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Günlük Buharlaşma Miktarının Bulanık Mantık Yöntemleri Kullanılarak Bölgesel Olarak Modellenmesi

Year 2019, Volume: 2 Issue: 1, 23 - 29, 30.12.2019

Kübra Özdülkar Fatih Üneş Mustafa Demirci Yunus Ziya Kaya

Abstract

Buharlaşma miktarı, hidrolojik ve meteorolojik çalışmalarda önemli bir parametredir. Buharlaşmanın doğru bir şekilde tahmin edilmesi, su yapılarının planlanmasında ve işletilmesinde çok önemlidir. Bu çalışmada günlük buharlaşma miktarının tahmini için Amerika Birleşik Devletleri Jeolojik Araştırma Enstitüsü (USGS)’den elde edilen 2008-2012 yılları arasındaki buharlaşma verileri kullanılmıştır. Günlük buharlaşma tahmini Mamdani ve Sugeno bulanık mantık yöntemleri kullanılarak tahmin edilmeye çalışılmış ve ortaya çıkan sonuçlar karşılaştırılmıştır. Çalışma alanı olarak Lewisville Gölü yakınında (Texas, ABD) belirtilen bir istasyon seçilmiştir. Günlük ortalama buharlaşma tahmini için ortalama günlük hava sıcaklığı (T), rüzgar hızı (U), güneş ışınımı (SR) ve bağıl nem (RH) parametreleri kullanılmıştır. Bulanık Mantık modelleri arasında karşılaştırma yapılmış ve sonucunda Sugeno yönteminin performansının daha iyi olduğu görülmüştür.

