Flow prediction by three back propagation techniques using k-fold partitioning of neural network training data

Cigizoglu H., Kisi O.

NORDIC HYDROLOGY, vol.36, no.1, pp.49-64, 2005 (Journal Indexed in SCI) identifier

  • Publication Type: Article / Article
  • Volume: 36 Issue: 1
  • Publication Date: 2005
  • Title of Journal : NORDIC HYDROLOGY
  • Page Numbers: pp.49-64


Flow forecasting performance by artificial neural networks (ANNs) is generally considered to be dependent on the data length. In this study k-fold partitioning, a statistical method, was employed in the ANN training stage. The method was found useful in the case of using the conventional feed-forward back propagation algorithm. It was shown that with a data period much shorter than the whole training duration similar flow prediction performance could be obtained. Prediction performance and convergence velocity comparison between three different back propagation algorithms, Levenberg-Marquardt, conjugate gradient and gradient descent was the next concern of the study and the Levenberg-Marquardt technique was found advantageous thanks to its shorter training duration and more satisfactory performance criteria.