Longshore sediment transport estimation using a fuzzy inference system

Bakhtyar, R, Ghaheri, A, Yeganeh-Bakhtiary, A and Baldock, TE (2009) Longshore sediment transport estimation using a fuzzy inference system. Applied Ocean Research, 30 4: 273-286. doi:10.1016/j.apor.2008.12.001

Author Bakhtyar, R
Ghaheri, A
Yeganeh-Bakhtiary, A
Baldock, TE
Title Longshore sediment transport estimation using a fuzzy inference system
Journal name Applied Ocean Research   Check publisher's open access policy
ISSN 0141-1187
Publication date 2009-01-01
Year available 2009
Sub-type Article (original research)
DOI 10.1016/j.apor.2008.12.001
Open Access Status
Volume 30
Issue 4
Start page 273
End page 286
Total pages 14
Editor M Kashiwahi
Place of publication Oxford, England, U.K.
Publisher Elsevier Science Inc.
Language eng
Subject 960902 Coastal and Estuarine Land Management
0905 Civil Engineering
Abstract Accurate prediction of longshore sediment transport in the nearshore zone is essential for control of shoreline erosion and beach evolution. In this paper, a hybrid Adaptive-Network-Based FUZZY Inference System (ANFIS), Fuzzy Inference System (FIS), CERC, Walton-Bruno (WB) and Van Rijn (VR) formulae are used to predict and model longshore sediment transport in the surf zone. The architecture of ANFIS consisted of three inputs (breaking wave height), (breaking angle), (wave period) and one output (longshore sediment transport rate). For statistical comparison of predicted and measured sediment transport. bias, root mean square error and scatter index are used. The longshore sediment transport rate (LSTR) and wave characteristics at a 4 km-long beach on the central west coast of India are used as case Studies. The CERC, WB and VR methods are also applied to the same data. Results indicate that the errors of the ANFIS model in predicting wave parameters are less than those of the empirical formulas. The scatter index of the CERC, WB and VR methods in predicting LSTR is 51.9%, 27.9% and 22.5%, respectively, while the scatter index of the ANFIS model in the prediction of LSTR is 17.32%. A comparison of results reveals that the ANFIS model provides higher accuracy and reliability for LSTR estimation than the other techniques. (C) 2008 Elsevier Ltd. All rights reserved.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: 2010 Higher Education Research Data Collection
School of Civil Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 13 times in Thomson Reuters Web of Science Article | Citations
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Created: Thu, 03 Sep 2009, 17:56:32 EST by Mr Andrew Martlew on behalf of School of Civil Engineering