Bed load prediction in gravel-bed rivers using wavelet kernel extreme learning machine and meta-heuristic methods

The present study aims at developing a wavelet kernel extreme learning machine (WKELM) and meta-heuristic method, known as particle swarm optimization (PSO). PSO algorithm is employed in order to provide a desirable modeling by optimal determination of parameters attributed to WKELM. In order to confirm the ability of employed PSO-WKELM approach in solving the problem, a well-known kernel-based support vector machine (SVM) is applied to compare the obtained results. 890 data points from 19 gravel-bed rivers located in the USA were used to feed the utilized heuristic models. Three different scenarios were proposed; in the scenario 1, different combinations of parameters based on hydraulic characteristics were prepared, scenario 2 was developed using both hydraulic and sediment properties as model inputs of bed load transport, and lastly, the performance of employed PSO-WKELM approach for prediction of bed load transport with different range of median particle size was investigated. The obtained results confirmed the higher predictive potential of PSO-WKELM in comparison with SVM. Also, it was found that prediction of bed load transport with median particles size ranging from 1 to 1.4 mm led to more valid outcome. Performing the sensitivity analysis demonstrated the remarkable impact of the ratio of average velocity ( V ) to shear velocity ( U *) in modeling process.