference, Houston, Texas, USA.
  • Lerch, M., De-Prada-Gil, M., Molins, C., & Benveniste, G. (2018). Sensitivity analysis on the levelized cost of energy for floating offshore wind farms. Sustainable Energy Technologies and Assessments, 30, 77–90. https://doi.org/10.1016/j.seta.2018.09.005
  • Li, Y., Huang, X., Tee, K. F., Li, Q., & Wu, X. P. (2020). Comparative study of onshore and offshore wind characteristics and wind energy potentials: A case study for southeast coastal region of China. Sustainable Energy Technologies and Assessments, 39, Article 100711. https://doi.org/10.1016/j.seta.2020.100711
  • Liao, H., Jiang, L., Lev, B., & Fujita, H. (2019). Novel operations of PLTSs based on the disparity degrees of linguistic terms and their use in designing the probabilistic linguistic ELECTRE III method. Applied Soft Computing, 80, 450–464. https://doi.org/10.1016/j.asoc.2019.04.018
  • Liu, P. C. Y., Lo, H. W., & Liou, J. J. H. (2020). A combination of DEMATEL- and BWM-based ANP methods for exploring the green building rating system in Taiwan. Sustainability, 12(8), Article 3216. https://doi.org/10.3390/su12083216
  • Lo, H. W., Hsu, C. C., Chen, B. C., & Liou, J. J. H. (2021). Building a grey-based multi-criteria decision-making model for offshore wind farm site selection. Sustainable Energy Technologies and Assessments, 43, Article 100935. https://doi.org/10.1016/j.seta.2020.100935
  • Lo, H. W., & Liou, J. J. H. (2018). A novel multiple-criteria decision-making-based FMEA model for risk assessment. Applied Soft Computing, 73, 684–696. https://doi.org/10.1016/j.asoc.2018.09.020
  • Luthra, S., Govindan, K., & Mangla, S. K. (2017). Structural model for sustainable consumption and production adoption: A grey-DEMATEL based approach. Resources, Conservation and Recycling, 125, 198–207. https://doi.org/10.1016/j.resconrec.2017.02.018
  • Maslov, N., Claramunt, C., Wang, T., & Tang, T. (2017). Method to estimate the visual impact of an offshore wind farm. Applied Energy, 204, 1422–1430. https://doi.org/10.1016/j.apenergy.2017.05.053
  • Mittal, P., Mitra, K., & Kulkarni, K. (2017). Optimizing the number and locations of turbines in a wind farm addressing energy–noise trade-off: A hybrid approach. Energy Conversion and Management, 132, 147–160. https://doi.org/10.1016/j.enconman.2016.11.014
  • Mohammadi, M., & Rezaei, J. (2020). Ensemble ranking: Aggregation of rankings produced by different multi-criteria decision-making methods. Omega, 96, Article 102254. https://doi.org/10.1016/j.omega.2020.102254
  • Mohammadifar, A., Gholami, H., & Golzari, S. (2023). Novel integrated modelling based on multiplicative long short-term memory (mLSTM) deep learning model and ensemble multi-criteria decision making (MCDM) models for mapping flood risk. Journal of Environmental Management, 345, Article 118838. https://doi.org/10.1016/j.jenvman.2023.118838
  • Neelakanta, P., Kapur, J., & Kesavan, H. (1992). Entropy optimization principles with applications. Academic Press/Harcourt Brace Jovanovich Publishers.
  • Negro, V., López-Gutiérrez, J. S., Esteban, M. D., Alberdi, P., Imaz, M., & Serraclara, J. M. (2017). Monopiles in offshore wind: Preliminary estimate of main dimensions. Ocean Engineering, 133, 253–261. https://doi.org/10.1016/j.oceaneng.2017.02.011
  • Oguz, E., Clelland, D., Day, A. H., Incecik, A., López, J. A., Sánchez, G., & Almeria, G. G. (2018). Experimental and numerical analysis of a TLP floating offshore wind turbine. Ocean Engineering, 147, 591–605. https://doi.org/10.1016/j.oceaneng.2017.10.052
  • OpenStreetMap. (2025). OpenStreetMap data. Retrieved March 19, 2025, from https://www.openstreetmap.org/ #map=7/39.031/35.252
  • Ospino-Castro, A., Robles-Algarín, C., Mangones-Cordero, A., & Romero-Navas, S. (2023). An analytic hierarchy process-based approach for evaluating feasibility of offshore wind farm on the Colombian Caribbean coast. International Journal of Energy Economics and Policy, 13(6), 64–73. https://doi.org/10.32479/ijeep.14621
  • Pillai, A. C., Chick, J., Khorasanchi, M., Barbouchi, S., & Johanning, L. (2017). Application of an offshore wind farm layout optimization methodology at Middelgrunden wind farm. Ocean Engineering, 139, 287–297. https://doi.org/10.1016/j.oceaneng.2017.04.049
  • Poudyal, R., Loskot, P., Nepal, R., Parajuli, R., & Khadka, S. K. (2019). Mitigating the current energy crisis in Nepal with renewable energy sources. Renewable and Sustainable Energy Reviews, 116, Article 109388. https://doi.org/10.1016/j.rser.2019.109388
  • Qin, Y., Guan, K., Kou, J., Ma, Y., Zhou, H., & Zhang, X. (2022). Durability evaluation and life prediction of fiber concrete with fly ash based on entropy weight method and grey theory. Construction and Building Materials, 327, Article 126918. https://doi.org/10.1016/j.conbuildmat.2022.126918
  • Roy, U., & Majumder, M. (2019). Productivity yielding in shell and tube heat exchanger by MCDM–NBO approach. Measurement and Control, 52(3–4), 262–275. https://doi.org/10.1177/0020294019836109
  • Saaty, R. W. (1987). The analytic hierarchy process—What it is and how it is used. Mathematical Modelling, 9(3–5), 161–176. https://doi.org/10.1016/0270-0255(87)90473-8
  • Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83–98.
