Accurate prediction of a breached dam’s peak outflow is a significant factor for flood risk analysis.
In this study, the capability of Support Vector Machine and Kernel Extreme Learning Machine as
kernel-based approaches and Gene Expression Programming method was assessed in breached
dam peak outflow prediction. Two types of modeling were considered. First, only dam reservoir
height and volume at the failure time were used as the input combinations (state 1). Then, soil
characteristics were added to input combinations to investigate particularly the impact of soil
characteristics (state 2). Results showed that the use of only soil characteristics did not lead to a
desired accuracy; however, adding soil characteristics to input combinations (state 2) improved the
models’accuracy up to 40%. The outcome of the applied models was also compared with existing
empirical equations and it was found the applied models yielded better results. Sensitivity analysis
results showed that dam height had the most important role in the peak outflow prediction, while the
strength parameters did not have significant impacts. Furthermore, for assessing the best-applied
model dependability, uncertainty analysis was used and the results indicated that the SVM model
had an allowable degree of uncertainty in peak outflow modelling