Estimation of Peak Flood Discharges at Ungauged Sites Across Turkey

SEÇKİN N., Guven A.

WATER RESOURCES MANAGEMENT, vol.26, no.9, pp.2569-2581, 2012 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 26 Issue: 9
  • Publication Date: 2012
  • Doi Number: 10.1007/s11269-012-0033-1
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.2569-2581
  • Keywords: Peak flood discharge, Ungauged site, Linear genetic programming, Gene-expression programming, Logistic regression, FREQUENCY-ANALYSIS, NEURAL-NETWORKS, RIVERS, REGION, BASIN
  • Çukurova University Affiliated: Yes


The reliable forecasting of the peak flood discharge at river basins is a common problem, and it becomes more complicated when there is inadequate recorded data. The statistical methods commonly used for the estimation of peak flood discharges are generally considered to be inadequate because of the complexity of this problem. Recently, genetic programming (GP) which is a branch of soft computing methods has attracted the attention of the hydrologists. In this study, gene-expression programming (GEP) and linear genetic programming (LGP), which are extensions to GP, in addition to logistic regression (LR) were employed in order to forecast peak flood discharges. The study covered 543 ungauged sites across Turkey. Drainage area, elevation, latitude, longitude, and return period were used as the inputs while the peak flood discharge was the output. Model comparison results revealed that GEP predicted the peak flood discharges with R (2) = 57.4 % correlation, LGP with 56 % and LR model with 42.3 %, respectively. The peak flood discharges in all river basins can now be determined using the single equation provided by the GEP model.