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EVALUATION OF THE GLOBAL WARMING IMPACTS USING A HYBRID METHOD BASED ON FUZZY TECHNIQUES: A CASE STUDY IN TURKEY

Yıl 2016, Cilt: 29 Sayı: 4, 883 - 894, 19.12.2016

Öz

The aim of this study is to measure the degree of effect of the global warming for cities of Turkey. The results of the global warming such as drought, temperature changes and rainfall changes are considered as criteria and the evaluation of the degree of effect of the global warming of the cities of Turkey is handled as a multi criteria decision making problem. A hybrid method considering fuzzy analytic hierarchy process and fuzzy measure theory is proposed to determine the corresponding degree of effect. Finally, considering real data, the map of effect with respect to the cities is presented.

Kaynakça

  • http://www.climate.nasa.gov/
  • Leimbach, M. Development of a Fuzzy optimization model, supporting global warming decision-making, Environmental and Resource Economics, Vol. 7 (2), 163-192 (1996).
  • Huang, G.H., Cohen, S.J., Yin, Y.Y. and Bass, B. Incorporation of inexact dynamic optimization with fuzzy relation analysis for integrated climate change impact study, Journal of Environmental Management, Vol. 48 (1), 45-68 (1996).
  • Kojiri, T., Hamaguchi, T., and Ode, M. Assessment of global warming impacts on water resources and ecology of a river basin in Japan, Journal of Hydro-environment Research, Vol. 1 (4), 164-175 (2008).
  • Prato, T. Evaluating and managing wildlife impacts of climate change under uncertainty, Ecological Modelling, Vol. 220 (7), 923-930 (2009).
  • Teegavarapu, R.S.V. Modeling climate change uncertainties in water resources management models, Environmental Modelling & Software, Vol. 25 (10), 1261-1265 (2010).
  • Zaman, H., and Shakouri, G.H. A combined 2-dimensional fuzzy regression model to study effect of climate change on the electricity consumption in Iran, 1st International Conference on Energy, Power and Control (EPC-IQ), College of Engineering, University of Basrah, Basrah, Iraq, November 30-December 2 (2010).
  • Cai, Y.P., Huang, G.H., Tan, Q., and Liu, L. An integrated approach for climate-change impact analysis and adaptation planning under multi-level uncertainties. Part II. Case study, Renewable and Sustainable Energy Reviews, Vol. 15 (6), 3051-3073 (2011).
  • Bernetti, I., Ciampi, C., Fagarazzi, C., and Sacchelli, S., The evaluation of forest crop damages due to climate change. An application of Dempster-Shafer method, Journal of Forest Economics, Vol. 17 (3), 285-297 (2011).
  • Kim, Y. and Chung, E-S. Fuzzy VIKOR approach for assessing the vulnerability of the water supply to climate change and variability in South Korea, Applied Mathematical Modelling, Vol. 37 (22), 9419-9430 (2013).
  • Jun, K-S., Chung, E-S., Kim, Y-G., and Kim, Y. A fuzzy multi-criteria approach to flood risk vulnerability in South Korea by considering climate change impacts, Expert Systems with Applications, Vol. 40 (4), 1003-1013 (2013).
  • Batisha, A.F. Implementing fuzzy decision making technique in analyzing the Nile Delta resilience to climate change, Alexandria Engineering Journal, Vol. 54 (4) 1043-1056 (2015).
  • Wu, J., and Shi, Y. Attribution index for changes in migratory bird distributions: The role of climate change over the past 50 years in China, Ecological Informatics, doi: 10.1016/j.ecoinf.2015.11.013 (2015).
  • Chatterjee, K., Bandyopadhyay, A., Ghosh, A., and Kar, S. Assessment of environmental factors causing wetland degradation, using Fuzzy Analytic Network Process: A case study on Keoladeo National Park, India, Ecological Modelling, Vol. 316, 1-13 (2015).
  • El-Zein, A., and Tonmoy, F.N. Assessment of vulnerability to climate change using a multi-criteria outranking approach with application to heat stress in Sydney, Ecological Indicators, Vol. 48, 207-217 (2015).
