Peak discharge prediction due to embankment dam break by using sensitivity analysis based ANN

Ali Osman Pektas, Tarkan Erdik

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


Accurate prediction of peak discharges due to embankment dam failure is essential to identifying and reducing potential for loss of life and damage in the downstream floodplain. Because, when a dam fails the damage is certain, but the extent of this damage cannot be evaluated in advance. The loss of life and property damage can vary depending on flood area and population. In order to cope with embankment dam breaching and to take necessary steps beforehand many researchers worked on parametric breach models based on Regression Analysis (RA) to estimate the peak outflow from a breached embankment dam since 1970s. RA is a widely-used approach that could provide acceptable results. Since, this approach bears restrictive assumptions, direct application of RA ignoring these assumptions might cause pitfalls and biased calculations. In this study, it is shown that previous works generated by RA gives biased calculations and a new alternative approach, based on Artificial Neural Networks (ANN), is suggested in replacement of classical RA, which gives more accurate results according to both numerical error criteria and scientific background.

Original languageEnglish
Pages (from-to)1868-1876
Number of pages9
JournalKSCE Journal of Civil Engineering
Issue number6
Publication statusPublished - Sept 2014


  • artificial neural networks
  • embankment dam failure
  • multilayer perceptrons
  • peak discharge
  • sensitivity analysis
  • variable importance


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