Accurate prediction of breached dam's peak outflow is a significance factor for flood risk analysis. In this study, capability of Support Vector Machine and Kernel Extreme Learning Machine as kernel-based approaches and Gene Expression Programming method was assessed in breached dam's peak outflow predicting. 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 also was 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 had 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.
Some novelties of present research can be summarized as follow:
Applicability and accuracy of two different Meta models, namely, Gene Expression Programming (GEP) and Support Vector Machine (SVM) are used to predict the peak outflow from breached embankment dams.
Different two scenarios are developed based on dam reservoir height and volume at the time of failure and soil characteristics.
Additional of geometrical characteristics of dam, the impact of soil characteristics on modelling the peak outflow is investigated.
The outcomes of the SVM and GEP models are also compared with the existing empirical equations and it is shown the intelligence models yield better results.