Yıl: 2005 Cilt: 29 Sayı: 1 Sayfa Aralığı: 9 - 20 Metin Dili: İngilizce İndeks Tarihi: 29-07-2022

Daily river flow forecasting using artificial neural networks and auto-regressive models

Öz:
Estimating the flows of rivers can have a significant economic impact, as this can help in agricultural water management and in providing protection from water shortages and possible flood damage. This paper provides forecasting benchmarks for river flow prediction in the form of a numerical and graphical comparison between neural networks and auto-regressive (AR) models. Benchmarking was based on 7 and 4-year periods of continuous river flow data for 2 rivers in the USA, the Blackwater River and the Gila River, and a 2-year period of streamflow data for the Filyos Stream in Turkey. The choice of appropriate artificial neural network (ANN) architectures for hydrological forecasting, in terms of hidden layers and nodes, was investigated. Three simple neural network (NN) architectures were then selected for comparison with the AR model forecasts. Sum of square errors (SSEs) and correlation statistic measures were used to evaluate the models' performances. The benchmark results showed that NNs were able to produce better results than AR models when given the same data inputs.
Anahtar Kelime:

Konular: Çevre Mühendisliği
Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
  • 1.Chang, F-J. and Chen, Y-C, "A Counterpropagation Fuzzy-Neural Network Modeling Approach to Real Time Streamflow Prediction". J. of Hydrology, 245, 153-164, 2001.
  • 2.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, 2005 (in press).
  • 3.Coulibaly, P., Anctil, F. and Bobe'e, B., "Pre'vision hydrologique par re'seaux de neurones artificiels: e'tat de Tart" Can. J. Civil Eng., 26, 293-304, 1999.
  • 4.Cybenko, G., "Approximation by superposition of a sigmoidal function", Math. Control Signals Syst., 2, 303-314, 1989.
  • 5.Duan, D., Fermini, B. and Natteli, S., "Sustained Outward Current Observed after Itol Inactivation in Rabbit Atrial Myocytes Is a Novel $Cl^-$ current", Am. J. Physiol. 263: H1967-1971, 1992.
  • 6.Hornik, K., Stinchcombe, M. and White, H., "Multilayer Feedforward Networks Are Universal Approximators", Neural Networks 2, 359-366, 1989. 7.Hsu, K., Gupta, H.V. and Sorooshian, S., "Artificial Neural Network Modeling of the Rainfall-Runoff Process", Water. Resour. Res., 31, 2517-2530, 1995.
  • 8.Jain, S.K., Das, D. and Srivastava, D.K., "Application of ANN for Reservoir Inflow Prediction and Operation", J. Water Resour. Planning Mgmt. ASCE, 125, 263-271, 1999.
  • 9.Karunanithi, N., Grenney, W.J., Whitley, D. and Bovee, K., "Neural Networks for River Flow Prediction", J. Comp. Civil Eng., ASCE 8, 201-220, 1994.
  • 10.Kişi,Ö.,. "River Flow Modeling Using Artificial Neural Networks", ASCE J. of Hydrologic Engineering, 9, 1, 60-63, 2004.
  • 11.Kitanidis, P.K. and Bras, R.L., "Adaptive Filtering Through Detection of Isolated Transient Errors in Rainfall-Runoff Models", Water Resour. Res., 16, 740-748, 1980.
  • 12.Kitanidis, P.K. and Bras, R.L., "Real-Time Forecasting With a Conceptual Hydrological Model", Water Resour. Res., 16, 740-748, 1980.
  • 13.Raman, H. and Sunilkumar, N., "Multivariate Modelling of Water Resources Time Series Using Artificial Neural Networks", J. Hydrol. Sci., 40, 145-163, 1995.
  • 14.Rumelhart, D.E., Hinton, G.E. and Williams, R.J., "Learning internal representation by error propagation", In: Rumelhart, D.E. and McClelland, J.L. (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, 1. MIT Press, Cambridge, MA, 318-362, 1986.
