Application of Artificial Neural Networks for Inflow Estimation of Yuvacık Dam Catchment Area

  • Bahattin Yanık
  • Melih Inal
  • Erhan Butun
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 91)


Inflow data for longer length at a reservoir site is necessary for proper planning and operation of the reservoir. However presently for most of the reservoirs, the measured length of inflow data is insufficient for use in planning and operation. Artificial neural networks (ANNs) have been applied within the field of hydrological modeling for over a decade but relatively little attention has been paid to the use of these tools for flood estimation in catchments. Modeling of non-linearity and uncertainty associated with rainfall-runoff process has received a lot of attention in the past years. We analyzed the potential of neural network models for the estimation of inflow for Yuvacik Dam Catchment. Multilayer feed-forward neural networks were developed to model the relationships between known rain, snow depth and temperature data. Results suggest that artificial neural network model can be simple, robust, reliable and a cost-efficient tool for environmental inflow determination at the catchment area.


Artificial Neural Networks inflow estimation dam catchment area 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Alcázar, J., Palau, A., Vega-Garcí, C.: A neural net model for environmental flow estimation at the Ebro River Basin, Spain. Journal of Hydrology 349, 44–55 (2008)CrossRefGoogle Scholar
  2. 2.
    Remesan, R., Shamim, M.A., Han, D., Mathew, J.: ANFIS and NNARX based Rainfall-Runoff Modeling. In: IEEE International Conference on Systems, Man and Cybernetics, pp. 1454–1459 (2008)Google Scholar
  3. 3.
    Partal, T., Kerem Cigizoglu, H.: Estimation and forecasting of daily suspended sediment data using wavelet–neural networks. Journal of Hydrology 358, 317–331 (2008)CrossRefGoogle Scholar
  4. 4.
    Kentel, E.: Estimation of river flow by artificial neural networks and identification of input vectors susceptible to producing unreliable flow estimates. Journal of Hydrology 375, 481–488 (2009)CrossRefGoogle Scholar
  5. 5.
    Dawson, C.W., Abrahart, R.J., Shamseldin, A.Y., Wilby, R.L.: Flood estimation at ungauged sites using artificial neural networks. Journal of Hydrology 319, 391–409 (2006)CrossRefGoogle Scholar
  6. 6.
    Blazkova, S., Beven, K.: Flood frequency estimation by continuous simulation of subcatchment rainfalls and discharges with the aim of improving dam safety assessment in a large basin in the Czech Republic. Journal of Hydrology 292, 153–172 (2004)CrossRefGoogle Scholar
  7. 7.
    Guohua, H., Xing, Y., Hehua, S.: Medium-Long Term Forecast of the Annual Maximum Peak Discharge at the Xiangjiang River Basin on Fuzzy Method. In: International Forum on Information Technology and Applications, pp. 200–204 (2009)Google Scholar
  8. 8.
    Kote, A., Jothiprakash, V.: Reservoir Inflow Prediction Using Time Lagged Recurrent Neural Networks. In: First International Conference on Emerging Trends in Engineering and Technology, pp. 618–623. IEEE, Los Alamitos (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Bahattin Yanık
    • 1
  • Melih Inal
    • 2
  • Erhan Butun
    • 3
  1. 1.ISUKocaeli Water and Seewage AdministrationTurkey
  2. 2.Technical Education FacultyKocaeli UniversityTurkey
  3. 3.Civil Aviation CollegeKocaeli UniversityTurkey

Personalised recommendations