Fatahi, M., Lashkar-Ara, B. (2017). Estimating scour below inverted siphon structures using stochastic and soft computing approaches. Journal of AI and Data Mining, 5(1), 55-66. doi: 10.22044/jadm.2016.757
M. Fatahi; B. Lashkar-Ara. "Estimating scour below inverted siphon structures using stochastic and soft computing approaches". Journal of AI and Data Mining, 5, 1, 2017, 55-66. doi: 10.22044/jadm.2016.757
Fatahi, M., Lashkar-Ara, B. (2017). 'Estimating scour below inverted siphon structures using stochastic and soft computing approaches', Journal of AI and Data Mining, 5(1), pp. 55-66. doi: 10.22044/jadm.2016.757
Fatahi, M., Lashkar-Ara, B. Estimating scour below inverted siphon structures using stochastic and soft computing approaches. Journal of AI and Data Mining, 2017; 5(1): 55-66. doi: 10.22044/jadm.2016.757
Estimating scour below inverted siphon structures using stochastic and soft computing approaches
Civil Engineering Department, Jundi-Shapur University of Technology, Dezful, Iran.
Abstract
This paper uses nonlinear regression, Artificial Neural Network (ANN) and Genetic Programming (GP) approaches for predicting an important tangible issue i.e. scours dimensions downstream of inverted siphon structures. Dimensional analysis and nonlinear regression-based equations was proposed for estimation of maximum scour depth, location of the scour hole, location and height of the dune downstream of the structures. In addition, The GP-based formulation results are compared with experimental results and other accurate equations. The results analysis showed that the equations derived from Forward Stepwise nonlinear regression method have correlation coefficient of R2=0.962 , 0.971 and 0.991 respectively. This correlates the relative parameter of maximum scour depth (s/z) in comparison with the genetic programming (GP) model and artificial neural network (ANN) model. Furthermore, the slope of the fitted line extracted from computations and observations for dimensionless parameters generally presents a new achievement for sediment engineering and scientific community, indicating the superiority of artificial neural network (ANN) model
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