Skip to main content
SpringerLink
Log in

Forecasting Daily Streamflow Discharges Using Various Neural Network Models and Training Algorithms

  • Water Resources and Hydrologic Engineering
  • Published:
KSCE Journal of Civil Engineering Aims and scope

Abstract

Streamflow forecasting based on past records is an important issue in both hydrologic engineering and hydropower reservoir management. In the study, three artificial Neural Network (NN) models, namely NN with well-known multi-layer perceptron (MLPNN), NN with principal component analyses (PCA-NN), and NN with time lagged recurrent (TLR-NN), were used to 1, 3, 5, 7, and 14 ahead of daily streamflow forecast. Daily flow discharges of Haldizen River, located in the Eastern Black Sea Region, Turkey the time period of 1998–2009 was used to forecast discharges. Backpropagation (BP), Conjugate Gradient (CG), and Levenberg-Marquardt (LM) were applied to the models as training algorithm. The result demonstrated that, firstly, the forecast ability of CG algorithm much better than BP and LM algorithms in the models; secondly, the best performance was obtained by PCA-NN and MLP-NN for short time (1, 3, and 5 day-ahead) forecast and TLR-NN for long time (7 and 14 day-ahead) forecast.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Abdi, H. and Williams, L. J. (2010). “Principal component analysis.” Wiley Interdisciplinary Reviews: Computational Statistics, Vol. 2, No. 4, pp. 433–459, DOI: 10.1002/wics.101.

    Article  Google Scholar 

  • Adeli, H. and Hung, S. L. (1994). “Machine learning neural networks genetic algorithms and fuzzy systems.” John Wiley & Sons Inc., New York, N.Y.

    MATH  Google Scholar 

  • Bayram, A., Kankal, M., Tayfur, G., and Önsoy, H. (2014). “Prediction of suspended sediment concentration from water quality variables.” Neural Computing and Applications, Vol. 24, No. 5, pp. 1079–1087. DOI: 10.1007/s00521-012-1333-3.

    Article  Google Scholar 

  • Cancelliere, A., Giuliano, G., Ancarani, A., and Rossi, G. (2002). “A neural networks approach for deriving irrigation reservoir operating rules.” Water Resources Management, Vol. 16, No. 1, pp. 71–88, DOI: 10.1023/A:1015563820136.

    Article  Google Scholar 

  • Cigizoglu, H. K. and Kisi, Ö. (2005). “Flow prediction by three back propagation techniques using k-fold partitioning of neural network training data.” Hydrology Research, Vol. 36, No. 1, pp. 49–64.

    Article  Google Scholar 

  • Cigizoglu, H. K. and Kisi, Ö. (2006). “Methods to improve the neural network performance in suspended sediment estimation.” Journal of hydrology, Vol. 317, No. 3, pp. 221–238, DOI: 10.1016/j.jhydrol.2005.05.019.

    Article  Google Scholar 

  • Coulibaly, P., Anctil, F., and Bobee, B. (2000). “Daily reservoir inflow forecasting using artificial neural networks with stopped training approach.” Journal of Hydrology, Vol. 230, No. 3, pp. 244–257, DOI: 10.1016/S0022-1694(00)00214-6.

    Article  Google Scholar 

  • Elfattah, M. A., El-Bendary, N., Elsoud, M. A. A., Hassanien, A. E., and Tolba, M. F. (2013). “An intelligent approach for galaxies images classification.” In Hybrid Intelligent Systems (HIS), 2013 13th International Conference on (pp. 167-172). IEEE.

    Google Scholar 

  • Govindaraju, R. S. and Rao, A. R. (Eds.). (2013). “Artificial neural networks in hydrology.” Vol. 36. Springer Science & Business Media.

  • Goyal, M. K., Ojha, C. S. P., Singh, R. D., and Swamee, P. K. (2013). “Application of artificial neural network, fuzzy logic and decision tree algorithms for modelling of streamflow at Kasol in India.” Water Science Technology, Vol. 68, No. 12, DOI: 10.2166/wst.2013.491.

    Google Scholar 

  • Helena, B., Pardo, R., Vega, M., Barrado, E., Fernandez, J. M., and Fernandez, L. (2000). “Temporal evolution of groundwater composition in an alluvial aquifer (Pisuerga River, Spain) by principal component analysis.” Water Research, Vol. 34, No. 3, pp. 807–816, DOI: 10.1016/S0043-1354(99)00225-0.

    Article  Google Scholar 

  • Huang, W., Xu, B., and Chan-Hilton, A. (2004). “Forecasting flows in Apalachicola River using neural networks.” Hydrological Processes, Vol. 18, No. 13, pp. 2545–2564, DOI: 10.1002/hyp.1492.

    Article  Google Scholar 

  • Hotelling, H. (1933). “Analysis of a complex of statistical variables into principal components.” J. Educ Psychol, Vol. 25, pp. 417–441, DOI: 10.1037/h0071325.

    Article  MATH  Google Scholar 

  • Imrie, C. E., Durucan, S., and Korre, A. (2000). “River flow prediction using artificial neural networks: Generalisation beyond the calibration range.” Journal of Hydrology, Vol. 233, No. 1, pp. 138–153, DOI: 10.1016/S0022-1694(00)00228-6.

