147713
20181203021843.0
1464-7141
10.2166/hydro.2010.101
doi
000292538300020
ISI
ARTICLE
Combined Particle Swarm Optimization and Fuzzy Inference System model for estimation of current-induced scour beneath marine pipelines
2011
IWA Publishing
2011
Journal Articles
In this paper the capability of PSO is employed to deal with the ANFIS inherent shortcomings to extract optimum fuzzy If-Then rules in noisy area arisen from application of nondimentional variables to estimate scouring depth. In the model, a PSO algorithm is employed to optimize the clustering parameters controls fuzzy If-Then rules in subtractive clustering while another PSO algorithm is employed to tune the fuzzy rules parameters associated with the fuzzy If-Then rules. The PSO models objective function is RMSE by which the model attempts to minimize the error of scouring depth estimation with respect to its generalization capability. To evaluate the model performance, the experimental data sets are used as training, checking and testing data sets. In the dimensional model the mean current velocity, mean grain size, water depth, pipe diameter, shear boundary velocity while in the nondimensional model the pipe, boundary Reynolds numbers, Froude number and normalized depth of water are set as the as input variables. The results show that the model provides an alternative approach to the conventional empirical formulas. It is evident that the PSO-FIS-SO is superior to ANFIS model in the noisy area that the input and output variables slightly related to each other.
ANFIS
clustering parameters
fuzzy If-Then parameters
gradient-based algorithms
noisy area
PSO
scour estimation
Zanganeh, M.
Yeganeh-Bakhtiary, A.
Bakhtyar, R.
185013
242875
558-573
3
Journal of Hydroinformatics
13
ECOL
252101
U11221
oai:infoscience.tind.io:147713
ENAC
article
185013
185013
148230
EPFL-ARTICLE-147713
OTHER
PUBLISHED
REVIEWED
ARTICLE