Scour prediction in long contractions using ANFIS and SVM. (1st January 2016)

• Record Type: Journal Article
• Authors: Najafzadeh, Mohammad; Etemad-Shahidi, Amir; Lim, Siow Yong
• Is Part Of: Ocean engineering. Volume 111(2016)
• Page Start: 128
• Page End: 135
• Publication Date: 2016-01-01
• Subjects: Adaptive Neuro-Fuzzy Inference System -- Support vector machines -- Long contraction -- Rectangular channel -- Scour depth -- Traditional equations; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2015.10.053
• Languages: English
• ISSNs: 0029-8018

Abstract

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. Highlights: The paper studies local scour depth in long contractions of waterway using the ANFIS and SVM. Training and testing stages of the models are carried out using non-dimensional input-output variables. The ANFIS model can predict scour depth more accurate than SVM and the existing equations.