Prediction of maximum annual flood discharges using artificial neural network approaches


ANILAN T., NACAR S., KANKAL M. , YÜKSEK Ö.

GRADEVINAR, vol.72, no.3, pp.215-224, 2020 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 72 Issue: 3
  • Publication Date: 2020
  • Doi Number: 10.14256/jce.2316.2018
  • Title of Journal : GRADEVINAR
  • Page Numbers: pp.215-224
  • Keywords: artificial neural networks, principal component analysis, maximum annual flows, PRINCIPAL COMPONENT ANALYSIS, L-MOMENTS APPROACH, FREQUENCY-ANALYSIS, INDEX-FLOOD, FEEDFORWARD NETWORKS, STREAMFLOW, BASIN, CLASSIFICATION, RAINFALL, QUALITY

Abstract

The applicability of artificial neural network (ANN) approaches for estimation of maximum annual flows is investigated in the paper. The performance of three neural network models is compared: multi layer perceptron neural networks (MLP_NN), generalized feed forward neural networks (GFF_NN), and principal component analysis with neural networks (PCA_ NN). The proposed approaches were applied to 33 stream-gauging stations. It was found that the optimal 3-hidden layered PCA_NN method was more appropriate than the optimal MLP_NN and GFF_NN models for the estimation of maximum annual flows.