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Intelligent Control and Automation pp 309–318Cite as

Modeling of Rainfall-Runoff Relationship at the Semi-arid Small Catchments Using Artificial Neural Networks

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Part of the book series: Lecture Notes in Control and Information Sciences ((LNCIS,volume 344))

Abstract

The artificial neural networks (ANNs) have been applied to various hydrologic problems in recently. In this paper, the artificial neural network (ANN) model is employed in the application of rainfall-runoff process on a semi-arid catchment, namely the Kurukavak catchment. The Kurukavak catchment, a sub-basin of the Sakarya basin in NW Turkey, has a drainage area of 4.25 km2. The performance of the developed neural network based model was compared with multiple linear regression based model using the same observed data. It was found that the neural network model consistently gives good predictions. The conclusion is drawn that the ANN model can be used for prediction of flow for small semi-arid catchments.

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Author information

Authors and Affiliations

  1. Engineering Faculty of Civil Engineering Department, Anadolu University, Eskişehir, Turkey

    Mustafa Tombul

  2. III.Regional Directorate of State Hydraulic Work, Eskişehir, Turkey

    Ersin Oğul

Authors
  1. Mustafa Tombul
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  2. Ersin Oğul
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Editor information

Editors and Affiliations

  1. Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, Anhui, China

    De-Shuang Huang

  2. Queen’s University, Belfast, UK

    Kang Li  & George William Irwin  & 

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© 2006 Springer-Verlag Berlin Heidelberg

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Tombul, M., Oğul, E. (2006). Modeling of Rainfall-Runoff Relationship at the Semi-arid Small Catchments Using Artificial Neural Networks. In: Huang, DS., Li, K., Irwin, G.W. (eds) Intelligent Control and Automation. Lecture Notes in Control and Information Sciences, vol 344. Springer, Berlin, Heidelberg . https://doi.org/10.1007/978-3-540-37256-1_38

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  • DOI: https://doi.org/10.1007/978-3-540-37256-1_38

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-37255-4

  • Online ISBN: 978-3-540-37256-1

  • eBook Packages: EngineeringEngineering (R0)

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