Keywords

Evaporasyon Bulanık Mantık Sugeno Mamdani

References

  • [1] Jensen, M. E. Consumptive use of water and irrigation water requirements. ASCE. 1974.
  • [2] Frevert, D. K., Hill, R. W., & Braaten, B. C. Estimation of FAOevapotranspiration coefficients. Journal of Irrigation and Drainage Engineering 1983, 109(2), 265-270.
  • [3] Irmak, S., Haman, D. Z., & Jones, J. W. Evaluation of class A pan coefficients for estimating reference evapotranspiration in humid location. Journal of Irrigation and Drainage Engineering, 2002, 128(3), 153-159.
  • [4] Stephens, J. C., & Stewart, E. H. A comparison of procedures for computing evaporation and evapotranspiration. Publication, 1963, 62, 123-133.
  • [5] Burman, R. D. Intercontinental comparison of evaporation estimates. Journal of the Irrigation and Drainage Division, 1976, 102(1), 109-118.
  • [6] Dos Reis, R. J., & Dias, N. L. Multi-season lake evaporation: energy-budget estimates and CRLE model assessment with limited meteorological observations. Journal of Hydrology, 1998, 208(3-4), 135-147.
  • [7] Vallet-Coulomb, C., Legesse, D., Gasse, F., Travi, Y., & Chernet, T. Lake evaporation estimates in tropical Africa (Lake Ziway, Ethiopia). Journal of hydrology, 2001, 245(1-4), 1-18.
  • [8] Gavin, H., & Agnew, C. A. Modelling actual, reference and equilibrium evaporation from a temperate wet grassland. Hydrological Processes, 2004, 18(2), 229-246
  • [9] Sudheer, K. P., Gosain, A. K., Mohana Rangan, D., & Saheb, S. M. Modelling evaporation using an artificial neural network algorithm. Hydrological Processes, 2002, 16(16), 3189-3202.
  • [10] Taşar, B., Üneş, F., Demirci, M., & Kaya, Y. Z. Yapay sinir ağları yöntemi kullanılarak buharlaşma miktarı tahmini. DÜMF Mühendislik Dergisi, 2018, 9(1), 543-551. [11] Üneş, F., Doğan, S., Taşar, B., Kaya, Y., Demirci, M. The Evaluation and Comparison of Daily Reference Evapotranspiration with ANN and Empirical Methods. Natural and Engineering Sciences, 2018 3(3), Supplement, 54-64.
  • [12] Kaya, Y.Z., Taşar, B. Evapotranspiration Calculation for South Carolina, USA and Creation Different ANFIS Models for ET Estimation. 2019 ”Air and Water – Components of the Environment” Conference Proceedings, Cluj-Napoca, Romania, p. 217-224, 2019. DOI: 10.24193/AWC2019_22.
  • [13] Demirci, M., Üneş, F., & Saydemir, S. Suspended sediment estimation using an artificial intelligence approach. In: Sediment matters. Eds. P. Heininger, J. Cullmann. Springer International Publishing p. 83–95. 2015.
  • [14] Tasar, B., Kaya, Y. Z., Varcin, H., Üneş, F., & Demirci, M. Forecasting of Suspended Sediment in Rivers Using Artificial Neural Networks Approach, International Journal of Advanced Engineering Research and Science (IJAERS), 4(12), pp. 79-84. 2017. [15] Tașar, B., Unes, F., Varcin, H. Prediction of the Rainfall – Runoff Relationship Using Neuro-Fuzzy and Support Vector Machines. 2019 ”Air and Water – Components of the Environment” Conference Proceedings, Cluj-Napoca, Romania, p. 237-246, 2019. DOI: 10.24193/AWC2019_24.
  • [16] Üneş, F., Bölük, O., Kaya, Y. Z., Taşar, B., &Varçin, H. (2018). Estimation of Rainfall-Runoff Relationship Using Artificial Neural Network Models for Muskegon Basin. International Journal of Advanced Engineering Research and Science (ISSN : 2349-6495(P) | 2456-1908(O)),5(12), 198-205.
  • [17] Kaya, Y.Z., Üneş, F., Demirci, M., Tasar, B., & Varcin, H. Groundwater Level Prediction Using Artificial Neural Network and M5 Tree Models, Air and Water Components of the Environment Conference, 2018. DOI: 10.24193/AWC2018_23
  • [18] Demirci, M., Taşar, B., Kaya, Y. Z, & Varçin, H. Estimation of Groundwater Level Fluctuations Using Neuro-Fuzzy and Support Vector Regression Models. International Journal of Advanced Engineering Research and Science 2018, (ISSN : 2349-6495(P) | 2456-1908(O)),5(12), 206-212. http://dx.doi.org/10.22161/ijaers.5.12.29
  • [19] Demirci, M., Unes, F., Kaya, Y. Z., Mamak, M., Tasar, B., & Ispir, E. Estimation of groundwater level using artificial neural networks: a case study of Hatay-Turkey. In 10th International Conference „Environmental Engineering “. 2017.
  • [20] Demirci, M., Üneş, F., & Körlü, S. (2019) Modeling of groundwater level using artificial intelligence techniques: a case study of Reyhanlı region in Turkey. Applied Ecology and Env. Research, 2019, 17(2):2651-2663. http://dx.doi.org/10.15666/aeer/1702_26512663
  • [21] Kaya, Y. Z., Üneş, F., Demirci, M., Taşar, B., & Varçin, H. Groundwater Level Prediction Using Artificial Neural Network and M5 Tree Models. Aerul si Apa. Componente ale Mediului, 195-201. 2018.
  • [22] Üneş, F., Demirci, M., Mertcan, Z., Taşar, B., Varçin, H., Ziya, Y. Determination of Groundwater Level Fluctuations by Artificial Neural Networks. Natural and Engineering Sciences, 2018, 3(3), Supplement, 35-42.
  • [23] Üneş, F, Maruf, A.G., & Taşar, B. Ground Water Level Estimation for Dörtyol region in HATAY. International Journal of Environment, Agriculture and Biotechnology, 2019, 4(3).
  • [24] Demirci, M., & Unes, F. “Generalized Regression Neural Networks For Reservoir Level Modeling”, International Journal of Advanced Computational Engineering and Networking, 2015, 3, 81-84.
  • [25] Üneş, F., Demirci, M., Taşar, B., Kaya, Y.Z., & Varçin H. Estimating Dam Reservoir Level Fluctuations Using Data-Driven Techniques. Pol. J. Environ. Stud. 2019, Vol. 28, No. 5, 1-12. DOI: 10.15244/pjoes/93923
  • [26] Üneş, F., Demirci, M., Taşar, B., Kaya, Y.Z., & Varçin, H. Modeling of dam reservoir volume using generalized regression neural network, support vector machines and M5 decision tree models. Applied Ecology and Environmental Research. 2019, 17(3), 7043-7055.
  • [27] Demirci, M., Üneş, F., Kaya, Y.Z., Tasar, B., & Varcin, H. Modeling of Dam Reservoir Volume Using Adaptive Neuro Fuzzy Method, Air and Water Components of the Environment Conference, 2018, DOI: 10.24193/AWC2018_18.
  • [28] Unes, F. Prediction of Dam Reservoir Volume Fluctuations Using Adaptive Neuro Fuzzy Approach. EJENS, 2017,2(1), 144-148.
  • [29] Zadeh, L. A. Fuzzy sets. Information and control, 1965, 8(3), 338-353.
  • [30] Lee, C. C. Fuzzy logic in control systems: fuzzy logic controller. II. IEEE Transactions on systems, man, and cybernetics, 1990, 20(2), 419-435.
  • [31] Jung, C. H., Ham, C. S., & Lee, K. I. (1995). A real-time self-tuning fuzzy controller through scaling factor adjustment for the steam generator of NPP. Fuzzy sets and systems, 74(1), 53-60.
  • [32] Ünsal, S., & Alişkan, İ. Performance analysis of fuzzy logic controllers having Mamdani and Takagi-Sugeno inference methods by using unique software and toolbox. In 2016 National Conference on Electrical, Electronics and Biomedical Engineering (ELECO) 2016, (pp. 237-241). IEEE.
  • [33] Mamdani, E.H. and S. Assilian, "An experiment in linguistic synthesis with a fuzzy logic controller," International Journal of Man-Machine Studies, , 1975 Vol. 7, No. 1, pp. 1-13
  • [34] Sugeno, M., Industrial applications of fuzzy control, Elsevier Science Pub. Co., 1985.
  • [35] Hamam, A., & Georganas, N. D. A comparison of Mamdani and Sugeno fuzzy inference systems for evaluating the quality of experience of Hapto-Audio-Visual applications. In 2008 IEEE International Workshop on Haptic Audio visual Environments and Games (pp. 87-92). IEEE. 2008, October

