Skip to main content
SpringerLink
Log in

Prediction of monthly discharge volume by different artificial neural network algorithms in semi-arid regions

  • Original Paper
  • Published:
Arabian Journal of Geosciences Aims and scope Submit manuscript

Abstract

Prediction of monthly discharge volume is important for reservoir management and evaluation of drinking-water supplies. Also, it is very essential in arid and semi-arid regions due to the lack of observed data. This study compared four artificial neural network (ANN) algorithms to predict the monthly discharge volume from Idenak Watershed in Kohkiloye Boier Ahmad Province in southwestern Iran. These algorithms, including resilient backpropagation (ANN_RP), scaled conjugate gradient (ANN_SCG), variable learning rate (ANN_GDX), and Levenberg–Marquardt (ANN_LM), were applied to monthly discharge volume data. The transfer function employed was the tangent sigmoid, and input vectors were constructed in different ways during the algorithm development. The algorithms were trained and tested using a 36-year data record (432 monthly values) selected randomly. Comparison of the algorithms showed that ANN_SCG performed better than the other algorithms, where the values of R 2 and root mean square errors during validation were 0.78 and 63 million cubic meters. Furthermore, the input vector consisting of precipitation [P(t)], antecedent precipitation [P(t − 1)], and antecedent monthly discharge volume with one time lag [V(t− 1)] was superior to the other input vectors for monthly discharge volume prediction. Generally, the proposed models are capable for prediction of monthly discharge volume in arid and semi-arid regions.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Cigizoglu HK, Kisi O (2005) Flow prediction by three back propagation techniques using k-fold partitioning of neural network training data. Nord Hydrol 36(1):49–64

    Google Scholar 

  • De Martonne E (1926) Une nouvelle fonction climatologique: L’indice d’aridité. La Meteorologie 2:449–458

    Google Scholar 

  • Hosseinzadeh Talaee P, Heydari M, Fathi P, Marofi S, Tabari H (2011) Numerical model and computational intelligence approaches for estimating flow through rockfill dam. J Hydrol Eng ASCE. doi:10.1061/(ASCE)HE.1943-5584.0000446

  • Hsu KL, Gupta HV, Sorooshian S (1995) Artificial neural network modeling of rainfall-runoff process. Water Resour Res 31(10):2517–2530

    Article  Google Scholar 

  • Kalman BL, Kwasny SC (1992) Why Tanh: choosing a sigmoidal function. In: Proceedings of the International Joint Conference on Neural Networks, Baltimore, vol. 4, pp. 578–581

  • Kisi O (2004) River flow modeling using artificial neural networks. J Hydrol Eng ASCE 9(1):60–63

    Article  Google Scholar 

  • Kisi O (2007) Streamflow forecasting using different artificial neural network algorithms. J Hydrol Eng 12(5):532–539

    Article  Google Scholar 

  • Kisi O (2008) Stream flow forecasting using neuro-wavelet technique. Hydrol Process 22(20):4142–4152

    Article  Google Scholar 

  • Kisi O (2009) Neural network and wavelet conjunction model for modeling monthly level fluctuations in Turkey. Hydrol Process 23(14):2081–2092

    Article  Google Scholar 

  • Kumar APS, Sudheer KP, Jain SK, Agarwal PK (2005) Rainfall-runoff modeling using artificial neural networks: comparison of network types. Hydrol Process 19:1277–1291

    Article  Google Scholar 

  • Maier HR, Dandy GC (1998) The effect of internal parameters and geometry on the performance of back-propagation neural networks: an empirical study. Environ Model Softw 13(2):193–209

    Article  Google Scholar 

  • Marofi S, Tabari H, Zare Abyaneh H (2011) Predicting spatial distribution of snow water equivalent using multivariate non-linear regression and computational intelligence methods. Water Resour Manag 25:1417–1435

    Article  Google Scholar 

  • McMahon TA (1979) Hydrological characteristics of arid zones. Proceedings of a Symposium on the Hydrology of Areas of Low Precipitation, vol 128. IAHS, Canberra, pp 105–123

    Google Scholar 

  • Moller M (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6(4):525–533

    Article  Google Scholar 

  • More JJ (1977) The Levenberg–Marquardt algorithm: implementation and theory, numerical analysis. In: Watson GA (ed) Lecture notes in mathematics, 630th edn. Springer, New York, pp 105–116