  • Kriegler, E., and Held, H. Utilizing belief functions for the estimation of future climate change, International Journal of Approximate Reasoning, Vol. 39 (2-3), 185-209 (2005).
  • Rahmani, M.A., and Zarghami, M. A new approach to combine climate change projections by ordered weighting averaging operator; applications to northwestern provinces of Iran, Global and Planetary Change, Vol. 102, 41-50 (2013).
  • Abdallah, N.B., Mouhous-Voyneau, N., and Denoeux, T. Combining statistical and expert evidence using belief functions: Application to centennial sea level estimation taking into account climate change, International Journal of Approximate Reasoning, Vol. 55 (1), 341-354, (2014).
  • Chen, T. Analyzing and forecasting the global CO2 concentration – a collaborative fuzzy–neural agent network approach, Journal of Applied Research and Technology, Vol. 13 (3), 364-373 (2015).
  • Larbani, M., Huang, C., and Tzeng, G. A novel method for fuzzy measure identification, International Journal of Fuzzy Systems, Vol. 13, No.1 (2011).
  • Simon, H.A., The New Science of Management Decision. New Jersey: Prentice Hall PTR (1977).
  • Keeney, R.L., Raiffa, H., Decision with Multiple Objectives: Preferences and Value Tradeoffs. New York: John Wiley and Son (1976).
  • Kleindorfer, P.R., Kunreuther, H.C., and Schoemaker, P.J.H., Decision Sciences: An Integrative Perspective. Cambridge: Cambridge University Press (1993).
  • Tzeng, G.H, and Huang, J.J., Multiple Attribute Decision Making: methods and applications. CRC press, Taylor Francis Group, Boca Raton (2011).
  • Grabisch, M. The application of fuzzy integrals in multi criteria decision making, European Journal of Operational Research, Vol. 89 (3), 445—456 (1996).
  • Sugeno M., and Kwon, S.H. A new approach to time series modeling with fuzzy measures and the Choquet integral, International Joint Conference of the 4th IEEE International Conference on Fuzzy Systems and the 2nd International Fuzzy Engineering Symposium, Yokohama, Japan, 799-804 (1995).
  • Tanaka, K., and Sugeno, M. A study on subjective evaluations of color printing images, 4th Fuzzy System Symposium, Tokyo, Japan, 229-234 (1988) (in Japanese).
  • lshii, K., and Sugeno, M. A model of human evaluation process using fuzzy measure, International Journal of ManMachine Studies, 22, 19-38 (1985).
  • Onisawa, T., Sugeno, M., Nishiwaki, Y., Kawai, H., and Harima, Y. Fuzzy measure analysis of public attitudetowards the use of nuclear energy, Fuzzy Sets & Systems , 20, 259-289 (1986).
  • Tanaka, K., and Sugeno, M. A study on subjective evaluation of color printing images, International Journal of Approximate Reasoning, 5, 213-222 (1991).
  • Inoue, K., and Anzai, T. A study on the industrial design evaluation based upon non-additive measures, 7th Fuzzy System Symposium, Nagoya, Japan, 521-524 (1991) (in Japanese).
  • Washio, T., Takahashi, H., and Kitamura, M. A method for supporting decision making on plant operation based on human reliability analysis by fuzzy integral", 2nd International Conference on Fuzzy Logic and Neural Networks, Iizuka, Japan, 841-845 (1992).
  • Kahraman, C., Ertay, T., and Büyüközkan, G. A fuzzy optimization model for QFD planning process using analytic network approach, European Journal of Operational Research 171, 390-411 (2006).
  • Dağdeviren, M., Personnel selection with fuzzy analytical hierarchy process and an application, J. Fac. Eng. Arch. Gazi Univ., 22 (4), 791-799 (2007) (in Turkish).
  • Özçelik, G., Aydoğan, E.K., and Gencer, C. A hybrid moora-fuzzy algorithm for special education and rehabilitation center selection. Journal of Military and Information Science, 2(3), 53-62 (2014).
  • Deng, H. Multicriteria analysis with fuzzy pairwise comparison, International Journal of Approximate Reasoning, 21 (3), 215-231 (1999).
  • http://www.mgm.gov.tr/en-US/about.aspx
  • http://www.kutahyasanayi.net/2014/
Yıl 2016, Cilt: 29 Sayı: 4, 883 - 894, 19.12.2016