  • Sarı, F., & Koyuncu, F. (2021). Multi-criteria decision analysis to determine the suitability of agricultural crops for land consolidation areas. International Journal of Engineering and Geosciences, 6(2), 64–73. https://doi.org/10.26833/ijeg.683754
  • Sarkar, B., Das, P., Islam, N., Basak, A., Debnath, M., & Roy, R. (2022). Land suitability analysis for paddy crop using GIS-based fuzzy-AHP (F-AHP) method in Koch Bihar District, West Bengal. Geocarto International, 37(25), 8952–8978.
  • Savanović, R., & Savanović, M. (2024). The need for renewal of the cadastral maps on the territory of Vojvodina, Republic of Serbia. International Journal of Engineering and Geosciences, 9(3), 324–333. https://doi.org/10.26833/ijeg.1422964
  • Shao, M., Mao, Z., Sun, J., Guan, X., Shao, Z., & Tang, T. (2025). Multi-scale offshore wind farm site selection decision framework based on GIS, MCDM and meta-heuristic algorithm. Ocean Engineering, 316, Article 119921. https://doi.org/10.1016/j.oceaneng.2024.119921
  • Sharma, R., Mahanti, G. K., Panda, G., Rath, A., Dash, S., Mallik, S., & Zhao, Z. (2023). Comparative performance analysis of binary variants of FOX optimization algorithm with half-quadratic ensemble ranking method for thyroid cancer detection. Scientific Reports, 13(1), Article 19598. https://doi.org/10.1038/s41598-023-46865-8
  • Susanty, A., Saptadi, S., Dewi, W. R., & Tjahjono, B. (2019). Analyzing the drivers of green manufacturing practices using fuzzy TOPSIS: Case study Bandarharjo fish smoked industry centre. International Journal of Applied Science and Engineering, 16(1), 47–56. https://doi.org/10.6703/ijase.201906_16(1).047
  • Takeuchi, A. (2023). Social dimensions of offshore wind energy: A review of theories and frameworks of multi-criteria decision-making. Current Sustainable/Renewable Energy Reports, 10(4), 243–249. https://doi.org/10.1007/s40518-023-00225-2
  • T.C. Çevre, Şehircilik ve İklim Değişikliği Bakanlığı. (2025). TUCBS. Retrieved February 1, 2025, from https://tucbs.gov.tr/
  • Tercan, E., Tapkın, S., Latinopoulos, D., Dereli, M. A., Tsiropoulos, A., & Ak, M. F. (2020). A GIS-based multi-criteria model for offshore wind energy power plants site selection in both sides of the Aegean Sea. Environmental Monitoring and Assessment, 192(10), Article 652. https://doi.org/10.1007/s10661-020-08603-9
  • Topçu, G. Z., Bayır, K., Cavıldak, Z. E., Başeğmez, M., & Aydın, C. C. (2023). Yeşil alan uygunluk analizinin CBS tabanlı AHP ve TOPSIS yöntemleriyle değerlendirilmesi. Geomatik, 8(3), 235–249. https://doi.org/10.29128/geomatik.1171069
  • Umoh, K., Hasan, A., & Kenjegaliev, A. (2025). Combined AHP–GIS methodology for floating offshore wind site selection in South Africa. Ocean Engineering, 317, Article 120037. https://doi.org/10.1016/j.oceaneng.2024.120037
  • U.S. Geological Survey. (2025). Search Earthquake Catalog. Retrieved January 4, 2025, from https://earthquake.usgs.gov/earthquakes/search/
  • Urfalı, T., & Eymen, A. (2021). CBS ve AHP yöntemi yardımıyla Kayseri ili örneğinde rüzgâr enerji santrallerinin yer seçimi. Geomatik, 6(3), 227–237. https://doi.org/10.29128/geomatik.772453
  • Vaisi, S., Shariati, E., & Ghaslani, N. (2024). Integrated methodology for construction site selection: A case study of the Tazeh Abad neighborhood, Sanandaj City. Journal of Studies in Science and Engineering, 4(2), 48–73. https://doi.org/10.53898/josse2024423
  • Vieira, M., Snyder, B., Henriques, E., & Reis, L. (2019). European offshore wind capital cost trends up to 2020. Energy Policy, 129, 1364–1371. https://doi.org/10.1016/j.enpol.2019.03.036
  • Vyas, V., Uma, V., & Ravi, K. (2022). Aspect-based approach to measure performance of financial services using voice of customer. Journal of King Saud University – Computer and Information Sciences, 34(5), 2262–2270. https://doi.org/10.1016/j.jksuci.2019.12.009
  • Wang, B., Song, J., Ren, J., Li, K., Duan, H., & Wang, X. (2019). Selecting sustainable energy conversion technologies for agricultural residues: A fuzzy AHP–VIKOR based prioritization from life cycle perspective. Resources, Conservation and Recycling, 142, 78–87. https://doi.org/10.1016/j.resconrec.2018.11.011
  • Wang, T. C., & Chen, Y. H. (2008). Applying fuzzy linguistic preference relations to the improvement of consistency of fuzzy AHP. Information Sciences, 178(19), 3755–3765. https://doi.org/10.1016/j.ins.2008.05.028
  • Wu, H.-W., Li, E., Sun, Y., & Dong, B. (2021). Research on the operation safety evaluation of urban rail stations based on the improved TOPSIS method and entropy weight method. Journal of Rail Transport Planning & Management, 20, Article 100262. https://doi.org/10.1016/j.jrtpm.2021.100262