    Google Scholar 

  • Nemec J, Rodier JA (1979) Streamflow characteristics in areas of low precipitation. Proceedings of a Symposium on the Hydrology of Areas of Low Precipitation, vol 128. IAHS, Canberra, pp 125–140

    Google Scholar 

  • Pilgrim DH, Chapman TG, Doran DG (1988) Problems of rainfall-runoff modelling in arid and semi-arid regions. Hydrol Sci J 33(4):379–400

    Article  Google Scholar 

  • Rezaeian Zadeh M, Abghari H, van de Giesen N, Nikian A, Niknia N (2009) Maximum daily discharge volume prediction using multi layer perceptron network. Geophys Res Abstr 11:3652–3662, EGU General Assembly 2009

    Google Scholar 

  • Rezaeian Zadeh M, Amin S, Khalili D, Singh VP (2010) Daily outflow prediction by multi layer perceptron with logistic sigmoid and tangent sigmoid activation functions. Water Resour Manag 24(11):2673–2688. doi:10.1007/s11269-009-9573-4

    Article  Google Scholar 

  • Riedmiller M, Braun H (1993) A direct adaptive method for faster back propagation learning: the RPROP algorithm’. Proc. IEEE Int. Joint Conf. on Neural Networks, San Francisco, CA, USA, 586–591

  • Shamseldin AY (1997) Application of neural network technique to rainfall-runoff modeling. J Hydrol 199(3–4):272–294

    Article  Google Scholar 

  • Smith M (1993) Neural networks for statistical modeling. Wiley, New York

    Google Scholar 

  • Sudheer KP, Gosain AK, Ramasastri KS (2002) A data driven algorithm for constructing artificial neural network rainfall-runoff models. Hydrol Process 16(6):1325–1330

    Article  Google Scholar 

  • Tabari H, Marofi S, Sabziparvar AA (2010a) Estimation of daily pan evaporation using artificial neural network and multivariate non-linear regression. Irrig Sci 28:399–406

    Article  Google Scholar 

  • Tabari H, Marofi S, Zare Abyaneh H, Sharifi MR (2010b) Comparison of artificial neural network and combined models in estimating spatial distribution of snow depth and snow water equivalent in Samsami basin of Iran. Neural Comput App 19:625–635

    Article  Google Scholar 

  • Tabari H, Sabziparvar AA, Ahmadi M (2011) Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region. Meteorog Atmos Phys 110:135–142

    Article  Google Scholar 

  • Tokar AS, Johnson A (1999) Rainfall-runoff modeling using artificial neural networks. J Hydrol Eng ASCE 4(3):232–239

    Article  Google Scholar 

  • Tokar AS, Markus M (2000) Precipitation-runoff modeling using artificial neural networks and conceptual models. J Hydrol Eng ASCE 5(2):156–161

    Article  Google Scholar 

  • Wang W, Van Gelder PHAJM, Vrijling JK, Ma J (2006) Forecasting daily streamflow using hybrid ANN models. J Hydrol 324(1–2):383–399

    Article  Google Scholar 

  • Wu Jy S, Han J, Annambhotla S, Bryant S (2005) Artificial neural networks for forecasting watershed runoff and stream flows. J Hydrol Eng ASCE 10(3):216–222

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Young Researchers Club, Shiraz Branch, Islamic Azad University, Shiraz, Iran

    Mehdi Rezaeian-Zadeh

  2. Department of Water Engineering, Ayatollah Amoli Branch, Islamic Azad University, Amol, Iran

    Hossein Tabari

  3. Department of Watershed Management, Faculty of Natural Resources, Urmia University, Urmia, Iran

    Hirad Abghari

Authors
  1. Mehdi Rezaeian-Zadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hossein Tabari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hirad Abghari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mehdi Rezaeian-Zadeh.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Rezaeian-Zadeh, M., Tabari, H. & Abghari, H. Prediction of monthly discharge volume by different artificial neural network algorithms in semi-arid regions. Arab J Geosci 6, 2529–2537 (2013). https://doi.org/10.1007/s12517-011-0517-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12517-011-0517-y

Keyword

Search

Navigation