Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/82808
Title: Scour prediction in long contractions using ANFIS and SVM
Authors: Najafzadeh, Mohammad
Etemad-Shahidi, Amir
Lim, Siow Yong
Keywords: Adaptive Neuro-Fuzzy Inference System
Support vector machines
Long contraction
Rectangular channel
Scour depth
Traditional equations
Issue Date: 2015
Source: Najafzadeh, M., Etemad-Shahidi, A., & Lim, S. Y. (2016). Scour prediction in long contractions using ANFIS and SVM. Ocean Engineering, 111, 128-135.
Series/Report no.: Ocean Engineering
Abstract: Protection of the channel bed in waterways against scour phenomena in long contractions is a very significant issue in channels design. Several field and experimental investigations were carried out to produce a relationship between the scour depth due to the contracted channels width and the governing variables. However, existing empirical equations do not always provide accurate scour prediction due to the complexity of the scour process. This paper investigates local scour depth in long contractions of rectangular channels using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machines (SVM). For modeling of ANFIS and SVM, the input parameters that affect the scour phenomena are average flow velocity, critical threshold velocity of sediment movement, flow depth, median particle diameter, geometric standard deviation, un-contracted and contracted channel widths. Training and testing stages of the models are carried out using experimental data collected from different literature. The performances of the developed models are compared with those calculated using existing scour prediction equations. The results show that the developed ANFIS model can predict scour depth more accurately than SVM and the existing equations. A sensitivity analysis is also performed to determine the most important parameter in predicting the scour depth in long contractions.
URI: https://hdl.handle.net/10356/82808
http://hdl.handle.net/10220/40327
ISSN: 0029-8018
DOI: 10.1016/j.oceaneng.2015.10.053
Schools: School of Civil and Environmental Engineering 
Rights: © 2015 Elsevier Ltd. This is the author created version of a work that has been peer reviewed and accepted for publication by Ocean Engineering, Elsevier Ltd. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1016/j.oceaneng.2015.10.053].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:CEE Journal Articles

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