Evaluation of GA-SVR method for modeling bed load transport in gravel-bed rivers

- Bed load transport rates of three gravel-bed rivers were predicted using GA-SVR.
- Different combinations of hydraulic parameters were used as GA-SVR inputs.
- ERBF kernel showed better performance than other kernels in bed load prediction.
- The GA-SVR models were superior to traditional bed load formulas.
- The elimination of high bed load transport rates improved prediction accuracy.

SummaryThe aim of the present study is to apply Support Vector Regression (SVR) method to predict bed load transport rates for three gravel-bed rivers. Different combinations of hydraulic parameters are used as inputs for modeling bed load transport using four kernel functions of SVR models. Genetic Algorithm (GA) method is applicably administered to determine optimal SVR parameters. The GA-SVR models are developed and tested using the available data sets, and consecutive predicted results are compared in terms of Efficiency Coefficient and Correlation Coefficient. Obtained results show that the GA-SVR models with Exponential Radial Basis Function (ERBF) kernel present higher accuracy than the other applied GA-SVR models. Furthermore, testing data sets are predicted by Einstein and Meyer-Peter and Müller (MPM) formulas. The GA-SVR models demonstrate a better performance compared to the traditional bed load formulas. Finally, high bed load transport values were eliminated from data sets and the models are re-analyzed. The elimination of high bed load transport rates improves prediction accuracy using GA-SVR method.