  • Wu, Y., Tao, Y., Zhang, B., Wang, S., Xu, C., & Zhou, J. (2020). A decision framework of offshore wind power station site selection using a PROMETHEE method under intuitionistic fuzzy environment: A case in China. Ocean & Coastal Management, 184, Article 105016. https://doi.org/10.1016/j.ocecoaman.2019.105016
  • Wu, Y., Zhang, J., Yuan, J., Geng, S., & Zhang, H. (2016). Study of decision framework of offshore wind power station site selection based on ELECTRE-III under intuitionistic fuzzy environment: A case of China. Energy Conversion and Management, 113, 66–81. https://doi.org/10.1016/j.enconman.2016.01.020
  • Xilin, L., & Long, C. (2019). Evaluation and research on site selection scheme of high-speed railway station based on binary semantic combination weighting method. IOP Conference Series: Materials Science and Engineering, 688(2), Article 022040. https://doi.org/10.1088/1757-899X/688/2/022040
  • Yang, J. J., Chuang, Y. C., Lo, H. W., & Lee, T. I. (2020). A two-stage MCDM model for exploring the influential relationships of sustainable sports tourism criteria in Taichung City. International Journal of Environmental Research and Public Health, 17(7), Article 2319. https://doi.org/10.3390/ijerph17072319
  • Yang, J., Xing, S., Qiu, R., Chen, Y., Hua, C., & Dong, D. (2022). Mathematical problems in engineering decision-making based on improved entropy weighting method: An example of passenger comfort in a smart cockpit of a car. Mathematical Problems in Engineering, 2022(1), Article 6846696. https://doi.org/10.1155/2022/6846696
  • Yılmaz, D., Akkaya, S., & Vaheddoost, B. (2023). Gemlik ilçesi rüzgâr enerji santrali potansiyel yer analizi. Geomatik, 8(3), 264–276. https://doi.org/10.29128/geomatik.1209940
  • Zaidan, B. B., Ibrahim, H. A., Mourad, N., Zaidan, A. A., Pilehkouhic, H., Qahtan, S., Deveci, M., & Delen, D. (2024). An in-depth analysis of ensemble multi-criteria decision making: A comprehensive guide to terminology, design, applications, evaluations, and future prospects. Applied Soft Computing, 167, Article 112267. https://doi.org/10.1016/j.asoc.2024.112267
  • Zaredar, N., & Kheirkhah Zarkesh, M. M. (2012). Examination of compensatory model application in site selection. Environmental Monitoring and Assessment, 184(1), 397–404. https://doi.org/10.1007/s10661-011-1976-z
  • Zhang, J., Zhang, J., Cai, L., & Ma, L. (2017). Energy performance of wind power in China: A comparison among inland, coastal and offshore wind farms. Journal of Cleaner Production, 143, 836–842. https://doi.org/10.1016/j.jclepro.2016.12.040
  • Zhao, H., Ge, Y., & Wang, W. (2024). A study on offshore wind farm site selection based on CRITIC and CPT-TOPSIS: A case study of China. Kybernetes, 53(3), 1117–1147. https://doi.org/10.1108/K-09-2022-1267
  • Zhu, Y., Tian, D., & Yan, F. (2020). Effectiveness of entropy weight method in decision-making. Mathematical Problems in Engineering, 2020(1), Article 3564835. https://doi.org/10.1155/2020/3564835

Açık Deniz Rüzgâr Enerjisi İçin Rize Kıta Sahanlığında CBS Destekli Hibrit Çok Kriterli Saha Seçimi

Yıl 2025, Cilt: 6 Sayı: 2, 315 - 342, 27.09.2025
https://doi.org/10.48123/rsgis.1754468

Öz

Bu çalışma, Rize ili kıta sahanlığı için açık deniz rüzgâr santrali saha seçimine yönelik coğrafi bilgi sistemleri (CBS) destekli, çok kriterli ve entegre bir karar destek çerçevesi geliştirmiştir. Bulanık analitik hiyerarşi süreci (FAHP), analitik hiyerarşi süreci (AHP), entropi ağırlıklandırma yöntemi (EWM) ve yarı-karesel programlama (HQP) yöntemlerinin birlikte kullanılmasıyla hem öznel uzman görüşleri hem de nesnel veriler dengeli şekilde değerlendirilmiş, karar sürecine güvenilirlik ve tutarlılık kazandırılmıştır. CBS’nin mekânsal analiz kapasitesi sayesinde, uygunluk kriterlerinin coğrafi dağılımı yüksek doğrulukla görselleştirilmiş ve uygulanabilir, şeffaf sonuçlar elde edilmiştir. Çalışma alanının %13,5’i yüksek ve çok yüksek uygunluk düzeyinde bulunurken, özellikle Rize Merkez, Pazar ve Çayeli açıklarında yoğunlaşan bölgeler stratejik avantajlar taşımaktadır. Ancak bölgenin genel rüzgâr rejiminin zayıf olması, türbin seçiminin ve enerji üretim modellerinin dikkatli planlanmasını zorunlu kılmaktadır. Çalışmanın literatüre katkısı, teknik uygunluğun ötesinde çevresel, jeolojik ve lojistik faktörleri çok katmanlı biçimde ele alması ve hibrit ağırlıklandırma yaklaşımıyla saha seçim sürecini yenilikçi biçimde modellemesidir. Geliştirilen yöntem, farklı bölgesel ve iklimsel koşullarda da tekrarlanabilir bir referans çerçevesi sunmaktadır.