Öz

Kaynakça

  • http://www.climate.nasa.gov/
  • Leimbach, M. Development of a Fuzzy optimization model, supporting global warming decision-making, Environmental and Resource Economics, Vol. 7 (2), 163-192 (1996).
  • Huang, G.H., Cohen, S.J., Yin, Y.Y. and Bass, B. Incorporation of inexact dynamic optimization with fuzzy relation analysis for integrated climate change impact study, Journal of Environmental Management, Vol. 48 (1), 45-68 (1996).
  • Kojiri, T., Hamaguchi, T., and Ode, M. Assessment of global warming impacts on water resources and ecology of a river basin in Japan, Journal of Hydro-environment Research, Vol. 1 (4), 164-175 (2008).
  • Prato, T. Evaluating and managing wildlife impacts of climate change under uncertainty, Ecological Modelling, Vol. 220 (7), 923-930 (2009).
  • Teegavarapu, R.S.V. Modeling climate change uncertainties in water resources management models, Environmental Modelling & Software, Vol. 25 (10), 1261-1265 (2010).
  • Zaman, H., and Shakouri, G.H. A combined 2-dimensional fuzzy regression model to study effect of climate change on the electricity consumption in Iran, 1st International Conference on Energy, Power and Control (EPC-IQ), College of Engineering, University of Basrah, Basrah, Iraq, November 30-December 2 (2010).
  • Cai, Y.P., Huang, G.H., Tan, Q., and Liu, L. An integrated approach for climate-change impact analysis and adaptation planning under multi-level uncertainties. Part II. Case study, Renewable and Sustainable Energy Reviews, Vol. 15 (6), 3051-3073 (2011).
  • Bernetti, I., Ciampi, C., Fagarazzi, C., and Sacchelli, S., The evaluation of forest crop damages due to climate change. An application of Dempster-Shafer method, Journal of Forest Economics, Vol. 17 (3), 285-297 (2011).
  • Kim, Y. and Chung, E-S. Fuzzy VIKOR approach for assessing the vulnerability of the water supply to climate change and variability in South Korea, Applied Mathematical Modelling, Vol. 37 (22), 9419-9430 (2013).
  • Jun, K-S., Chung, E-S., Kim, Y-G., and Kim, Y. A fuzzy multi-criteria approach to flood risk vulnerability in South Korea by considering climate change impacts, Expert Systems with Applications, Vol. 40 (4), 1003-1013 (2013).
  • Batisha, A.F. Implementing fuzzy decision making technique in analyzing the Nile Delta resilience to climate change, Alexandria Engineering Journal, Vol. 54 (4) 1043-1056 (2015).
  • Wu, J., and Shi, Y. Attribution index for changes in migratory bird distributions: The role of climate change over the past 50 years in China, Ecological Informatics, doi: 10.1016/j.ecoinf.2015.11.013 (2015).
  • Chatterjee, K., Bandyopadhyay, A., Ghosh, A., and Kar, S. Assessment of environmental factors causing wetland degradation, using Fuzzy Analytic Network Process: A case study on Keoladeo National Park, India, Ecological Modelling, Vol. 316, 1-13 (2015).
  • El-Zein, A., and Tonmoy, F.N. Assessment of vulnerability to climate change using a multi-criteria outranking approach with application to heat stress in Sydney, Ecological Indicators, Vol. 48, 207-217 (2015).
  • Kriegler, E., and Held, H. Utilizing belief functions for the estimation of future climate change, International Journal of Approximate Reasoning, Vol. 39 (2-3), 185-209 (2005).
  • Rahmani, M.A., and Zarghami, M. A new approach to combine climate change projections by ordered weighting averaging operator; applications to northwestern provinces of Iran, Global and Planetary Change, Vol. 102, 41-50 (2013).
