Abstract
AbstractIn this paper, the neuro-fuzzy based group method of data handling (NF-GMDH) as an adaptive learning network was used to predict the scour process at pile groups due to waves. The NF-GMDH network was developed using the particle swarm optimization (PSO) algorithm and gravitational search algorithm (GSA). Effective parameters on the scour depth include sediment size, geometric property, pile spacing, arrangement of pile group, and wave characteristics upstream of group piles. Seven dimensionless parameters were obtained to define a functional relationship between input and output variables. Published data were compiled from the literature for the scour depth modeling due to waves. The efficiency of training stages for both NF-GMDH-PSO and NF-GMDH-GSA models were investigated. The results indicated that NF-GMDH models could provide more accurate predictions than those obtained using model tree and traditional equations.
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