Kaynakça

  • Aldersey-Williams, J., Broadbent, I. D., & Strachan, P. A. (2019). Better estimates of LCOE from audited accounts – A new methodology with examples from United Kingdom offshore wind and CCGT. Energy Policy, 128, 25–35. https://doi.org/10.1016/j.enpol.2018.12.044
  • Ali, S., Lee, S. M., & Jang, C. M. (2017). Determination of the most optimal on-shore wind farm site location using a GIS–MCDM methodology: Evaluating the case of South Korea. Energies, 10(12), Article 2072. https://doi.org/10.3390/en10122072
  • Ali, F., Etemad-Shahidi, A., Stewart, R. A., Sanjari, M. J., Hayward, J. A., & Nicholson, R. C. (2024). Co-located offshore wind and floating solar farms: A systematic quantitative literature review of site selection criteria. Renewable Energy Focus, 50, Article 100611. https://doi.org/10.1016/j.ref.2024.100611
  • Argin, M., Yerci, V., Erdogan, N., Kucuksari, S., & Cali, U. (2019). Exploring the offshore wind energy potential of Turkey based on multi-criteria site selection. Energy Strategy Reviews, 23, 33–46. https://doi.org/10.1016/j.esr.2018.12.005
  • Başeğmez, M. (2025a). Strategic multi-criteria framework for nuclear plant siting: Integrating AHP, EWM, and game theory with GIS. Progress in Nuclear Energy, 188, Article 105897. https://doi.org/10.1016/j.pnucene.2025.105897
  • Başeğmez, M. (2025b). A comprehensive GIS-driven hybrid approach for safe and sustainable radioactive waste disposal facility planning. Annals of Nuclear Energy, 223, Article 111629. https://doi.org/10.1016/j.anucene.2025.111629
  • Basegmez, M., & Aydin, C. C. (2025). Climate change impact on green spaces planning in an urban area using a hybrid approach. Environmental Science and Pollution Research, 32(7), 4288–4312. https://doi.org/10.1007/s11356-025-35927-1
  • Bayram, P., Celep, E., Kostak, S., & Öztürk, Z. K. (2021). Heterojen rüzgâr çiftliği saha seçimi. Sürdürülebilir Mühendislik Uygulamaları ve Teknolojik Gelişmeler Dergisi, 4(2), 64–78. https://doi.org/10.51764/SMUTGD.962923
  • Bidwell, D. (2017). Ocean beliefs and support for an offshore wind energy project. Ocean & Coastal Management, 146, 99–108. https://doi.org/10.1016/j.ocecoaman.2017.06.012
  • Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy Sets and Systems, 17(3), 233–247. https://doi.org/10.1016/0165-0114(85)90090-9
  • Büyüközkan, G., Karabulut, Y., & Mukul, E. (2018). A novel renewable energy selection model for United Nations’ Sustainable Development Goals. Energy, 165, 290–302. https://doi.org/10.1016/j.energy.2018.08.215
  • Caceoğlu, E., Yildiz, H. K., Oğuz, E., Huvaj, N., & Guerrero, J. M. (2022). Offshore wind power plant site selection using analytical hierarchy process for Northwest Turkey. Ocean Engineering, 252, Article 111178. https://doi.org/10.1016/j.oceaneng.2022.111178
  • Cali, U., Erdogan, N., Kucuksari, S., & Argin, M. (2018). Techno-economic analysis of high-potential offshore wind farm locations in Turkey. Energy Strategy Reviews, 22, 325–336. https://doi.org/10.1016/j.esr.2018.10.007
  • Canat, A. N., & Özkan, C. (2024). Appropriate port selection for floating based offshore wind farms with MCDM methods: The case of Türkiye. Journal of the Faculty of Engineering and Architecture of Gazi University, 40(1), 639–652. https://doi.org/10.17341/GAZIMMFD.1398232
  • Caner, H. I., & Aydin, C. C. (2021). Shipyard site selection by raster calculation method and AHP in GIS environment, İskenderun, Turkey. Marine Policy, 127, Article 104439. https://doi.org/10.1016/j.marpol.2021.104439
  • Castro-Santos, L., Lamas-Galdo, M. I., & Filgueira-Vizoso, A. (2020). Managing the oceans: Site selection of a floating offshore wind farm based on GIS spatial analysis. Marine Policy, 113, Article 103803. https://doi.org/10.1016/j.marpol.2019.103803
  • Chamanehpour, E. (2017). Site selection of wind power plant using multi-criteria decision-making methods in GIS: A case study. Computational Ecology and Software, 7(2), 49–64.
  • Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP. European Journal of Operational Research, 95(3), 649–655. https://doi.org/10.1016/0377-2217(95)00300-2
  • Chen, L., & Macdonald, E. (2014). A system-level cost-of-energy wind farm layout optimization with landowner modeling. Energy Conversion and Management, 77, 484–494. https://doi.org/10.1016/j.enconman.2013.10.003
  • Deveci, M., Özcan, E., & John, R. (2020, February 6–7). Offshore wind farms: A fuzzy approach to site selection in a Black Sea Region [Conference presentation]. 2020 IEEE Texas Power and Energy Conference (TPEC), College Station, TX, USA.