Year 2019, Volume: 2 Issue: 1, 23 - 29, 30.12.2019

Kübra Özdülkar Fatih Üneş Mustafa Demirci Yunus Ziya Kaya

Abstract

References

  • [1] Jensen, M. E. Consumptive use of water and irrigation water requirements. ASCE. 1974.
  • [2] Frevert, D. K., Hill, R. W., & Braaten, B. C. Estimation of FAOevapotranspiration coefficients. Journal of Irrigation and Drainage Engineering 1983, 109(2), 265-270.
  • [3] Irmak, S., Haman, D. Z., & Jones, J. W. Evaluation of class A pan coefficients for estimating reference evapotranspiration in humid location. Journal of Irrigation and Drainage Engineering, 2002, 128(3), 153-159.
  • [4] Stephens, J. C., & Stewart, E. H. A comparison of procedures for computing evaporation and evapotranspiration. Publication, 1963, 62, 123-133.
  • [5] Burman, R. D. Intercontinental comparison of evaporation estimates. Journal of the Irrigation and Drainage Division, 1976, 102(1), 109-118.
  • [6] Dos Reis, R. J., & Dias, N. L. Multi-season lake evaporation: energy-budget estimates and CRLE model assessment with limited meteorological observations. Journal of Hydrology, 1998, 208(3-4), 135-147.
  • [7] Vallet-Coulomb, C., Legesse, D., Gasse, F., Travi, Y., & Chernet, T. Lake evaporation estimates in tropical Africa (Lake Ziway, Ethiopia). Journal of hydrology, 2001, 245(1-4), 1-18.
  • [8] Gavin, H., & Agnew, C. A. Modelling actual, reference and equilibrium evaporation from a temperate wet grassland. Hydrological Processes, 2004, 18(2), 229-246
  • [9] Sudheer, K. P., Gosain, A. K., Mohana Rangan, D., & Saheb, S. M. Modelling evaporation using an artificial neural network algorithm. Hydrological Processes, 2002, 16(16), 3189-3202.
  • [10] Taşar, B., Üneş, F., Demirci, M., & Kaya, Y. Z. Yapay sinir ağları yöntemi kullanılarak buharlaşma miktarı tahmini. DÜMF Mühendislik Dergisi, 2018, 9(1), 543-551. [11] Üneş, F., Doğan, S., Taşar, B., Kaya, Y., Demirci, M. The Evaluation and Comparison of Daily Reference Evapotranspiration with ANN and Empirical Methods. Natural and Engineering Sciences, 2018 3(3), Supplement, 54-64.
  • [12] Kaya, Y.Z., Taşar, B. Evapotranspiration Calculation for South Carolina, USA and Creation Different ANFIS Models for ET Estimation. 2019 ”Air and Water – Components of the Environment” Conference Proceedings, Cluj-Napoca, Romania, p. 217-224, 2019. DOI: 10.24193/AWC2019_22.
  • [13] Demirci, M., Üneş, F., & Saydemir, S. Suspended sediment estimation using an artificial intelligence approach. In: Sediment matters. Eds. P. Heininger, J. Cullmann. Springer International Publishing p. 83–95. 2015.
  • [14] Tasar, B., Kaya, Y. Z., Varcin, H., Üneş, F., & Demirci, M. Forecasting of Suspended Sediment in Rivers Using Artificial Neural Networks Approach, International Journal of Advanced Engineering Research and Science (IJAERS), 4(12), pp. 79-84. 2017. [15] Tașar, B., Unes, F., Varcin, H. Prediction of the Rainfall – Runoff Relationship Using Neuro-Fuzzy and Support Vector Machines. 2019 ”Air and Water – Components of the Environment” Conference Proceedings, Cluj-Napoca, Romania, p. 237-246, 2019. DOI: 10.24193/AWC2019_24.
  • [16] Üneş, F., Bölük, O., Kaya, Y. Z., Taşar, B., &Varçin, H. (2018). Estimation of Rainfall-Runoff Relationship Using Artificial Neural Network Models for Muskegon Basin. International Journal of Advanced Engineering Research and Science (ISSN : 2349-6495(P) | 2456-1908(O)),5(12), 198-205.
  • [17] Kaya, Y.Z., Üneş, F., Demirci, M., Tasar, B., & Varcin, H. Groundwater Level Prediction Using Artificial Neural Network and M5 Tree Models, Air and Water Components of the Environment Conference, 2018. DOI: 10.24193/AWC2018_23