  • Abdallah, N.B., Mouhous-Voyneau, N., and Denoeux, T. Combining statistical and expert evidence using belief functions: Application to centennial sea level estimation taking into account climate change, International Journal of Approximate Reasoning, Vol. 55 (1), 341-354, (2014).
  • Chen, T. Analyzing and forecasting the global CO2 concentration – a collaborative fuzzy–neural agent network approach, Journal of Applied Research and Technology, Vol. 13 (3), 364-373 (2015).
  • Larbani, M., Huang, C., and Tzeng, G. A novel method for fuzzy measure identification, International Journal of Fuzzy Systems, Vol. 13, No.1 (2011).
  • Simon, H.A., The New Science of Management Decision. New Jersey: Prentice Hall PTR (1977).
  • Keeney, R.L., Raiffa, H., Decision with Multiple Objectives: Preferences and Value Tradeoffs. New York: John Wiley and Son (1976).
  • Kleindorfer, P.R., Kunreuther, H.C., and Schoemaker, P.J.H., Decision Sciences: An Integrative Perspective. Cambridge: Cambridge University Press (1993).
  • Tzeng, G.H, and Huang, J.J., Multiple Attribute Decision Making: methods and applications. CRC press, Taylor Francis Group, Boca Raton (2011).
  • Grabisch, M. The application of fuzzy integrals in multi criteria decision making, European Journal of Operational Research, Vol. 89 (3), 445—456 (1996).
  • Sugeno M., and Kwon, S.H. A new approach to time series modeling with fuzzy measures and the Choquet integral, International Joint Conference of the 4th IEEE International Conference on Fuzzy Systems and the 2nd International Fuzzy Engineering Symposium, Yokohama, Japan, 799-804 (1995).
  • Tanaka, K., and Sugeno, M. A study on subjective evaluations of color printing images, 4th Fuzzy System Symposium, Tokyo, Japan, 229-234 (1988) (in Japanese).
  • lshii, K., and Sugeno, M. A model of human evaluation process using fuzzy measure, International Journal of ManMachine Studies, 22, 19-38 (1985).
  • Onisawa, T., Sugeno, M., Nishiwaki, Y., Kawai, H., and Harima, Y. Fuzzy measure analysis of public attitudetowards the use of nuclear energy, Fuzzy Sets & Systems , 20, 259-289 (1986).
  • Tanaka, K., and Sugeno, M. A study on subjective evaluation of color printing images, International Journal of Approximate Reasoning, 5, 213-222 (1991).
  • Inoue, K., and Anzai, T. A study on the industrial design evaluation based upon non-additive measures, 7th Fuzzy System Symposium, Nagoya, Japan, 521-524 (1991) (in Japanese).
  • Washio, T., Takahashi, H., and Kitamura, M. A method for supporting decision making on plant operation based on human reliability analysis by fuzzy integral", 2nd International Conference on Fuzzy Logic and Neural Networks, Iizuka, Japan, 841-845 (1992).
  • Kahraman, C., Ertay, T., and Büyüközkan, G. A fuzzy optimization model for QFD planning process using analytic network approach, European Journal of Operational Research 171, 390-411 (2006).
  • Dağdeviren, M., Personnel selection with fuzzy analytical hierarchy process and an application, J. Fac. Eng. Arch. Gazi Univ., 22 (4), 791-799 (2007) (in Turkish).
  • Özçelik, G., Aydoğan, E.K., and Gencer, C. A hybrid moora-fuzzy algorithm for special education and rehabilitation center selection. Journal of Military and Information Science, 2(3), 53-62 (2014).
  • Deng, H. Multicriteria analysis with fuzzy pairwise comparison, International Journal of Approximate Reasoning, 21 (3), 215-231 (1999).
  • http://www.mgm.gov.tr/en-US/about.aspx
  • http://www.kutahyasanayi.net/2014/
Toplam 38 adet kaynakça vardır.