  • Díaz-Cuevas, P., Biberacher, M., Domínguez-Bravo, J., & Schardinger, I. (2018). Developing a wind energy potential map on a regional scale using GIS and multi-criteria decision methods: The case of Cadiz (south of Spain). Clean Technologies and Environmental Policy, 20(6), 1167–1183. https://doi.org/10.1007/s10098-018-1539-x
  • DTU Wind and Energy Systems. (2025). Global Wind Atlas. Retrieved April 1, 2025, from https://globalwindatlas.info/en/
  • Emeksiz, C., & Demirci, B. (2019). The determination of offshore wind energy potential of Turkey by using novelty hybrid site selection method. Sustainable Energy Technologies and Assessments, 36, Article 100562. https://doi.org/10.1016/j.seta.2019.100562
  • European Commission. (2025). EMODnet map viewer. Retrieved January 24, 2025, from https://emodnet.ec.europa.eu/ geoviewer/#
  • European Space Agency. (2025). Copernicus Marine Data Store | Copernicus Marine Service. Retrieved January 4, 2025, from https://data.marine.copernicus.eu/products?facets=areas%7EBlack+Sea
  • Feng, Y., Fanghui, Y., & Li, C. (2019). Improved entropy weighting model in water quality evaluation. Water Resources Management, 33(6), 2049–2056. https://doi.org/10.1007/s11269-019-02227-6
  • Feng, Z., Li, G., Wang, W., Zhang, L., Xiang, W., He, X., Zhang, M., & Wei, N. (2023). Emergency logistics centers site selection by multi-criteria decision-making and GIS. International Journal of Disaster Risk Reduction, 96, Article 103921. https://doi.org/10.1016/j.ijdrr.2023.103921
  • Fetanat, A., & Khorasaninejad, E. (2015). A novel hybrid MCDM approach for offshore wind farm site selection: A case study of Iran. Ocean & Coastal Management, 109, 17–28. https://doi.org/10.1016/j.ocecoaman.2015.02.005
  • Genç, M. S., Karipoğlu, F., Koca, K., & Azgın, Ş. T. (2021). Suitable site selection for offshore wind farms in Turkey’s seas: GIS-MCDM based approach. Earth Science Informatics, 14(3), 1213–1225. https://doi.org/10.1007/s12145-021-00632-3
  • Gil-García, I. C., Ramos-Escudero, A., Molina-García, Á., & Fernández-Guillamón, A. (2023). GIS-based MCDM dual optimization approach for territorial-scale offshore wind power plants. Journal of Cleaner Production, 428, Article 139484. https://doi.org/10.1016/j.jclepro.2023.139484
  • Hasanzadeh, M., Kamran, K. V., Feizizadeh, B., & Mollabashi, S. H. (2024). GIS based spatial decision-making approach for solar energy site selection, Ardabil, Iran. International Journal of Engineering and Geosciences, 9(1), 115–130. https://doi.org/10.26833/IJEG.1341451
  • Hosseinzadeh, S., Etemad-Shahidi, A., & Stewart, R. A. (2023). Site selection of combined offshore wind and wave energy farms: A systematic review. Energies, 16(4), Article 2074. https://doi.org/10.3390/en16042074
  • Hou, P., Enevoldsen, P., Hu, W., Chen, C., & Chen, Z. (2017). Offshore wind farm repowering optimization. Applied Energy, 208, 834–844. https://doi.org/10.1016/j.apenergy.2017.09.064
  • İnci, M., & Türksoy, Ö. (2019). Review of fuel cells to grid interface: Configurations, technical challenges and trends. Journal of Cleaner Production, 213, 1353–1370. https://doi.org/10.1016/j.jclepro.2018.12.281
  • Ioannou, A., Angus, A., & Brennan, F. (2018). A lifecycle techno-economic model of offshore wind energy for different entry and exit instances. Applied Energy, 221, 406–424. https://doi.org/10.1016/j.apenergy.2018.03.143
  • International Renewable Energy Agency. (2020). Renewable capacity statistics 2020. https://www.eaere.org/policy/ energy/irenas-renewable-capacity-statistics-2020/.
  • Isiacik Colak, T. A., Senel, G., & Goksel, C. (2021). GIS-based maritime spatial planning for site selection of offshore wind farms with exergy efficiency analysis: A case study. International Journal of Exergy, 34(2), 255–268. https://doi.org/10.1504/IJEX.2021.113008
  • Iyappan, L., & Pandian, P. K. (2016). Geoprocessing model for identifying potential wind farm locations. IET Renewable Power Generation, 10(9), 1287–1297. https://doi.org/10.1049/iet-rpg.2015.0187
  • Jadoon, T. R., Ali, M. K., Hussain, S., Wasim, A., & Jahanzaib, M. (2020). Sustaining power production in hydropower stations of developing countries. Sustainable Energy Technologies and Assessments, 37, Article 100637. https://doi.org/10.1016/j.seta.2020.100637
  • Jeon, S. H., Cho, Y. U., Seo, M. W., Cho, J. R., & Jeong, W. B. (2013). Dynamic response of floating substructure of spar-type offshore wind turbine with catenary mooring cables. Ocean Engineering, 72, 356–364. https://doi.org/10.1016/j.oceaneng.2013.07.017
  • Kabak, M., & Akalın, S. (2022). A model proposal for selecting the installation location of offshore wind energy turbines. International Journal of Energy and Environmental Engineering, 13(1), 121–134. https://doi.org/10.1007/s40095-021-00421-0
  • Kim, C. K., Jang, S., & Kim, T. Y. (2018). Site selection for offshore wind farms in the southwest coast of South Korea. Renewable Energy, 120, 151–162. https://doi.org/10.1016/j.renene.2017.12.081
  • Lee, J., Qin, S., Coughlan, K., & Aubeny, C. P. (2025, May 5–8). Decision-making framework for marine anchor selection in floating offshore wind farms [Conference presentation]. Annual Offshore Technology Conference, Houston, Texas, USA.
