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You are here : eLibrary : IAHR World Congress Proceedings : 36th Congress - The Hague (2015) ALL CONTENT : Sediment management and morphodynamics : Artificial neural network modeling of suspended load inside surf zone using wavelet transform
Artificial neural network modeling of suspended load inside surf zone using wavelet transform
Author : ANZY LEE (1), HYUN-DOUG YOON(2) & KYUNG-DUCK SUH(3)
ABSTRACT
An artificial neural network (ANN) model is developed to predict the sediment suspension load at a specific time using
the components of different scales that have been extracted from the wavelet analysis of wave surface elevation data.
Considering that the period of each wave in a wave group can be detected by the wavelet transform of the surface
elevation data, a time series of each component from the analysis with a scale a is chosen to be an input data of the
ANN model if it passes through the local maximum which can be noted by (a,t) in a time- scale domain. The developed
model is used to predict the time-dependent sediment concentration data collected during the CROSSTEX (CROss-
Shore Sediment Transport EXperiment) at Oregon State University. The peak wave period was 4 s for the erosive case
and 6.8 s for the accretive case.
File Size : 575,132 bytes
File Type : Adobe Acrobat Document
Chapter : IAHR World Congress Proceedings
Category : 36th Congress - The Hague (2015) ALL CONTENT
Article : Sediment management and morphodynamics
Date Published : 28/08/2015
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