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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)

Abstract

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.

Keywords

Artificial Neural Networks inflow estimation dam catchment area 

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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

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