  • Lerch, M., De-Prada-Gil, M., Molins, C., & Benveniste, G. (2018). Sensitivity analysis on the levelized cost of energy for floating offshore wind farms. Sustainable Energy Technologies and Assessments, 30, 77–90. https://doi.org/10.1016/j.seta.2018.09.005
  • Li, Y., Huang, X., Tee, K. F., Li, Q., & Wu, X. P. (2020). Comparative study of onshore and offshore wind characteristics and wind energy potentials: A case study for southeast coastal region of China. Sustainable Energy Technologies and Assessments, 39, Article 100711. https://doi.org/10.1016/j.seta.2020.100711
  • Liao, H., Jiang, L., Lev, B., & Fujita, H. (2019). Novel operations of PLTSs based on the disparity degrees of linguistic terms and their use in designing the probabilistic linguistic ELECTRE III method. Applied Soft Computing, 80, 450–464. https://doi.org/10.1016/j.asoc.2019.04.018
  • Liu, P. C. Y., Lo, H. W., & Liou, J. J. H. (2020). A combination of DEMATEL- and BWM-based ANP methods for exploring the green building rating system in Taiwan. Sustainability, 12(8), Article 3216. https://doi.org/10.3390/su12083216
  • Lo, H. W., Hsu, C. C., Chen, B. C., & Liou, J. J. H. (2021). Building a grey-based multi-criteria decision-making model for offshore wind farm site selection. Sustainable Energy Technologies and Assessments, 43, Article 100935. https://doi.org/10.1016/j.seta.2020.100935
  • Lo, H. W., & Liou, J. J. H. (2018). A novel multiple-criteria decision-making-based FMEA model for risk assessment. Applied Soft Computing, 73, 684–696. https://doi.org/10.1016/j.asoc.2018.09.020
  • Luthra, S., Govindan, K., & Mangla, S. K. (2017). Structural model for sustainable consumption and production adoption: A grey-DEMATEL based approach. Resources, Conservation and Recycling, 125, 198–207. https://doi.org/10.1016/j.resconrec.2017.02.018
  • Maslov, N., Claramunt, C., Wang, T., & Tang, T. (2017). Method to estimate the visual impact of an offshore wind farm. Applied Energy, 204, 1422–1430. https://doi.org/10.1016/j.apenergy.2017.05.053
  • Mittal, P., Mitra, K., & Kulkarni, K. (2017). Optimizing the number and locations of turbines in a wind farm addressing energy–noise trade-off: A hybrid approach. Energy Conversion and Management, 132, 147–160. https://doi.org/10.1016/j.enconman.2016.11.014
  • Mohammadi, M., & Rezaei, J. (2020). Ensemble ranking: Aggregation of rankings produced by different multi-criteria decision-making methods. Omega, 96, Article 102254. https://doi.org/10.1016/j.omega.2020.102254
  • Mohammadifar, A., Gholami, H., & Golzari, S. (2023). Novel integrated modelling based on multiplicative long short-term memory (mLSTM) deep learning model and ensemble multi-criteria decision making (MCDM) models for mapping flood risk. Journal of Environmental Management, 345, Article 118838. https://doi.org/10.1016/j.jenvman.2023.118838
  • Neelakanta, P., Kapur, J., & Kesavan, H. (1992). Entropy optimization principles with applications. Academic Press/Harcourt Brace Jovanovich Publishers.
  • Negro, V., López-Gutiérrez, J. S., Esteban, M. D., Alberdi, P., Imaz, M., & Serraclara, J. M. (2017). Monopiles in offshore wind: Preliminary estimate of main dimensions. Ocean Engineering, 133, 253–261. https://doi.org/10.1016/j.oceaneng.2017.02.011
  • Oguz, E., Clelland, D., Day, A. H., Incecik, A., López, J. A., Sánchez, G., & Almeria, G. G. (2018). Experimental and numerical analysis of a TLP floating offshore wind turbine. Ocean Engineering, 147, 591–605. https://doi.org/10.1016/j.oceaneng.2017.10.052
  • OpenStreetMap. (2025). OpenStreetMap data. Retrieved March 19, 2025, from https://www.openstreetmap.org/ #map=7/39.031/35.252
  • Ospino-Castro, A., Robles-Algarín, C., Mangones-Cordero, A., & Romero-Navas, S. (2023). An analytic hierarchy process-based approach for evaluating feasibility of offshore wind farm on the Colombian Caribbean coast. International Journal of Energy Economics and Policy, 13(6), 64–73. https://doi.org/10.32479/ijeep.14621
  • Pillai, A. C., Chick, J., Khorasanchi, M., Barbouchi, S., & Johanning, L. (2017). Application of an offshore wind farm layout optimization methodology at Middelgrunden wind farm. Ocean Engineering, 139, 287–297. https://doi.org/10.1016/j.oceaneng.2017.04.049
  • Poudyal, R., Loskot, P., Nepal, R., Parajuli, R., & Khadka, S. K. (2019). Mitigating the current energy crisis in Nepal with renewable energy sources. Renewable and Sustainable Energy Reviews, 116, Article 109388. https://doi.org/10.1016/j.rser.2019.109388
  • Qin, Y., Guan, K., Kou, J., Ma, Y., Zhou, H., & Zhang, X. (2022). Durability evaluation and life prediction of fiber concrete with fly ash based on entropy weight method and grey theory. Construction and Building Materials, 327, Article 126918. https://doi.org/10.1016/j.conbuildmat.2022.126918
  • Roy, U., & Majumder, M. (2019). Productivity yielding in shell and tube heat exchanger by MCDM–NBO approach. Measurement and Control, 52(3–4), 262–275. https://doi.org/10.1177/0020294019836109
  • Saaty, R. W. (1987). The analytic hierarchy process—What it is and how it is used. Mathematical Modelling, 9(3–5), 161–176. https://doi.org/10.1016/0270-0255(87)90473-8
  • Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83–98.
