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GEN İFADE PROGRAMLAMA İLE GÖKSU NEHRİ’NİN AKIM TAHMİNİ

Year 2017, , 483 - 488, 18.12.2017
https://doi.org/10.21923/jesd.330479

Abstract

Bu çalışmada, Gen İfade Programlama (GEP) yöntemi kullanılarak Doğu
Akdeniz havzasında bulunan Göksu Nehri’nin akım tahmini yapılmıştır. Akım
tahmini için Göksu Nehri’nde bulunan Bucakkışla, Karahacılı, Kırkkavak, Hamam,
Yeşilköy ve Gravga akım gözlem istasyonlarından 2006–2010 yıllarına ait günlük
akım değerleri kullanılarak modeller geliştirilmiştir. Karahacılı istasyonuna
ait modeller geliştirilirken diğer istasyonların akım değerleri ve Karahacılı
istasyonunun önceki günlerine ait akım değerleri girdi kullanılarak çeşitli
kombinasyonlar denenmiştir. Modellerin performansları değerlendirildiğinde,
akım tahmininde GEP yönteminin başarılı sonuçlar verdiği görülmüştür.

References

  • Aytek, A., Kisi, O. 2008. A genetic programming approach to suspended sediment modelling. Journal of Hydrology, 351(3), 288-298.
  • Azamathulla, H. M., Ghani, A. A., Leow, C. S., Chang, C. K., Zakaria, N. A. 2011. Gene-expression programming for the development of a stage-discharge curve of the Pahang River. Water resources management, 25(11), 2901-2916.
  • Fernando, A. K., Shamseldin, A. Y., Abrahart, R. J. 2011. Use of gene expression programming for multimodel combination of rainfall-runoff models. Journal of Hydrologic Engineering, 17(9), 975-985.
  • Ferreira, C. 2001. Gene expression programming: A new adaptive algorithm for solving problems. Complex Systems 13(2), 87–129.
  • Ferreira, C. 2006. Gene-expression programming: Mathematical modeling by an artificial intelligence. Springer-Verlag Berlin Heidelberg.
  • Goldberg, D.E. 1989. Genetic algorithms in search, optimization and machine learning. Addison-Wesley.
  • Göksu (Kilikya). http://tr.wikipedia.org/ wiki/Göksu_(Kilikya) (Erişim Tarihi: 02.04.2017)
  • Grosan, C., Abraham, A. 2006. Evolving computer programs for knowledge discovery. International Journal of System Management 4(2), 7–24.
  • Kisi, O., Shiri, J., Tombul, M. 2013. Modeling rainfall-runoff process using soft computing techniques. Computers & Geosciences, 51, 108-117.
  • Koza, J.R. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection, A Bradford Book The MIT Press Cambridge, Massachusetts London, England.
  • Nourani, V., Komasi, M., Alami, M. T. 2011. Hybrid wavelet–genetic programming approach to optimize ANN modeling of rainfall–runoff process. Journal of Hydrologic Engineering, 17(6), 724-741.
  • Shiri, J., Kisi, Ö. 2011. Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations. Computers & Geosciences, 37(10), 1692-1701.
  • Shiri, J., Sadraddini, A. A., Nazemi, A. H., Kisi, O., Landeras, G., Fard, A. F., Marti, P. 2014. Generalizability of Gene Expression Programming-based approaches for estimating daily reference evapotranspiration in coastal stations of Iran. Journal of hydrology, 508, 1-11.
  • Shoaib, M., Shamseldin, A. Y., Melville, B. W., Khan, M. M. 2015. Runoff forecasting using hybrid wavelet gene expression programming (WGEP) approach. Journal of Hydrology, 527, 326-344.
  • Traore, S., Guven, A. 2013. New algebraic formulations of evapotranspiration extracted from gene-expression programming in the tropical seasonally dry regions of West Africa. Irrigation science, 31(1), 1-10.
  • Yılmaz, M.U. 2014. Performans Ağırlıklı Yöntemlerle Aylık Akımların Tahmini: Orta Fırat Havzası Uygulaması. İTÜ Fen Bilimleri Enstitüsü YL Tezi, 89 s.

