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DAILY RUNOFF MODELLING OF YIGITLER STREAM BY USING ARTIFICIAL NEURAL NETWORKS AND REGRESSION ANALYSIS

Year 2010, Issue: 023, 33 - 48, 15.12.2010

Umut Okkan , Ayşe Mollamahmutoğlu

Abstract

It is very
important to make reliable runoff estimations and runoff modeling studies when
planning and designing of water resources. In the study presented, an
artificial neural network (ANN) model was established to estimate daily runoff
of Yigitler Stream in Gediz basin. The ANN model was also compared with multi-linear
regression model structures. Performances of each model were examined with
observed daily runoff values of Yigitler Stream. After analysis, it was noticed
that the artificial neural network algorithm is more successful than the regression
model 

Keywords

Daily Runoff estimation Artificial Neural Networks Regression Analysis Yigitler River

References

  • [1] Abbott, M.B. and Refsgaard, J.C. “Distributed Hydrological Modelling” Kluver Academic Publishers,Dordrecht. 17-39 (1996).
  • [2] Gül, A. ve Harmancıoğlu, N. “Su Kaynakları Yönetiminde Bilgisayar Modellerinin Kullanımı” I. Ulusal Su Mühendisliği Sempozyumu Bildiriler Kitabı, 735-745 (2003).
  • [3] Perrin, C., Michel, C. and Andreassian, V. “Does a large number of parameters enhance model performance?. Comparative assessment of common catchment model structures on 429 catchments”. Journal of Hydrology. 242, 275-301 (2001).
  • [4] İçağa, Y. “Akarçay Havzası Yağış-Akış İlişkilerinin Modellenmes” I. Ulusal Su Mühendisliği Sempozyumu, 22-26 Eylül, İzmir, Türkiye, 203-214 (2003).
  • [5] Karabörk, M.Ç., ve Kahya, E.”Sakarya havzasındaki aylık akımların çok değişkenli stokastik modellemesi” Turkish J. Eng. Env. Sci., 23(2), 133-147 (1999).
  • [6] Keskin, E.M. ve Taylan D.E. “Orta Akdeniz Havzasındaki Akımların Stokastik Modellemesi” İMO Teknik Dergi, 282, 4271-4291 (2007).
  • [7] Alp, M. ve Cığızoğlu, K. “Yapay Sinir Ağı Metodları ve Regresyon Analizi ile Akım Tahmini” II. Ulusal Su Mühendisliği Sempozyumu Bildiriler Kitabı, 589-598 (2005).
  • [8] Raman, H. and Sunilkumar, N. “Multivariate modelling of water resources time series using artificial neural networks” Hydrological Sciences Journal, 40(2), 145-163 (1995).
  • [9] Cığızoğlu, H.K.”Incorporation of ARMA models into flow forecasting by artificial neural networks” Environmetrics, 14(4), 417-427 (2003).
  • [10]Tokar, A.S. and Johnson, P.A., “Rainfall runoff modelling using artificial neural networks” Journal of Hydrologic Engineering, 4(3), 232-239 (1999).
  • [11]Campolo, M., Andreussi, P. and Soldati, A., “River flood forecasting with a neural network model” Water Resources Research, 35, 1191-1197 (1999).
  • [12] Hsu, K., Gupta, H.V. and Sorooshian, S., “Artificial neural network modelling of the rainfall runoff process” Water Res. Research, 31, 2517-2530 (1995).
  • [13] Minns, A.W. and Hall, M.J., “Artificial neural networks as rainfall runoff models” Hydrological Sciences Journal, 41(3), 399-417 (1996).
  • [14] Alp, M. ve Cığızoğlu, H.K., “Farklı yapay sinir ağı metodları ile yağış-akış ilişkisinin modellenmesi” İTU dergisi, 3(1), 80-88 (2004).
  • [15]Cigizoglu H.K. “Application of the Generalized Regression Neural Networks to Intermittent Flow Forecasting and Estimation”, Journal of Hydrologic Engineering, 10(4), 336-341 (2005a).
  • [16]Cigizoglu H.K. “Generalized regression neural networks in monthly flow forecasting” Civil Engineering and Environmental Systems. 22 (2), 71-84 (2005b).
  • [17] Fernando, D.A.K., and Jayawardena, A.W., “Runoff forecasting using RBF networks with OLS algorithm” Journal of Hydrologic Engineering 3(3), 203-209 (1998).
  • [18] Lin, G., and Chen, L., “ A non-linear rainfall-runoff model using radial basis function network”, Journal of Hydrology, 289, 1-8 (2004).
  • [19] Skapura, D. M. “Building Neural Networks” Addison-Wesley, New York (1996).
  • [20] Haykin, S. ”Neural Networks: A Comprehensive Foundation” MacMillan. New York (1994)
  • [21] Öztemel, E., “Yapay Sinir Ağları” Papatya Yayıncılık. İstanbul (2003).
  • [22] Rumelhart D.E., Hinton G.E., Williams R.J. “Learning representations by backpropagation errors” Nature, 323, 533-536 (1986).
  • [23]Kisi, O.,”Multi-layer perceptrons with Levenberg–Marquardt training algorithm for suspended sediment concentration prediction and estimation” Hydrological Sciences Journal 49 (6), 1025–1040 (2004).
  • [24]Cigizoglu, H.K., and Kisi, O., “ Flow prediction by three back propagation techniques using k-fold partitioning of neural network training data” Nordic Hydrology 36 (1), 49–64, (2005).
  • [25] Okkan, U. ve Dalkılıç, H. Y.” Demirköprü barajı aylık buharlaşma yüksekliklerinin yapay sinir ağları ile tahmin edilmesi” DSİ Teknik Bülten. 108, 30-36 (2010).
  • [26] Marquardt, D., “An algorithm for least squares estimation of non-linear parameters “Journal of the Society for Industrial and Applied Mathematics 11 (2), 431–441 (1963).
  • [27] Hagan M. T. and Menhaj M. B. “Training feed forward network with the Marquardt algorithm” IEEE Trans. on Neural Net., 5(6), 989-993 (1994).
  • [28] Cong Chen T., D. Jian Han, F. T. K. Au, L. G. Than. “Acceleration of Levenberg-Marquardt training of neural networks with variable decay rate,” IEEE Trans. on Neural Net., 3(6), 1873-1878 (2003).
  • [29] Lindley, D.V. “Regression and correlation analysis”. New Palgrave: A Dictionary of Economics, 4, 120-23 (1987).
  • [30] Hocking, R. R. “The Analysis and Selection of Variables in Linear Regression” Biometrics, (1976).