  • Sarı, F., & Koyuncu, F. (2021). Multi-criteria decision analysis to determine the suitability of agricultural crops for land consolidation areas. International Journal of Engineering and Geosciences, 6(2), 64–73. https://doi.org/10.26833/ijeg.683754
  • Sarkar, B., Das, P., Islam, N., Basak, A., Debnath, M., & Roy, R. (2022). Land suitability analysis for paddy crop using GIS-based fuzzy-AHP (F-AHP) method in Koch Bihar District, West Bengal. Geocarto International, 37(25), 8952–8978.
  • Savanović, R., & Savanović, M. (2024). The need for renewal of the cadastral maps on the territory of Vojvodina, Republic of Serbia. International Journal of Engineering and Geosciences, 9(3), 324–333. https://doi.org/10.26833/ijeg.1422964
  • Shao, M., Mao, Z., Sun, J., Guan, X., Shao, Z., & Tang, T. (2025). Multi-scale offshore wind farm site selection decision framework based on GIS, MCDM and meta-heuristic algorithm. Ocean Engineering, 316, Article 119921. https://doi.org/10.1016/j.oceaneng.2024.119921
  • Sharma, R., Mahanti, G. K., Panda, G., Rath, A., Dash, S., Mallik, S., & Zhao, Z. (2023). Comparative performance analysis of binary variants of FOX optimization algorithm with half-quadratic ensemble ranking method for thyroid cancer detection. Scientific Reports, 13(1), Article 19598. https://doi.org/10.1038/s41598-023-46865-8
  • Susanty, A., Saptadi, S., Dewi, W. R., & Tjahjono, B. (2019). Analyzing the drivers of green manufacturing practices using fuzzy TOPSIS: Case study Bandarharjo fish smoked industry centre. International Journal of Applied Science and Engineering, 16(1), 47–56. https://doi.org/10.6703/ijase.201906_16(1).047
  • Takeuchi, A. (2023). Social dimensions of offshore wind energy: A review of theories and frameworks of multi-criteria decision-making. Current Sustainable/Renewable Energy Reports, 10(4), 243–249. https://doi.org/10.1007/s40518-023-00225-2
  • T.C. Çevre, Şehircilik ve İklim Değişikliği Bakanlığı. (2025). TUCBS. Retrieved February 1, 2025, from https://tucbs.gov.tr/
  • Tercan, E., Tapkın, S., Latinopoulos, D., Dereli, M. A., Tsiropoulos, A., & Ak, M. F. (2020). A GIS-based multi-criteria model for offshore wind energy power plants site selection in both sides of the Aegean Sea. Environmental Monitoring and Assessment, 192(10), Article 652. https://doi.org/10.1007/s10661-020-08603-9
  • Topçu, G. Z., Bayır, K., Cavıldak, Z. E., Başeğmez, M., & Aydın, C. C. (2023). Yeşil alan uygunluk analizinin CBS tabanlı AHP ve TOPSIS yöntemleriyle değerlendirilmesi. Geomatik, 8(3), 235–249. https://doi.org/10.29128/geomatik.1171069
  • Umoh, K., Hasan, A., & Kenjegaliev, A. (2025). Combined AHP–GIS methodology for floating offshore wind site selection in South Africa. Ocean Engineering, 317, Article 120037. https://doi.org/10.1016/j.oceaneng.2024.120037
  • U.S. Geological Survey. (2025). Search Earthquake Catalog. Retrieved January 4, 2025, from https://earthquake.usgs.gov/earthquakes/search/
  • Urfalı, T., & Eymen, A. (2021). CBS ve AHP yöntemi yardımıyla Kayseri ili örneğinde rüzgâr enerji santrallerinin yer seçimi. Geomatik, 6(3), 227–237. https://doi.org/10.29128/geomatik.772453
  • Vaisi, S., Shariati, E., & Ghaslani, N. (2024). Integrated methodology for construction site selection: A case study of the Tazeh Abad neighborhood, Sanandaj City. Journal of Studies in Science and Engineering, 4(2), 48–73. https://doi.org/10.53898/josse2024423
  • Vieira, M., Snyder, B., Henriques, E., & Reis, L. (2019). European offshore wind capital cost trends up to 2020. Energy Policy, 129, 1364–1371. https://doi.org/10.1016/j.enpol.2019.03.036
  • Vyas, V., Uma, V., & Ravi, K. (2022). Aspect-based approach to measure performance of financial services using voice of customer. Journal of King Saud University – Computer and Information Sciences, 34(5), 2262–2270. https://doi.org/10.1016/j.jksuci.2019.12.009
  • Wang, B., Song, J., Ren, J., Li, K., Duan, H., & Wang, X. (2019). Selecting sustainable energy conversion technologies for agricultural residues: A fuzzy AHP–VIKOR based prioritization from life cycle perspective. Resources, Conservation and Recycling, 142, 78–87. https://doi.org/10.1016/j.resconrec.2018.11.011
