Rainfall-runoff modelling using three neural network methods

Cigizoglu H., Alp M.

ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING - ICAISC 2004, cilt.3070, ss.166-171, 2004 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 3070
  • Basım Tarihi: 2004
  • Dergi Adı: ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING - ICAISC 2004
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED)
  • Sayfa Sayıları: ss.166-171
  • İstanbul Teknik Üniversitesi Adresli: Hayır

Özet

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. It was seen that all three different ANN algorithms compared well with conventional multi linear regression (MLR) technique. It was seen that only GRNN technique did not provide negative flow estimations for some observations. The rainfall-runoff correlogram was successfully used in determination of the input layer node number.