  • 15.Saad, M., Bigras, P., Turgeon, A. and Duquette, R., "Fuzzy Learning Decomposition for the Scheduling of Hydroelectric Power Systems", Water Resour. Res., 32, 179-186, 1996.
  • 16.Shamseldin, A.Y., "Application of a Neural Network Technique to Rainfall-Runoff Modelling", J. Hydrol., 199, 272-294, 1997.
  • 17.Sivakumar, B., Jayawardena, A.W. and Fernando, T.M.K.G., "River Flow Forecasting: Use of Phase Space Reconstruction and Artificial Neural Networks Approaches", J. of Hydrology, 265, 225-245, 2002. 18.Smith, J. and Eli, R.N., "Neural Network Models of Rainfall-Runoff Process", J. Wat. Resour. Plan. Mgmt., 499-508, 1995.
  • 19.Sorooshian, S., Daun, Q. and Gupta, V.K., "Calibration of Rainfall-Runoff Models: Application of Global Optimization to the Sacramento Soil Moisture Accounting Model", Water Resour. Res., 29, 1185-1194, 1993.
  • 20.Tokar, A.S. and Johnson, P.A., "Rainfall-Runoff Modeling Using Artificial Neural Networks", J. Hydrol. Eng., ASCE 4, 232-239, 1999.
  • 21.Yapo, P., Gupta, V.K. and Sorooshian, S., "Calibration of Conceptual Rainfall-Runoff Models: Sensitivity to Calibration Data", J. Hydrol., 181, 23-48, 1996.
  • 22.Zealand, CM., Burn, D.H. and Simonovic, S.P., "Short-Term Streamflow Forecasting Using Artificial Neural Networks", J. Hydrol., 214, 32-48, 1999.
  • 23.Zhang, G., Patuwo, B.E. and Hu, M.Y., "Forecasting With Artificial Neural Networks: The State of the Art. Int. J. Forecasting, 14, 35-62, 1998.
APA KİŞİ Ö (2005). Daily river flow forecasting using artificial neural networks and auto-regressive models. , 9 - 20.
Chicago KİŞİ Özgür Daily river flow forecasting using artificial neural networks and auto-regressive models. (2005): 9 - 20.
MLA KİŞİ Özgür Daily river flow forecasting using artificial neural networks and auto-regressive models. , 2005, ss.9 - 20.
AMA KİŞİ Ö Daily river flow forecasting using artificial neural networks and auto-regressive models. . 2005; 9 - 20.
Vancouver KİŞİ Ö Daily river flow forecasting using artificial neural networks and auto-regressive models. . 2005; 9 - 20.
IEEE KİŞİ Ö "Daily river flow forecasting using artificial neural networks and auto-regressive models." , ss.9 - 20, 2005.
ISNAD KİŞİ, Özgür. "Daily river flow forecasting using artificial neural networks and auto-regressive models". (2005), 9-20.
APA KİŞİ Ö (2005). Daily river flow forecasting using artificial neural networks and auto-regressive models. Turkish Journal of Engineering and Environmental Sciences, 29(1), 9 - 20.
Chicago KİŞİ Özgür Daily river flow forecasting using artificial neural networks and auto-regressive models. Turkish Journal of Engineering and Environmental Sciences 29, no.1 (2005): 9 - 20.
MLA KİŞİ Özgür Daily river flow forecasting using artificial neural networks and auto-regressive models. Turkish Journal of Engineering and Environmental Sciences, vol.29, no.1, 2005, ss.9 - 20.
AMA KİŞİ Ö Daily river flow forecasting using artificial neural networks and auto-regressive models. Turkish Journal of Engineering and Environmental Sciences. 2005; 29(1): 9 - 20.
Vancouver KİŞİ Ö Daily river flow forecasting using artificial neural networks and auto-regressive models. Turkish Journal of Engineering and Environmental Sciences. 2005; 29(1): 9 - 20.
IEEE KİŞİ Ö "Daily river flow forecasting using artificial neural networks and auto-regressive models." Turkish Journal of Engineering and Environmental Sciences, 29, ss.9 - 20, 2005.
ISNAD KİŞİ, Özgür. "Daily river flow forecasting using artificial neural networks and auto-regressive models". Turkish Journal of Engineering and Environmental Sciences 29/1 (2005), 9-20.