    Article  Google Scholar 

  • Kang, J. Y. and Song, J. H. (1998). “Neural network applications in determining the fatigue crack opening load.” International Journal of Fatigue, Vol. 20, No. 1, pp. 57–69, DOI: 10.1016/S0142-1123(97) 00119–9.

    Article  Google Scholar 

  • Karasu S. (2010). “The effect of daylight saving time options on electricity consumption of Turkey.” Energy, Vol. 35, No. 37, pp. 73–82, DOI: 10.1016/j.energy.2010.05.027.

    Google Scholar 

  • Khoshnevisan, B., Rafiee, S., Omid, M., Yousefi, M., and Movahedi, M. (2013). “Modeling of energy consumption and GHG (greenhouse gas) emissions in wheat production in Esfahan province of Iran using artificial neural networks.” Energy, Vol. 52, No. 33, pp. 3–8, DOI: 10.1016/j.energy.2013.01.028.

    Google Scholar 

  • Kisi, Ö. (2004). “Multi-layer perceptrons with Levenberg-Marquardt training algorithm for suspended sediment concentration prediction and estimation.” Hydrological Sciences Journal, Vol. 49, No. 6, DOI: 10.1623/hysj.49.6.1025.55720.

    Google Scholar 

  • Kisi, Ö. (2004). “River flow modeling using artificial neural networks.” Journal of Hydrologic Engineering, Vol. 9, No. 1, pp. 60–63, DOI: 10.1061/(ASCE)1084-0699(2004)9:1(60).

    Article  Google Scholar 

  • Kisi, Ö. (2007). “Evapotranspiration modelling from climatic data using a neural computing technique.” Hydrological Processes, Vol. 21, No. 14, pp. 1925–1934, DOI: 10.1002/hyp.6403.

    Article  Google Scholar 

  • Kisi, Ö. (2007). “Streamflow forecasting using different artificial neural network algorithms.” Journal of Hydrologic Engineering, Vol. 12, No. 5, pp. 532–539, DOI: 10.1061/(ASCE)1084-0699(2007)12:5(532).

    Article  Google Scholar 

  • Kisi, Ö. (2008). “River flow forecasting and estimation using different artificial neural network techniques.” Hydrology Research, Vol. 39, No. 1, pp. 27–40, DOI: 10.2166/nh.2008.026.

    Article  Google Scholar 

  • Kisi, Ö. (2009). “Neural networks and wavelet conjunction model for intermittent streamflow forecasting.” Journal of Hydrologic Engineering, Vol. 14, No. 8, pp. 773–782, DOI: 10.1061/(ASCE)HE.1943-5584.0000053.

    Article  Google Scholar 

  • Kisi, Ö. and Uncuoglu, E. (2005). “Comparison of the three backpropagation training algorithms for two case studies.” Indian J Eng Mater Sci, Vol. 12, No. 5, pp. 434–442.

    Google Scholar 

  • Kisi, Ö., Özkan, C., and Akay, B. (2012). “Modeling discharge–sediment relationship using neural networks with artificial bee colony algorithm.” Journal of Hydrology, Vol. 428, pp. 94–103, DOI: 10.1016/j.jhydrol.2012.01.026.

    Article  Google Scholar 

  • Kote, A. S. and Jothiprakash, V. (2008). “Reservoir inflow prediction using time lagged recurrent neural networks.” In Emerging Trends in Engineering and Technology, 2008. ICETET'08. First International Conference on, pp. 618–623. IEEE.

    Chapter  Google Scholar 

  • Minns, A. W. and Hall, M. J. (1996). “Artificial neural networks as rainfall-runoff models.” Hydrological Sciences Journal, Vol. 41, No. 3, pp. 399–417, DOI: 10.1080/02626669609491511.

    Article  Google Scholar 

  • Neuro Solutions v5.0, Neuro Solutions Getting Started Manual, https://doi.org/www.neurosolutions.com/(2005).

  • Noori, R., Khakpour, A., Omidvar, B., and Farokhnia, A. (2010). “Comparison of ANN and principal component analysis-multivariate linear regression models for predicting the river flow based on developed discrepancy ratio statistic.” Expert Systems with Applications, Vol. 37, No. 8, pp. 5856–5862, DOI: 10.1016/j.eswa.2010.02.020.

    Article  Google Scholar 

  • Noori, R., Farokhnia, A., Morid, S., and Riahi Madvar, H. (2009). “Effect of input variables preprocessing in artificial neural network on monthly flow prediction by PCA and wavelet transformation.” J. of Water and Wastewater, Vol. 1, pp. 13–22.

    Google Scholar 

  • Pearson, K. (1901). “On lines and planes of closest fit to systems of points in space.” Philos Mag A, Vol. 6, pp. 559–572, DOI: 10.1080/14786440109462720.

    Article  MATH  Google Scholar 

  • Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986). “Learning representations by back-propagating errors.” Nature, Vol. 323, No. 6088, pp. 533–538.