STREAMFLOW ESTIMATION OF GÖKSU RIVER WITH GENE EXPRESSION PROGRAMMING

Year 2017, , 483 - 488, 18.12.2017
https://doi.org/10.21923/jesd.330479

Abstract











In this study, Gene Expression Programming (GEP)
technique was used to forecast the river flow of Göksu River, located in Doğu
Akdeniz Basin. Various models were developed using the daily river flow data of
Bucakkışla, Karahacılı, Kırkkavak, Hamam, Yeşilköy and Gravga stations, located
on Göksu River, for the period of 2006-2010. While the models of Karahacılı
station were developed, several combinations were tried using the flow data of
other stations and also the previous flow data of Karahacılı station as input.
When the performances of the models are evaluated, it is seen that GEP is a
successful method in the forecast of river flow.

References

  • Aytek, A., Kisi, O. 2008. A genetic programming approach to suspended sediment modelling. Journal of Hydrology, 351(3), 288-298.
  • Azamathulla, H. M., Ghani, A. A., Leow, C. S., Chang, C. K., Zakaria, N. A. 2011. Gene-expression programming for the development of a stage-discharge curve of the Pahang River. Water resources management, 25(11), 2901-2916.
  • Fernando, A. K., Shamseldin, A. Y., Abrahart, R. J. 2011. Use of gene expression programming for multimodel combination of rainfall-runoff models. Journal of Hydrologic Engineering, 17(9), 975-985.
  • Ferreira, C. 2001. Gene expression programming: A new adaptive algorithm for solving problems. Complex Systems 13(2), 87–129.
  • Ferreira, C. 2006. Gene-expression programming: Mathematical modeling by an artificial intelligence. Springer-Verlag Berlin Heidelberg.
  • Goldberg, D.E. 1989. Genetic algorithms in search, optimization and machine learning. Addison-Wesley.
  • Göksu (Kilikya). http://tr.wikipedia.org/ wiki/Göksu_(Kilikya) (Erişim Tarihi: 02.04.2017)
  • Grosan, C., Abraham, A. 2006. Evolving computer programs for knowledge discovery. International Journal of System Management 4(2), 7–24.
  • Kisi, O., Shiri, J., Tombul, M. 2013. Modeling rainfall-runoff process using soft computing techniques. Computers & Geosciences, 51, 108-117.
  • Koza, J.R. 1992. Genetic Programming: On the Programming of Computers by Means of Natural Selection, A Bradford Book The MIT Press Cambridge, Massachusetts London, England.
  • Nourani, V., Komasi, M., Alami, M. T. 2011. Hybrid wavelet–genetic programming approach to optimize ANN modeling of rainfall–runoff process. Journal of Hydrologic Engineering, 17(6), 724-741.
  • Shiri, J., Kisi, Ö. 2011. Comparison of genetic programming with neuro-fuzzy systems for predicting short-term water table depth fluctuations. Computers & Geosciences, 37(10), 1692-1701.
  • Shiri, J., Sadraddini, A. A., Nazemi, A. H., Kisi, O., Landeras, G., Fard, A. F., Marti, P. 2014. Generalizability of Gene Expression Programming-based approaches for estimating daily reference evapotranspiration in coastal stations of Iran. Journal of hydrology, 508, 1-11.
  • Shoaib, M., Shamseldin, A. Y., Melville, B. W., Khan, M. M. 2015. Runoff forecasting using hybrid wavelet gene expression programming (WGEP) approach. Journal of Hydrology, 527, 326-344.
  • Traore, S., Guven, A. 2013. New algebraic formulations of evapotranspiration extracted from gene-expression programming in the tropical seasonally dry regions of West Africa. Irrigation science, 31(1), 1-10.
  • Yılmaz, M.U. 2014. Performans Ağırlıklı Yöntemlerle Aylık Akımların Tahmini: Orta Fırat Havzası Uygulaması. İTÜ Fen Bilimleri Enstitüsü YL Tezi, 89 s.
There are 16 citations in total.

Details

Subjects Engineering
Journal Section Research Articles
Authors

Özlem Terzi 0000-0001-6429-5176

Onur Özcanoğlu 0000-0001-6221-4918

Publication Date December 18, 2017
Submission Date July 24, 2017
Acceptance Date September 12, 2017
Published in Issue Year 2017

Cite

APA Terzi, Ö., & Özcanoğlu, O. (2017). GEN İFADE PROGRAMLAMA İLE GÖKSU NEHRİ’NİN AKIM TAHMİNİ. Mühendislik Bilimleri Ve Tasarım Dergisi, 5(3), 483-488. https://doi.org/10.21923/jesd.330479