YİĞİTLER ÇAYI GÜNLÜK AKIMLARININ YAPAY SİNİR AĞLARI VE REGRESYON ANALİZİ İLE MODELLENMESİ

Year 2010, Issue: 023, 33 - 48, 15.12.2010

Umut Okkan , Ayşe Mollamahmutoğlu

Abstract

Su kaynaklarının planlanması ve projelendirilmesi
aşamasında, güvenilir akım tahminlerinin ve akım modelleme çalışmalarının
yapılması büyük önem taşımaktadır. Sunulan çalışmada, Gediz havzasında yer alan
Yiğitler Çayına ait günlük akımların modellenmesi için kullanılabilecek bir
yapay sinir ağı modeli (YSA) hazırlanmıştır. Hazırlanan YSA modeli çoklu
doğrusal regresyon modeli ile karşılaştırılmış, model performansları, Yiğitler
Çayına ait ölçülmüş günlük akım değerleri ile sınanmıştır. Analiz sonucu, yapay
sinir ağı algoritması performansı regresyon modeline göre daha başarılı
bulunmuştur. 

Keywords

Günlük akım tahmini yapay sinir ağları regresyon analizi Yiğitler Çayı

References

  • [1] Abbott, M.B. and Refsgaard, J.C. “Distributed Hydrological Modelling” Kluver Academic Publishers,Dordrecht. 17-39 (1996).
  • [2] Gül, A. ve Harmancıoğlu, N. “Su Kaynakları Yönetiminde Bilgisayar Modellerinin Kullanımı” I. Ulusal Su Mühendisliği Sempozyumu Bildiriler Kitabı, 735-745 (2003).
  • [3] Perrin, C., Michel, C. and Andreassian, V. “Does a large number of parameters enhance model performance?. Comparative assessment of common catchment model structures on 429 catchments”. Journal of Hydrology. 242, 275-301 (2001).
  • [4] İçağa, Y. “Akarçay Havzası Yağış-Akış İlişkilerinin Modellenmes” I. Ulusal Su Mühendisliği Sempozyumu, 22-26 Eylül, İzmir, Türkiye, 203-214 (2003).
  • [5] Karabörk, M.Ç., ve Kahya, E.”Sakarya havzasındaki aylık akımların çok değişkenli stokastik modellemesi” Turkish J. Eng. Env. Sci., 23(2), 133-147 (1999).
  • [6] Keskin, E.M. ve Taylan D.E. “Orta Akdeniz Havzasındaki Akımların Stokastik Modellemesi” İMO Teknik Dergi, 282, 4271-4291 (2007).
  • [7] Alp, M. ve Cığızoğlu, K. “Yapay Sinir Ağı Metodları ve Regresyon Analizi ile Akım Tahmini” II. Ulusal Su Mühendisliği Sempozyumu Bildiriler Kitabı, 589-598 (2005).
  • [8] Raman, H. and Sunilkumar, N. “Multivariate modelling of water resources time series using artificial neural networks” Hydrological Sciences Journal, 40(2), 145-163 (1995).
  • [9] Cığızoğlu, H.K.”Incorporation of ARMA models into flow forecasting by artificial neural networks” Environmetrics, 14(4), 417-427 (2003).
  • [10]Tokar, A.S. and Johnson, P.A., “Rainfall runoff modelling using artificial neural networks” Journal of Hydrologic Engineering, 4(3), 232-239 (1999).
  • [11]Campolo, M., Andreussi, P. and Soldati, A., “River flood forecasting with a neural network model” Water Resources Research, 35, 1191-1197 (1999).
  • [12] Hsu, K., Gupta, H.V. and Sorooshian, S., “Artificial neural network modelling of the rainfall runoff process” Water Res. Research, 31, 2517-2530 (1995).
  • [13] Minns, A.W. and Hall, M.J., “Artificial neural networks as rainfall runoff models” Hydrological Sciences Journal, 41(3), 399-417 (1996).