  • [18] Demirci, M., Taşar, B., Kaya, Y. Z, & Varçin, H. Estimation of Groundwater Level Fluctuations Using Neuro-Fuzzy and Support Vector Regression Models. International Journal of Advanced Engineering Research and Science 2018, (ISSN : 2349-6495(P) | 2456-1908(O)),5(12), 206-212. http://dx.doi.org/10.22161/ijaers.5.12.29
  • [19] Demirci, M., Unes, F., Kaya, Y. Z., Mamak, M., Tasar, B., & Ispir, E. Estimation of groundwater level using artificial neural networks: a case study of Hatay-Turkey. In 10th International Conference „Environmental Engineering “. 2017.
  • [20] Demirci, M., Üneş, F., & Körlü, S. (2019) Modeling of groundwater level using artificial intelligence techniques: a case study of Reyhanlı region in Turkey. Applied Ecology and Env. Research, 2019, 17(2):2651-2663. http://dx.doi.org/10.15666/aeer/1702_26512663
  • [21] Kaya, Y. Z., Üneş, F., Demirci, M., Taşar, B., & Varçin, H. Groundwater Level Prediction Using Artificial Neural Network and M5 Tree Models. Aerul si Apa. Componente ale Mediului, 195-201. 2018.
  • [22] Üneş, F., Demirci, M., Mertcan, Z., Taşar, B., Varçin, H., Ziya, Y. Determination of Groundwater Level Fluctuations by Artificial Neural Networks. Natural and Engineering Sciences, 2018, 3(3), Supplement, 35-42.
  • [23] Üneş, F, Maruf, A.G., & Taşar, B. Ground Water Level Estimation for Dörtyol region in HATAY. International Journal of Environment, Agriculture and Biotechnology, 2019, 4(3).
  • [24] Demirci, M., & Unes, F. “Generalized Regression Neural Networks For Reservoir Level Modeling”, International Journal of Advanced Computational Engineering and Networking, 2015, 3, 81-84.
  • [25] Üneş, F., Demirci, M., Taşar, B., Kaya, Y.Z., & Varçin H. Estimating Dam Reservoir Level Fluctuations Using Data-Driven Techniques. Pol. J. Environ. Stud. 2019, Vol. 28, No. 5, 1-12. DOI: 10.15244/pjoes/93923
  • [26] Üneş, F., Demirci, M., Taşar, B., Kaya, Y.Z., & Varçin, H. Modeling of dam reservoir volume using generalized regression neural network, support vector machines and M5 decision tree models. Applied Ecology and Environmental Research. 2019, 17(3), 7043-7055.
  • [27] Demirci, M., Üneş, F., Kaya, Y.Z., Tasar, B., & Varcin, H. Modeling of Dam Reservoir Volume Using Adaptive Neuro Fuzzy Method, Air and Water Components of the Environment Conference, 2018, DOI: 10.24193/AWC2018_18.
  • [28] Unes, F. Prediction of Dam Reservoir Volume Fluctuations Using Adaptive Neuro Fuzzy Approach. EJENS, 2017,2(1), 144-148.
  • [29] Zadeh, L. A. Fuzzy sets. Information and control, 1965, 8(3), 338-353.
  • [30] Lee, C. C. Fuzzy logic in control systems: fuzzy logic controller. II. IEEE Transactions on systems, man, and cybernetics, 1990, 20(2), 419-435.
  • [31] Jung, C. H., Ham, C. S., & Lee, K. I. (1995). A real-time self-tuning fuzzy controller through scaling factor adjustment for the steam generator of NPP. Fuzzy sets and systems, 74(1), 53-60.
  • [32] Ünsal, S., & Alişkan, İ. Performance analysis of fuzzy logic controllers having Mamdani and Takagi-Sugeno inference methods by using unique software and toolbox. In 2016 National Conference on Electrical, Electronics and Biomedical Engineering (ELECO) 2016, (pp. 237-241). IEEE.
  • [33] Mamdani, E.H. and S. Assilian, "An experiment in linguistic synthesis with a fuzzy logic controller," International Journal of Man-Machine Studies, , 1975 Vol. 7, No. 1, pp. 1-13
  • [34] Sugeno, M., Industrial applications of fuzzy control, Elsevier Science Pub. Co., 1985.
  • [35] Hamam, A., & Georganas, N. D. A comparison of Mamdani and Sugeno fuzzy inference systems for evaluating the quality of experience of Hapto-Audio-Visual applications. In 2008 IEEE International Workshop on Haptic Audio visual Environments and Games (pp. 87-92). IEEE. 2008, October
There are 33 citations in total.