Ayrıntılar

Bölüm Mathematics
Yazarlar

Gökhan Özçelik

Mehmet Ünver

Cevriye Temel Gencer

Yayımlanma Tarihi 19 Aralık 2016
Yayımlandığı Sayı Yıl 2016 Cilt: 29 Sayı: 4

Kaynak Göster

APA Özçelik, G., Ünver, M., & Temel Gencer, C. (2016). EVALUATION OF THE GLOBAL WARMING IMPACTS USING A HYBRID METHOD BASED ON FUZZY TECHNIQUES: A CASE STUDY IN TURKEY. Gazi University Journal of Science, 29(4), 883-894.
AMA Özçelik G, Ünver M, Temel Gencer C. EVALUATION OF THE GLOBAL WARMING IMPACTS USING A HYBRID METHOD BASED ON FUZZY TECHNIQUES: A CASE STUDY IN TURKEY. Gazi University Journal of Science. Aralık 2016;29(4):883-894.
Chicago Özçelik, Gökhan, Mehmet Ünver, ve Cevriye Temel Gencer. “EVALUATION OF THE GLOBAL WARMING IMPACTS USING A HYBRID METHOD BASED ON FUZZY TECHNIQUES: A CASE STUDY IN TURKEY”. Gazi University Journal of Science 29, sy. 4 (Aralık 2016): 883-94.
EndNote Özçelik G, Ünver M, Temel Gencer C (01 Aralık 2016) EVALUATION OF THE GLOBAL WARMING IMPACTS USING A HYBRID METHOD BASED ON FUZZY TECHNIQUES: A CASE STUDY IN TURKEY. Gazi University Journal of Science 29 4 883–894.
IEEE G. Özçelik, M. Ünver, ve C. Temel Gencer, “EVALUATION OF THE GLOBAL WARMING IMPACTS USING A HYBRID METHOD BASED ON FUZZY TECHNIQUES: A CASE STUDY IN TURKEY”, Gazi University Journal of Science, c. 29, sy. 4, ss. 883–894, 2016.
ISNAD Özçelik, Gökhan vd. “EVALUATION OF THE GLOBAL WARMING IMPACTS USING A HYBRID METHOD BASED ON FUZZY TECHNIQUES: A CASE STUDY IN TURKEY”. Gazi University Journal of Science 29/4 (Aralık 2016), 883-894.
JAMA Özçelik G, Ünver M, Temel Gencer C. EVALUATION OF THE GLOBAL WARMING IMPACTS USING A HYBRID METHOD BASED ON FUZZY TECHNIQUES: A CASE STUDY IN TURKEY. Gazi University Journal of Science. 2016;29:883–894.
MLA Özçelik, Gökhan vd. “EVALUATION OF THE GLOBAL WARMING IMPACTS USING A HYBRID METHOD BASED ON FUZZY TECHNIQUES: A CASE STUDY IN TURKEY”. Gazi University Journal of Science, c. 29, sy. 4, 2016, ss. 883-94.
Vancouver Özçelik G, Ünver M, Temel Gencer C. EVALUATION OF THE GLOBAL WARMING IMPACTS USING A HYBRID METHOD BASED ON FUZZY TECHNIQUES: A CASE STUDY IN TURKEY. Gazi University Journal of Science. 2016;29(4):883-94.