  • [14] Alp, M. ve Cığızoğlu, H.K., “Farklı yapay sinir ağı metodları ile yağış-akış ilişkisinin modellenmesi” İTU dergisi, 3(1), 80-88 (2004).
  • [15]Cigizoglu H.K. “Application of the Generalized Regression Neural Networks to Intermittent Flow Forecasting and Estimation”, Journal of Hydrologic Engineering, 10(4), 336-341 (2005a).
  • [16]Cigizoglu H.K. “Generalized regression neural networks in monthly flow forecasting” Civil Engineering and Environmental Systems. 22 (2), 71-84 (2005b).
  • [17] Fernando, D.A.K., and Jayawardena, A.W., “Runoff forecasting using RBF networks with OLS algorithm” Journal of Hydrologic Engineering 3(3), 203-209 (1998).
  • [18] Lin, G., and Chen, L., “ A non-linear rainfall-runoff model using radial basis function network”, Journal of Hydrology, 289, 1-8 (2004).
  • [19] Skapura, D. M. “Building Neural Networks” Addison-Wesley, New York (1996).
  • [20] Haykin, S. ”Neural Networks: A Comprehensive Foundation” MacMillan. New York (1994)
  • [21] Öztemel, E., “Yapay Sinir Ağları” Papatya Yayıncılık. İstanbul (2003).
  • [22] Rumelhart D.E., Hinton G.E., Williams R.J. “Learning representations by backpropagation errors” Nature, 323, 533-536 (1986).
  • [23]Kisi, O.,”Multi-layer perceptrons with Levenberg–Marquardt training algorithm for suspended sediment concentration prediction and estimation” Hydrological Sciences Journal 49 (6), 1025–1040 (2004).
  • [24]Cigizoglu, H.K., and Kisi, O., “ Flow prediction by three back propagation techniques using k-fold partitioning of neural network training data” Nordic Hydrology 36 (1), 49–64, (2005).
  • [25] Okkan, U. ve Dalkılıç, H. Y.” Demirköprü barajı aylık buharlaşma yüksekliklerinin yapay sinir ağları ile tahmin edilmesi” DSİ Teknik Bülten. 108, 30-36 (2010).
  • [26] Marquardt, D., “An algorithm for least squares estimation of non-linear parameters “Journal of the Society for Industrial and Applied Mathematics 11 (2), 431–441 (1963).
  • [27] Hagan M. T. and Menhaj M. B. “Training feed forward network with the Marquardt algorithm” IEEE Trans. on Neural Net., 5(6), 989-993 (1994).
  • [28] Cong Chen T., D. Jian Han, F. T. K. Au, L. G. Than. “Acceleration of Levenberg-Marquardt training of neural networks with variable decay rate,” IEEE Trans. on Neural Net., 3(6), 1873-1878 (2003).
  • [29] Lindley, D.V. “Regression and correlation analysis”. New Palgrave: A Dictionary of Economics, 4, 120-23 (1987).
  • [30] Hocking, R. R. “The Analysis and Selection of Variables in Linear Regression” Biometrics, (1976).
There are 30 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Umut Okkan

Ayşe Mollamahmutoğlu This is me

Publication Date December 15, 2010
Published in Issue Year 2010 Issue: 023

Cite

APA Okkan, U., & Mollamahmutoğlu, A. (2010). YİĞİTLER ÇAYI GÜNLÜK AKIMLARININ YAPAY SİNİR AĞLARI VE REGRESYON ANALİZİ İLE MODELLENMESİ. Journal of Science and Technology of Dumlupınar University(023), 33-48.
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