  • Wang, T. C., & Chen, Y. H. (2008). Applying fuzzy linguistic preference relations to the improvement of consistency of fuzzy AHP. Information Sciences, 178(19), 3755–3765. https://doi.org/10.1016/j.ins.2008.05.028
  • Wu, H.-W., Li, E., Sun, Y., & Dong, B. (2021). Research on the operation safety evaluation of urban rail stations based on the improved TOPSIS method and entropy weight method. Journal of Rail Transport Planning & Management, 20, Article 100262. https://doi.org/10.1016/j.jrtpm.2021.100262
  • Wu, Y., Tao, Y., Zhang, B., Wang, S., Xu, C., & Zhou, J. (2020). A decision framework of offshore wind power station site selection using a PROMETHEE method under intuitionistic fuzzy environment: A case in China. Ocean & Coastal Management, 184, Article 105016. https://doi.org/10.1016/j.ocecoaman.2019.105016
  • Wu, Y., Zhang, J., Yuan, J., Geng, S., & Zhang, H. (2016). Study of decision framework of offshore wind power station site selection based on ELECTRE-III under intuitionistic fuzzy environment: A case of China. Energy Conversion and Management, 113, 66–81. https://doi.org/10.1016/j.enconman.2016.01.020
  • Xilin, L., & Long, C. (2019). Evaluation and research on site selection scheme of high-speed railway station based on binary semantic combination weighting method. IOP Conference Series: Materials Science and Engineering, 688(2), Article 022040. https://doi.org/10.1088/1757-899X/688/2/022040
  • Yang, J. J., Chuang, Y. C., Lo, H. W., & Lee, T. I. (2020). A two-stage MCDM model for exploring the influential relationships of sustainable sports tourism criteria in Taichung City. International Journal of Environmental Research and Public Health, 17(7), Article 2319. https://doi.org/10.3390/ijerph17072319
  • Yang, J., Xing, S., Qiu, R., Chen, Y., Hua, C., & Dong, D. (2022). Mathematical problems in engineering decision-making based on improved entropy weighting method: An example of passenger comfort in a smart cockpit of a car. Mathematical Problems in Engineering, 2022(1), Article 6846696. https://doi.org/10.1155/2022/6846696
  • Yılmaz, D., Akkaya, S., & Vaheddoost, B. (2023). Gemlik ilçesi rüzgâr enerji santrali potansiyel yer analizi. Geomatik, 8(3), 264–276. https://doi.org/10.29128/geomatik.1209940
  • Zaidan, B. B., Ibrahim, H. A., Mourad, N., Zaidan, A. A., Pilehkouhic, H., Qahtan, S., Deveci, M., & Delen, D. (2024). An in-depth analysis of ensemble multi-criteria decision making: A comprehensive guide to terminology, design, applications, evaluations, and future prospects. Applied Soft Computing, 167, Article 112267. https://doi.org/10.1016/j.asoc.2024.112267
  • Zaredar, N., & Kheirkhah Zarkesh, M. M. (2012). Examination of compensatory model application in site selection. Environmental Monitoring and Assessment, 184(1), 397–404. https://doi.org/10.1007/s10661-011-1976-z
  • Zhang, J., Zhang, J., Cai, L., & Ma, L. (2017). Energy performance of wind power in China: A comparison among inland, coastal and offshore wind farms. Journal of Cleaner Production, 143, 836–842. https://doi.org/10.1016/j.jclepro.2016.12.040
  • Zhao, H., Ge, Y., & Wang, W. (2024). A study on offshore wind farm site selection based on CRITIC and CPT-TOPSIS: A case study of China. Kybernetes, 53(3), 1117–1147. https://doi.org/10.1108/K-09-2022-1267
  • Zhu, Y., Tian, D., & Yan, F. (2020). Effectiveness of entropy weight method in decision-making. Mathematical Problems in Engineering, 2020(1), Article 3564835. https://doi.org/10.1155/2020/3564835
Toplam 95 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Coğrafi Bilgi Sistemleri ve Mekansal Veri Modelleme
Bölüm Araştırma Makaleleri
Yazarlar

Murat Başeğmez 0000-0002-7704-9510

Yayımlanma Tarihi 27 Eylül 2025
Gönderilme Tarihi 30 Temmuz 2025
Kabul Tarihi 14 Eylül 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 6 Sayı: 2

Kaynak Göster

APA Başeğmez, M. (2025). Açık Deniz Rüzgâr Enerjisi İçin Rize Kıta Sahanlığında CBS Destekli Hibrit Çok Kriterli Saha Seçimi. Türk Uzaktan Algılama ve CBS Dergisi, 6(2), 315-342. https://doi.org/10.48123/rsgis.1754468

Creative Commons License
Turkish Journal of Remote Sensing and GIS (Türk Uzaktan Algılama ve CBS Dergisi), Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License ile lisanlanmıştır.