Details

Primary Language Turkish
Subjects Civil Engineering
Journal Section Article
Authors

Kübra Özdülkar

Fatih Üneş

Mustafa Demirci

Yunus Ziya Kaya

Publication Date December 30, 2019
Submission Date December 5, 2019
Acceptance Date December 10, 2019
Published in Issue Year 2019 Volume: 2 Issue: 1

Cite

APA Özdülkar, K., Üneş, F., Demirci, M., Kaya, Y. Z. (2019). Günlük Buharlaşma Miktarının Bulanık Mantık Yöntemleri Kullanılarak Bölgesel Olarak Modellenmesi. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 2(1), 23-29.
AMA Özdülkar K, Üneş F, Demirci M, Kaya YZ. Günlük Buharlaşma Miktarının Bulanık Mantık Yöntemleri Kullanılarak Bölgesel Olarak Modellenmesi. Osmaniye Korkut Ata University Journal of Natural and Applied Sciences. December 2019;2(1):23-29.
Chicago Özdülkar, Kübra, Fatih Üneş, Mustafa Demirci, and Yunus Ziya Kaya. “Günlük Buharlaşma Miktarının Bulanık Mantık Yöntemleri Kullanılarak Bölgesel Olarak Modellenmesi”. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 2, no. 1 (December 2019): 23-29.
EndNote Özdülkar K, Üneş F, Demirci M, Kaya YZ (December 1, 2019) Günlük Buharlaşma Miktarının Bulanık Mantık Yöntemleri Kullanılarak Bölgesel Olarak Modellenmesi. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 2 1 23–29.
IEEE K. Özdülkar, F. Üneş, M. Demirci, and Y. Z. Kaya, “Günlük Buharlaşma Miktarının Bulanık Mantık Yöntemleri Kullanılarak Bölgesel Olarak Modellenmesi”, Osmaniye Korkut Ata University Journal of Natural and Applied Sciences, vol. 2, no. 1, pp. 23–29, 2019.
ISNAD Özdülkar, Kübra et al. “Günlük Buharlaşma Miktarının Bulanık Mantık Yöntemleri Kullanılarak Bölgesel Olarak Modellenmesi”. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi 2/1 (December 2019), 23-29.
JAMA Özdülkar K, Üneş F, Demirci M, Kaya YZ. Günlük Buharlaşma Miktarının Bulanık Mantık Yöntemleri Kullanılarak Bölgesel Olarak Modellenmesi. Osmaniye Korkut Ata University Journal of Natural and Applied Sciences. 2019;2:23–29.
MLA Özdülkar, Kübra et al. “Günlük Buharlaşma Miktarının Bulanık Mantık Yöntemleri Kullanılarak Bölgesel Olarak Modellenmesi”. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, vol. 2, no. 1, 2019, pp. 23-29.
Vancouver Özdülkar K, Üneş F, Demirci M, Kaya YZ. Günlük Buharlaşma Miktarının Bulanık Mantık Yöntemleri Kullanılarak Bölgesel Olarak Modellenmesi. Osmaniye Korkut Ata University Journal of Natural and Applied Sciences. 2019;2(1):23-9.

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