000147713 001__ 147713
000147713 005__ 20181203021843.0
000147713 0247_ $$2doi$$a10.2166/hydro.2010.101
000147713 022__ $$a1464-7141
000147713 02470 $$2ISI$$a000292538300020
000147713 037__ $$aARTICLE
000147713 245__ $$aCombined Particle Swarm Optimization and Fuzzy Inference System model for estimation of current-induced scour beneath marine pipelines
000147713 260__ $$bIWA Publishing$$c2011
000147713 269__ $$a2011
000147713 336__ $$aJournal Articles
000147713 520__ $$aIn 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.
000147713 6531_ $$aANFIS
000147713 6531_ $$aclustering parameters
000147713 6531_ $$afuzzy If-Then parameters
000147713 6531_ $$agradient-based algorithms
000147713 6531_ $$anoisy area
000147713 6531_ $$aPSO
000147713 6531_ $$ascour estimation
000147713 700__ $$aZanganeh, M.
000147713 700__ $$aYeganeh-Bakhtiary, A.
000147713 700__ $$0242875$$aBakhtyar, R.$$g185013
000147713 773__ $$j13$$k3$$q558-573$$tJournal of Hydroinformatics
000147713 909C0 $$0252101$$pECOL$$xU11221
000147713 909CO $$ooai:infoscience.tind.io:147713$$particle$$pENAC
000147713 917Z8 $$x185013
000147713 917Z8 $$x185013
000147713 917Z8 $$x148230
000147713 937__ $$aEPFL-ARTICLE-147713
000147713 973__ $$aOTHER$$rREVIEWED$$sPUBLISHED
000147713 980__ $$aARTICLE