Neuro-fuzzy GMDH based particle swarm optimization for prediction of scour depth at downstream of grade control structures

In the present study, neuro-fuzzy based-group method of data handling (NF-GMDH) as an adaptive learning network was utilized to predict the maximum scour depth at the downstream of grade-control structures. The NF-GMDH network was developed using particle swarm optimization (PSO). Effective parameters on the scour depth include sediment size, geometry of weir, and flow characteristics in the upstream and downstream of structure. Training and testing of performances were carried out using non-dimensional variables. Datasets were divided into three series of dataset (DS). The testing results of performances were compared with the gene-expression programming (GEP), evolutionary polynomial regression (EPR) model, and conventional techniques. The NF-GMDH-PSO network produced lower error of the scour depth prediction than those obtained using the other models. Also, the effective input parameter on the maximum scour depth was determined through a sensitivity analysis.