    Article  MATH  Google Scholar 

  • Sanikhani, H. and Kisi, Ö. (2012). “River flow estimation and forecasting by using two different adaptive neuro-fuzzy approaches.” Water Resources Management, Vol. 26, No. 6, pp. 1715–1729, DOI: 10.1007/s11269-012-9982-7.

    Article  Google Scholar 

  • Sattari, M. T., Yurekli, K., and Pal, M. (2012). “Performance evaluation of artificial neural network approaches in forecasting reservoir inflow.” Applied Mathematical Modelling, Vol. 36, No. 6, pp. 2649–2657, DOI: 10.1016/j.apm.2011.09.048.

    Article  Google Scholar 

  • Shiri, J. and Kisi, Ö. (2010). “Short-term and long-term streamflow forecasting using a wavelet and neuro-fuzzy conjunction model.” Journal of Hydrology, Vol. 394, No. 3, pp. 486–493, DOI: 10.1016/j.jhydrol.2010.10.008.

    Article  Google Scholar 

  • Shiri, J., Kisi, Ö., Makarynskyy, O., Shiri, A. A., and Nikoofar, B. (2012). “Forecasting daily stream flows using artificial intelligence approaches.” ISH Journal of Hydraulic Engineering, Vol. 18, No. 3, pp. 204–214, DOI: 10.1080/09715010.2012.721189.

    Article  Google Scholar 

  • Smith, J. and Eli, R. N. (1995). “Neural-network models of rainfall-runoff process.” Journal of Water Resources Planning and Management, Vol. 121, No. 6, pp. 499–508, DOI: 10.1061/(ASCE)0733-9496 (1995)121:6(499).

    Article  Google Scholar 

  • Supharatid, S. (2003). “Application of a neural network model in establishing a stage–discharge relationship for a tidal river.” Hydrological Processes, Vol. 17, No. 15, pp. 3085–3099, DOI: 10.1002/hyp.1278.

    Article  Google Scholar 

  • Tabachnick, B. G. and Fidell, L. S. (1989). Using multivariate statistics (2nd ed.) New York, NY: Harper & Row.

    Google Scholar 

  • Tfwala, S. S. and Wang, Y. M. (2016). “Estimating sediment discharge using sediment rating curves and artificial neural networks in the Shiwen River.” Taiwan. Water, Vol. 8, No. 2, pp. 53, DOI: 10.3390/w8020053.

    Google Scholar 

  • Thirumalaiah, K. and Deo, M. C. (1998). “River stage forecasting using artificial neural networks.” Journal of Hydrologic Engineering, Vol. 3, No. 1, pp. 26–32, DOI: 10.1061/(ASCE)1084-0699(1998)3:1(26).

    Article  Google Scholar 

  • Uzlu, E., Akpinar, A., and Kömürcü, M. I. (2011). “Restructuring of Turkey’s electricity market and the share of hydropower energy: The case of the Eastern Black Sea Basin.” Renewable Energy, Vol. 36, No. 2, pp. 676–688, DOI: 10.1016/j.renene.2010.08.012.

    Article  Google Scholar 

  • Uzlu, E., Akpinar, A., Özturk, H. T., Nacar, S., and Kankal, M. (2014). “Estimates of hydroelectric generation using neural networks with the artificial bee colony algorithm for Turkey.” Energy, Vol. 69, pp. 638–647, DOI: 10.1016/j.energy.2014.03.059.

    Article  Google Scholar 

  • Wang, W., Van Gelder, P. H., Vrijling, J. K., and Ma, J. (2006). “Forecasting daily streamflow using hybrid ANN models.” Journal of Hydrology, Vol. 324, No. 1, pp. 383–399, DOI: 10.1016/j.jhydrol.2005.09.032.

    Article  Google Scholar 

  • Wang, Y. M. and Traore, S. (2009). “Time-lagged recurrent network for forecasting episodic event suspended sediment load in typhoon prone area.” International Journal of Physical Sciences, Vol. 4, No. 9, pp. 519–528.

    Google Scholar 

  • Wasserman, P. D. (1993). Advanced methods in neural computing. Van Nostrand Reinhold, New York, N.Y.

    MATH  Google Scholar 

  • Yüksek, Ö., Kankal, M., and Üçüncü, O. (2013). “Assessment of big floods in the Eastern Black Sea Basin of Turkey.” Environmental Monitoring and Assessment, Vol. 185, No. 1, pp. 797–814, DOI: 10.1007/s10661-012-2592-2.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Civil Engineering Dept., Karadeniz Technical University, Trabzon, 61080, Turkey

    Sinan Nacar & Murat Kankal

  2. Civil Engineering Dept., Aksaray University, Aksaray, 68100, Turkey

    M. Ali Hınıs

Authors
  1. Sinan Nacar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. M. Ali Hınıs
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Murat Kankal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sinan Nacar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nacar, S., Hınıs, M.A. & Kankal, M. Forecasting Daily Streamflow Discharges Using Various Neural Network Models and Training Algorithms. KSCE J Civ Eng 22, 3676–3685 (2018). https://doi.org/10.1007/s12205-017-1933-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12205-017-1933-7

Keywords

Search

Navigation