Simulation of the depth scouring downstream sluice gate: The validation of newly developed data-intelligent models

Sluice gate is a common tool to regulate water conveyance systems like irrigation channels or pipelines. The interaction between the flow and sediment particles downstream of the sluice gate may initiate scouring phenomenon and extend the resulted scour hole beneath the sluice gate foundation. The consequence of this procedure is undermining the whole structure, interrupting the flow passage, and regulation. Thus, the scour process downstream of a sluice gate is a critical point and robust scour depth prediction is still a crucial issue for hydraulic engineers. This paper proposes several novel hybrid adaptive neuro-fuzzy inference system (ANFIS) methods called ANFIS-PSO (particle swarm optimization), ANFIS-ACO (ant colony optimization), ANFIS-DE (differential evolution) and ANFIS-GA (genetic algorithm) as predictive models to estimate scour depth down-stream of a sluice gate. To this end, some physical and hydraulic parameters such as d(50) (median diameter of bed material), b(gate opening), (tail water depth), l(apron length), U(mean velocity of the jet) and sigma(g) (geometric standard deviation of sediment grain size) are considered as predictive variables in form of non-dimensional parameters. To provide a reliable predictive model, three combinations of input variables are prepared by eliminating some predictive variables. To assess adequacy of proposed models, some error indices are employed in both training and testing phases. Results show the optimistic predictive model is ANFIS-PSO (RMSE = 0.437 and R-2 = 0.946) when all mentioned non-dimensional parameters are employed except h/b. Furthermore, the proposed model has the largest accuracy compared to the previously developed AI and empirical models. Ultimately, it can be concluded that the hybrid ANFIS-PSO is a robust approach for scour depth prediction downstream of a sluice gate.