Skip to main content
Article thumbnail
Location of Repository

ARTIFICIAL NEURAL NETWORK MODELS IN RAINFALL-RUNOFF MODELLING OF TURKISH RIVERS H.Kerem CIĞIZOĞLU 1*

By Pınar Aşkin, Ahmet Öztürk, Atilla Gürbüz, Mehmet Yildiz, İsmail Uçar and Maslak Istanbul-turkey

Abstract

Three neural network methods, feed forward back propagation (FFBP), radial basis function (RBF) and generalized regression neural network (GRNN) were employed for rainfall-runoff modelling of Turkish hydrometeorologic data. The daily rainfall and daily mean flow data are coupled to form the basis of rainfall-flow modelling using different ANN configurations. It was seen that all three different ANN algorithms compared well with conventional multi linear regression (MLR) technique. The peak flows of the observed hydrographs were closer approximated by FFBP and RBF algorithms. It was seen that only GRNN technique did not provide negative flow estimations for some observations. The rainfall-flow correlogram was successfully used in order to determine the input layer structure of the ANN configurations

Topics: neural network, rainfall-flow modelling, RIVER BASIN FLOOD MANAGEMENT 561
Year: 2009
OAI identifier: oai:CiteSeerX.psu:10.1.1.134.5577
Provided by: CiteSeerX
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://citeseerx.ist.psu.edu/v... (external link)
  • http://www.dsi.gov.tr/english/... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.